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merge into: rubydragon:test
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rubydragon:main rubydragon:remove_fusion rubydragon:refactor rubydragon:test_feynman_diagram_generator rubydragon:congruent_in_ph rubydragon:enable_gpu_vendors rubydragon:seeded_randomness rubydragon:workaround_trie rubydragon:experiments rubydragon:heterogeneity rubydragon:tape_machine rubydragon:likwid rubydragon:qed rubydragon:workflow rubydragon:safetestsets rubydragon:optimizer rubydragon:estimator rubydragon:fix-compute-typeinfo rubydragon:feature/property-tracking rubydragon:scheduling rubydragon:code-gen rubydragon:doc rubydragon:test rubydragon:performance rubydragon:refactoring
...
pull from: rubydragon:likwid
Branches Tags
rubydragon:remove_fusion rubydragon:refactor rubydragon:test_feynman_diagram_generator rubydragon:congruent_in_ph rubydragon:main rubydragon:enable_gpu_vendors rubydragon:seeded_randomness rubydragon:workaround_trie rubydragon:experiments rubydragon:heterogeneity rubydragon:tape_machine rubydragon:likwid rubydragon:qed rubydragon:workflow rubydragon:safetestsets rubydragon:optimizer rubydragon:estimator rubydragon:fix-compute-typeinfo rubydragon:feature/property-tracking rubydragon:scheduling rubydragon:code-gen rubydragon:doc rubydragon:test rubydragon:performance rubydragon:refactoring

24 Commits
test ... likwid

Author SHA1 Message Date
Anton Reinhard
9cd2bcdba2 WIP 2024-01-03 16:57:49 +01:00
Anton Reinhard
82ed774b7e Tape Machine (#30) 
Adds a tape machine way of executing the code.
The tape machine is a series of FunctionCall objects, which can either be called one by one, or be used to generate expressions to make up a function.

Reviewed-on: Rubydragon/MetagraphOptimization.jl#30
Co-authored-by: Anton Reinhard <anton.reinhard@proton.me>
Co-committed-by: Anton Reinhard <anton.reinhard@proton.me>
 2024-01-03 16:38:32 +01:00
Anton Reinhard
92e0eeaaef heterogeneity (#27) 
Prepare things to work with heterogeneity, make things work on GPU

Reviewed-on: Rubydragon/MetagraphOptimization.jl#27
Co-authored-by: Anton Reinhard <anton.reinhard@proton.me>
Co-committed-by: Anton Reinhard <anton.reinhard@proton.me>
 2023-12-18 14:31:52 +01:00
Anton Reinhard
c90346e948 Add QED Model (#25) 
Reviewed-on: Rubydragon/MetagraphOptimization.jl#25
Co-authored-by: Anton Reinhard <anton.reinhard@proton.me>
Co-committed-by: Anton Reinhard <anton.reinhard@proton.me>
 2023-12-07 02:54:15 +01:00
Anton Reinhard
938bf216e5 Improve actions workflow by removing prepare step (#23) 
Reviewed-on: Rubydragon/MetagraphOptimization.jl#23
Co-authored-by: Anton Reinhard <anton.reinhard@proton.me>
Co-committed-by: Anton Reinhard <anton.reinhard@proton.me>
 2023-11-24 19:20:05 +01:00
Anton Reinhard
04d5673b44 Use SafeTestsets for testing (#22) 
Fixes issue #18

Reviewed-on: Rubydragon/MetagraphOptimization.jl#22
Co-authored-by: Anton Reinhard <anton.reinhard@proton.me>
Co-committed-by: Anton Reinhard <anton.reinhard@proton.me>
 2023-11-22 16:01:17 +01:00
Anton Reinhard
b7560685d4 Optimizer interface and sample implementation (#19) 
Reviewed-on: Rubydragon/MetagraphOptimization.jl#19
Co-authored-by: Anton Reinhard <anton.reinhard@proton.me>
Co-committed-by: Anton Reinhard <anton.reinhard@proton.me>
 2023-11-22 13:51:54 +01:00
Anton Reinhard
16274919e4 Cost Estimation interface (#14) 
See issue #13

Reviewed-on: Rubydragon/MetagraphOptimization.jl#14
Co-authored-by: Anton Reinhard <anton.reinhard@proton.me>
Co-committed-by: Anton Reinhard <anton.reinhard@proton.me>
 2023-11-17 01:31:31 +01:00
Anton Reinhard
2709eeb3dc Fix the types, add some profiling examples (#15) 
Reviewed-on: Rubydragon/MetagraphOptimization.jl#15
Co-authored-by: Anton Reinhard <anton.reinhard@proton.me>
Co-committed-by: Anton Reinhard <anton.reinhard@proton.me>
 2023-11-13 12:55:02 +01:00
Anton Reinhard
5a30f57e1f Add scheduling, machine info, caching strategies and devices (#9)
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Reviewed-on: Rubydragon/MetagraphOptimization.jl#9
Co-authored-by: Anton Reinhard <anton.reinhard@proton.me>
Co-committed-by: Anton Reinhard <anton.reinhard@proton.me>
 2023-10-12 17:51:03 +02:00
Anton Reinhard
bd6c54c1ae Merge pull request 'Code Generation' (#8) from code-gen into main 
Reviewed-on: Rubydragon/MetagraphOptimization.jl#8
 2023-09-17 14:35:46 +02:00
Anton Reinhard
62791ab422 Fix docs
Some checks failed
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2023-09-17 12:40:11 +02:00
Anton Reinhard
4c452dce98 Add execution test 2023-09-17 10:32:43 +02:00
Anton Reinhard
27c4b8ba34 Use real ABC-Model compute effort and data transfer numbers 2023-09-07 18:46:41 +02:00
Anton Reinhard
e59d24ebe5 Add code gen documentation 2023-09-07 18:23:36 +02:00
Anton Reinhard
d1666de432 Add accurate arithmetic for summation, fix order of input particles 2023-09-07 16:49:44 +02:00
Anton Reinhard
0f78053ccf Fix topoligical ordering on the graph 2023-09-05 12:14:41 +02:00
Anton Reinhard
7a1a97dac8 Add basic execution function 2023-09-01 16:22:16 +02:00
Anton Reinhard
f1edce258a Start adding code generation 2023-08-31 18:24:48 +02:00
Anton Reinhard
32fcd069d7 Merge pull request 'Property Caching' (#7) from feature/property-tracking into main 
Reviewed-on: Rubydragon/MetagraphOptimization.jl#7
 2023-08-29 15:35:51 +02:00
Anton Reinhard
e09ab7c77b Add tests 2023-08-29 13:09:33 +02:00
Anton Reinhard
7387fa86b1 Add GraphProperties and property caching 2023-08-29 13:08:02 +02:00
Anton Reinhard
065236be22 Add documentation to every function and automatic doc html building (#6) 
Reviewed-on: Rubydragon/MetagraphOptimization.jl#6
Co-authored-by: Anton Reinhard <anton.reinhard@proton.me>
Co-committed-by: Anton Reinhard <anton.reinhard@proton.me>
 2023-08-29 12:57:46 +02:00
Anton Reinhard
8014bbffcd Merge pull request 'More Validation' (#5) from test into main 
Reviewed-on: Rubydragon/MetagraphOptimization.jl#5
 2023-08-25 11:05:17 +02:00
152 changed files with 11083 additions and 1125 deletions
Expand all files Collapse all files

2
.JuliaFormatter.toml
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@ -1,5 +1,5 @@
indent = 4
margin = 80
margin = 120
always_for_in = true
for_in_replacement = "in"
whitespace_typedefs = true

4
.gitattributes vendored
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@ -1,2 +1,2 @@
examples/AB->ABBBBBBB.txt filter=lfs diff=lfs merge=lfs -text
examples/AB->ABBBBBBBBB.txt filter=lfs diff=lfs merge=lfs -text
input/AB->ABBBBBBBBB.txt filter=lfs diff=lfs merge=lfs -text
input/AB->ABBBBBBB.txt filter=lfs diff=lfs merge=lfs -text

62
.gitea/workflows/julia-package-ci.yml
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@ -1,46 +1,80 @@
name: Test
name: MetagraphOptimization_CI
on: [push]
env:
# keep the depot directly in the repository for the cache
JULIA_DEPOT_PATH: './.julia'
jobs:
test:
runs-on: arch-latest
runs-on: ubuntu-22.04
steps:
#- name: Get git-lfs
# run: apt-get update && apt-get install git-lfs
- name: Checkout repository
uses: actions/checkout@v3
with:
fetch-depth: 0
#- name: Checkout LFS objects
# run: git lfs checkout
- name: Setup Julia environment
uses: https://github.com/julia-actions/setup-julia@v1.9.2
with:
version: '1.9.2'
- name: Install dependencies
run: julia --project=./ -e 'import Pkg; Pkg.instantiate()'
- name: Instantiate
run: |
julia --project=./ -e 'using Pkg; Pkg.instantiate()'
julia --project=./ -e 'using Pkg; Pkg.add(url="https://github.com/QEDjl-project/QEDprocesses.jl/")'
- name: Format check
run: |
julia --project=./ -e 'using JuliaFormatter; format(".", verbose=true)'
julia --project=./ -e 'using JuliaFormatter; format(".", verbose=true, ignore=[".julia/*"])'
julia --project=./ -e '
out = Cmd(`git diff --name-only`) |> read |> String
if out == ""
exit(0)
else
@error "Some files have not been formatted !!!"
@error "Some files have not been formatted!!!"
write(stdout, out)
exit(1)
end'
- name: Run tests
run: julia --project=./ -t 4 -e 'import Pkg; Pkg.test()' -O0
run: julia --project=./ -t 4 -e 'using Pkg; Pkg.test()' -O0
- name: Run examples
run: julia --project=examples/ -t 4 -e 'import Pkg; Pkg.develop(Pkg.PackageSpec(path=pwd())); Pkg.instantiate(); include("examples/import_bench.jl")' -O3
run: |
julia --project=examples/ -e 'using Pkg; Pkg.develop(Pkg.PackageSpec(path=pwd())); Pkg.instantiate(); Pkg.precompile()'
julia --project=examples/ -t 4 -e 'include("examples/import_bench.jl")' -O3
docs:
runs-on: ubuntu-22.04
steps:
- name: Checkout repository
uses: actions/checkout@v3
with:
fetch-depth: 0
- name: Setup Julia environment
uses: https://github.com/julia-actions/setup-julia@v1.9.2
with:
version: '1.9.2'
- name: Build docs
run: |
julia --project=docs/ -e 'using Pkg; Pkg.develop(Pkg.PackageSpec(path=pwd())); Pkg.instantiate(); Pkg.precompile()'
julia --project=docs/ docs/make.jl
- name: Upload artifacts
uses: actions/upload-artifact@v3
with:
name: web-doc
path: docs/build/
#- name: Webhook Trigger
# uses: https://github.com/zzzze/webhook-trigger@master
# continue-on-error: true
# with:
# data: "{\"event\":\"action_completed\", \"download_url\":\"deckardcain.local:8099/something\"}"
# webhook_url: ${{ secrets.WEBHOOK_URL }}

4
.gitignore vendored
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@ -5,6 +5,7 @@
# Files generated by invoking Julia with --track-allocation
*.mem
*.pb.gz
# System-specific files and directories generated by the BinaryProvider and BinDeps packages
# They contain absolute paths specific to the host computer, and so should not be committed
@ -26,3 +27,6 @@ Manifest.toml
# vscode workspace directory
.vscode
.julia
**/.ipynb_checkpoints/
*.bkp

10
Project.toml
View File

@ -4,10 +4,18 @@ authors = ["Anton Reinhard <anton.reinhard@proton.me>"]
version = "0.1.0"
[deps]
AccurateArithmetic = "22286c92-06ac-501d-9306-4abd417d9753"
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
NumaAllocators = "21436f30-1b4a-4f08-87af-e26101bb5379"
QEDbase = "10e22c08-3ccb-4172-bfcf-7d7aa3d04d93"
QEDprocesses = "46de9c38-1bb3-4547-a1ec-da24d767fdad"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Roots = "f2b01f46-fcfa-551c-844a-d8ac1e96c665"
StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
[extras]

46
README.md
View File

@ -42,7 +42,7 @@ Problems:
- Lots of testing required because mistakes will propagate and multiply.
## Other TODOs
- Reduce memory footprint of the graph, are the UUIDs too large?
- Reduce memory footprint of the graph
- Memory layout of Nodes? They should lie linearly in memory, right now probably on heap?
- Add scaling functions
@ -50,18 +50,18 @@ Problems:
For graphs AB->AB^n:
- Number of Sums should always be 1
- Number of ComputeTaskS2 should always be (n+1)!
- Number of ComputeTaskU should always be (n+3)
- Number of ComputeTaskABC_S2 should always be (n+1)!
- Number of ComputeTaskABC_U should always be (n+3)
Times are from my home machine: AMD Ryzen 7900X3D, 64GB DDR5 RAM @ 6000MHz
Times are from my home machine: AMD Ryzen 7900X3D, 64GB DDR5 RAM @ 6000MHz (not necessarily up to date, check Jupyter Notebooks in `notebooks/` instead)
```
$ julia --project examples/import_bench.jl
AB->AB:
Graph:
Nodes: Total: 34, DataTask: 19, ComputeTaskP: 4,
ComputeTaskS2: 2, ComputeTaskV: 4, ComputeTaskU: 4,
ComputeTaskSum: 1
Nodes: Total: 34, DataTask: 19, ComputeTaskABC_P: 4,
ComputeTaskABC_S2: 2, ComputeTaskABC_V: 4, ComputeTaskABC_U: 4,
ComputeTaskABC_Sum: 1
Edges: 37
Total Compute Effort: 185
Total Data Transfer: 102
@ -71,9 +71,9 @@ Graph:
AB->ABBB:
Graph:
Nodes: Total: 280, DataTask: 143, ComputeTaskP: 6,
ComputeTaskS2: 24, ComputeTaskV: 64, ComputeTaskU: 6,
ComputeTaskSum: 1, ComputeTaskS1: 36
Nodes: Total: 280, DataTask: 143, ComputeTaskABC_P: 6,
ComputeTaskABC_S2: 24, ComputeTaskABC_V: 64, ComputeTaskABC_U: 6,
ComputeTaskABC_Sum: 1, ComputeTaskABC_S1: 36
Edges: 385
Total Compute Effort: 2007
Total Data Transfer: 828
@ -83,9 +83,9 @@ Graph:
AB->ABBBBB:
Graph:
Nodes: Total: 7854, DataTask: 3931, ComputeTaskP: 8,
ComputeTaskS2: 720, ComputeTaskV: 1956, ComputeTaskU: 8,
ComputeTaskSum: 1, ComputeTaskS1: 1230
Nodes: Total: 7854, DataTask: 3931, ComputeTaskABC_P: 8,
ComputeTaskABC_S2: 720, ComputeTaskABC_V: 1956, ComputeTaskABC_U: 8,
ComputeTaskABC_Sum: 1, ComputeTaskABC_S1: 1230
Edges: 11241
Total Compute Effort: 58789
Total Data Transfer: 23244
@ -95,9 +95,9 @@ Graph:
AB->ABBBBBBB:
Graph:
Nodes: Total: 438436, DataTask: 219223, ComputeTaskP: 10,
ComputeTaskS2: 40320, ComputeTaskV: 109600, ComputeTaskU: 10,
ComputeTaskSum: 1, ComputeTaskS1: 69272
Nodes: Total: 438436, DataTask: 219223, ComputeTaskABC_P: 10,
ComputeTaskABC_S2: 40320, ComputeTaskABC_V: 109600, ComputeTaskABC_U: 10,
ComputeTaskABC_Sum: 1, ComputeTaskABC_S1: 69272
Edges: 628665
Total Compute Effort: 3288131
Total Data Transfer: 1297700
@ -107,7 +107,7 @@ Graph:
AB->ABBBBBBBBB:
Graph:
Nodes: Total: 39456442, DataTask: 19728227, ComputeTaskS1: 6235290, ComputeTaskP: 12, ComputeTaskU: 12, ComputeTaskV: 9864100, ComputeTaskS2: 3628800, ComputeTaskSum: 1
Nodes: Total: 39456442, DataTask: 19728227, ComputeTaskABC_S1: 6235290, ComputeTaskABC_P: 12, ComputeTaskABC_U: 12, ComputeTaskABC_V: 9864100, ComputeTaskABC_S2: 3628800, ComputeTaskABC_Sum: 1
Edges: 56578129
Total Compute Effort: 295923153
Total Data Transfer: 175407750
@ -116,9 +116,9 @@ Graph:
ABAB->ABAB:
Graph:
Nodes: Total: 3218, DataTask: 1613, ComputeTaskP: 8,
ComputeTaskS2: 288, ComputeTaskV: 796, ComputeTaskU: 8,
ComputeTaskSum: 1, ComputeTaskS1: 504
Nodes: Total: 3218, DataTask: 1613, ComputeTaskABC_P: 8,
ComputeTaskABC_S2: 288, ComputeTaskABC_V: 796, ComputeTaskABC_U: 8,
ComputeTaskABC_Sum: 1, ComputeTaskABC_S1: 504
Edges: 4581
Total Compute Effort: 24009
Total Data Transfer: 9494
@ -128,9 +128,9 @@ Graph:
ABAB->ABC:
Graph:
Nodes: Total: 817, DataTask: 412, ComputeTaskP: 7,
ComputeTaskS2: 72, ComputeTaskV: 198, ComputeTaskU: 7,
ComputeTaskSum: 1, ComputeTaskS1: 120
Nodes: Total: 817, DataTask: 412, ComputeTaskABC_P: 7,
ComputeTaskABC_S2: 72, ComputeTaskABC_V: 198, ComputeTaskABC_U: 7,
ComputeTaskABC_Sum: 1, ComputeTaskABC_S1: 120
Edges: 1151
Total Compute Effort: 6028
Total Data Transfer: 2411

16
data/qed_ke-kke_reduction_optimizer.csv Normal file
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@ -0,0 +1,16 @@
operations,graph_nodes,graph_edges,graph_ce,graph_dt,graph_ci,gen_func_t,cpu_compile_t,cpu_st_t,cpu_mt_t,gpu_compile_t,gpu_t
0,77,101,252.0,6240.0,0.04038461538461539,0.02087051,8.691e-6,3.405098066,0.244763721,1.565749515,0.936213163
1,76,99,246.0,6240.0,0.03942307692307692,0.020658734,9.36e-6,3.244313848,0.230460257,1.548012602,0.887605389
2,75,97,240.0,6240.0,0.038461538461538464,0.045333482,8.74e-6,3.163679857,0.217614064,1.52780456,0.816496837
3,74,95,234.0,6240.0,0.0375,0.020314034,9.081e-6,2.956421016,0.183415997,1.524262179,0.793770075
4,73,93,228.0,6240.0,0.03653846153846154,0.033579409,8.52e-6,2.845414866,0.19168374,1.50907807,0.742734411
5,72,92,228.0,6144.0,0.037109375,0.019736718,8.87e-6,2.827109937,0.207452606,1.497203204,0.719774022
6,71,90,222.0,6144.0,0.0361328125,0.043612693,1.01e-5,2.62776692,0.166492497,1.602060948,0.668929854
7,70,89,222.0,6048.0,0.03670634920634921,0.042731148,1.053e-5,2.631288029,0.185812224,1.514154792,0.694503947
8,69,87,216.0,6048.0,0.03571428571428571,0.042148711,8.19e-6,2.493343257,0.183595081,1.506478504,0.652420896
9,68,86,216.0,5952.0,0.036290322580645164,0.041568955,8.571e-6,2.487317627,0.147773078,1.472141844,0.653143947
10,67,85,216.0,5856.0,0.036885245901639344,0.041307868,9.13e-6,2.491634709,0.175728138,1.482162906,0.63058774
11,66,84,216.0,5760.0,0.0375,0.041265756,8.43e-6,2.516916643,0.180420842,1.463053866,0.650627815
12,65,83,205.0,5760.0,0.035590277777777776,0.039711293,9.22e-6,2.479664249,0.178013433,1.459566956,0.652477867
13,64,82,205.0,5664.0,0.03619350282485876,0.030866093,8.87e-6,2.485424881,0.179983608,1.564961227,0.647932468
14,63,81,205.0,5568.0,0.03681752873563218,0.029946916,8.93e-6,2.469922022,0.179443854,1.485935831,0.651804318
1 operations graph_nodes graph_edges graph_ce graph_dt graph_ci gen_func_t cpu_compile_t cpu_st_t cpu_mt_t gpu_compile_t gpu_t
2 0 77 101 252.0 6240.0 0.04038461538461539 0.02087051 8.691e-6 3.405098066 0.244763721 1.565749515 0.936213163
3 1 76 99 246.0 6240.0 0.03942307692307692 0.020658734 9.36e-6 3.244313848 0.230460257 1.548012602 0.887605389
4 2 75 97 240.0 6240.0 0.038461538461538464 0.045333482 8.74e-6 3.163679857 0.217614064 1.52780456 0.816496837
5 3 74 95 234.0 6240.0 0.0375 0.020314034 9.081e-6 2.956421016 0.183415997 1.524262179 0.793770075
6 4 73 93 228.0 6240.0 0.03653846153846154 0.033579409 8.52e-6 2.845414866 0.19168374 1.50907807 0.742734411
7 5 72 92 228.0 6144.0 0.037109375 0.019736718 8.87e-6 2.827109937 0.207452606 1.497203204 0.719774022
8 6 71 90 222.0 6144.0 0.0361328125 0.043612693 1.01e-5 2.62776692 0.166492497 1.602060948 0.668929854
9 7 70 89 222.0 6048.0 0.03670634920634921 0.042731148 1.053e-5 2.631288029 0.185812224 1.514154792 0.694503947
10 8 69 87 216.0 6048.0 0.03571428571428571 0.042148711 8.19e-6 2.493343257 0.183595081 1.506478504 0.652420896
11 9 68 86 216.0 5952.0 0.036290322580645164 0.041568955 8.571e-6 2.487317627 0.147773078 1.472141844 0.653143947
12 10 67 85 216.0 5856.0 0.036885245901639344 0.041307868 9.13e-6 2.491634709 0.175728138 1.482162906 0.63058774
13 11 66 84 216.0 5760.0 0.0375 0.041265756 8.43e-6 2.516916643 0.180420842 1.463053866 0.650627815
14 12 65 83 205.0 5760.0 0.035590277777777776 0.039711293 9.22e-6 2.479664249 0.178013433 1.459566956 0.652477867
15 13 64 82 205.0 5664.0 0.03619350282485876 0.030866093 8.87e-6 2.485424881 0.179983608 1.564961227 0.647932468
16 14 63 81 205.0 5568.0 0.03681752873563218 0.029946916 8.93e-6 2.469922022 0.179443854 1.485935831 0.651804318

176
data/qed_ke-kkke_greedy_optimizer_GlobalMetricEstimator().csv Normal file
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@ -0,0 +1,176 @@
operations,graph_nodes,graph_edges,graph_ce,graph_dt,graph_ci,gen_func_t,cpu_compile_t,cpu_st_t,cpu_mt_t,gpu_compile_t,gpu_t
0,356,493,1399.0,30528.0,0.0458267819706499,0.077070556,2.6761e-5,17.804336617,0.960385595,10.618577031,4.95440474
1,354,491,1399.0,30432.0,0.04597134595162986,1.030851104,2.37e-5,17.726472964,0.933074463,2.174912444,4.959474851
2,352,489,1399.0,30336.0,0.04611682489451477,0.376282553,2.3861e-5,17.935912907,0.968087391,2.238665483,4.912705328
3,350,487,1399.0,30240.0,0.04626322751322751,0.076651194,4.2451e-5,17.976779783,0.977130996,2.246167674,4.954520005
4,348,485,1399.0,30144.0,0.04641056263269639,0.223709216,2.8031e-5,17.67129111,0.97799748,2.175788856,4.923999491
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78,200,301,1399.0,23040.0,0.06072048611111111,0.155978707,2.4051e-5,17.624250437,0.794935481,2.247188963,4.940403894
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112,132,201,1399.0,19776.0,0.0707423139158576,0.181545084,2.485e-5,17.63441504,0.751343023,2.183033772,4.975534251
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119,118,177,1399.0,19104.0,0.07323073701842546,0.189204719,2.4961e-5,17.791522288,0.766082656,2.242948358,4.980365418
120,116,173,1399.0,19008.0,0.07360058922558922,0.186391669,2.4181e-5,17.645956891,0.750893368,2.197914806,4.98745469
121,114,171,1399.0,18912.0,0.07397419627749577,0.19060573,2.4701e-5,17.771140583,0.765197694,2.20643796,4.959618561
122,112,169,1399.0,18816.0,0.0743516156462585,0.188466188,2.381e-5,17.795228145,0.759434429,2.26208531,4.965068853
123,110,165,1399.0,18720.0,0.07473290598290598,0.191524927,2.3841e-5,17.779734215,0.767242896,2.242967333,4.950554681
124,108,161,1399.0,18624.0,0.07511812714776632,0.189450326,2.3601e-5,17.807849571,0.762371273,2.196711688,4.966122065
125,106,157,1399.0,18528.0,0.0755073402417962,0.191473057,2.357e-5,17.632877767,0.755845465,2.188474891,4.977562868
126,104,153,1399.0,18432.0,0.0759006076388889,0.191382079,2.3851e-5,17.775729988,0.758861116,2.278116886,4.979965119
127,102,151,1399.0,18336.0,0.07629799301919721,0.192296369,2.394e-5,17.777918793,0.764981303,2.224818047,4.949944943
128,100,149,1399.0,18240.0,0.07669956140350877,0.191424719,2.4331e-5,17.856475915,0.76057459,2.201588049,4.941974925
129,98,146,1399.0,18144.0,0.07710537918871252,0.194280932,2.3951e-5,17.779963845,0.766401736,2.223182601,4.961465017
130,96,142,1399.0,18048.0,0.07751551418439716,0.192850597,2.3861e-5,17.765033828,0.760509569,2.250897799,4.967399083
131,94,138,1399.0,17952.0,0.07793003565062388,0.194741823,2.38e-5,17.778261696,0.764271609,2.248898068,4.975998565
132,92,136,1399.0,17856.0,0.07834901433691756,0.193567295,2.5281e-5,17.791322862,0.759809249,2.216694812,4.962092553
133,90,132,1399.0,17760.0,0.07877252252252252,0.196949912,2.4641e-5,17.775924767,0.766636532,2.192664527,4.943809886
134,88,129,1399.0,17664.0,0.07920063405797101,0.19423328,2.4491e-5,17.775940481,0.759698903,2.241454301,4.965419114
135,86,125,1399.0,17568.0,0.07963342440801457,0.196021362,2.4541e-5,17.749824568,0.77002309,2.244133161,4.973507276
136,84,123,1399.0,17472.0,0.08007097069597069,0.195945063,2.4791e-5,17.793381264,0.758984676,2.223761942,4.967845004
137,82,120,1399.0,17376.0,0.0805133517495396,0.196404909,2.5491e-5,17.781126567,0.76777764,2.208548873,4.942758101
138,80,116,1399.0,17280.0,0.08096064814814814,0.197313346,2.469e-5,17.785944557,0.814271788,2.200296465,4.939179018
139,78,114,1399.0,17184.0,0.08141294227188083,0.155633427,2.5181e-5,17.79491891,0.767423131,2.233213884,4.963944358
140,76,111,1399.0,17088.0,0.08187031835205992,0.194686919,2.4311e-5,17.835512877,0.761171578,2.216772786,4.968370761
141,74,108,1399.0,16992.0,0.0823328625235405,0.19895497,2.4301e-5,17.80769545,0.768202031,2.212642548,4.971369432
142,72,106,1399.0,16896.0,0.08280066287878787,0.197589165,2.4241e-5,17.817799582,0.760097766,2.219367009,4.967751237
143,70,102,1399.0,16800.0,0.08327380952380953,0.200103786,2.425e-5,17.804210307,0.767108387,2.264925155,4.965506236
144,68,99,1399.0,16704.0,0.08375239463601533,0.196633322,2.5371e-5,17.822197608,0.762852947,2.20877412,4.971541033
145,66,97,1399.0,16608.0,0.08423651252408478,0.200144552,2.4801e-5,17.823667792,0.766965999,2.209992675,4.969252216
146,64,93,1399.0,16512.0,0.08472625968992248,0.199816644,2.4901e-5,17.838429006,0.764432365,2.241092809,4.961995819
147,62,89,1399.0,16416.0,0.08522173489278752,0.187325579,2.5321e-5,17.811923957,0.767393244,2.227406228,4.960056608
148,60,85,1399.0,16320.0,0.08572303921568628,0.198893612,2.4451e-5,17.82940565,0.760747136,2.209815727,4.971563658
149,58,83,1399.0,16224.0,0.08623027613412229,0.201039293,2.4651e-5,17.817639935,0.767607352,2.210546374,4.97066195
150,56,81,1399.0,16128.0,0.08674355158730158,0.199841932,2.414e-5,17.82203287,0.760048809,2.243550629,4.954439346
151,54,79,1399.0,16032.0,0.0872629740518962,0.2011596,2.4741e-5,17.804574042,0.767800679,2.250206119,4.955980994
152,52,75,1399.0,15936.0,0.08778865461847389,0.19971389,2.4331e-5,17.829821975,0.762018993,2.205143141,4.970086548
153,50,73,1399.0,15840.0,0.08832070707070708,0.201368798,2.4881e-5,17.836101646,0.767371477,2.218711432,4.96364023
154,48,71,1399.0,15744.0,0.08885924796747967,0.200798594,2.4491e-5,17.830384655,0.765407907,2.286796949,4.939295093
155,46,67,1399.0,15648.0,0.08940439672801637,0.202551163,2.5121e-5,17.827221721,0.768466657,2.262575248,4.943430916
156,44,65,1399.0,15552.0,0.08995627572016461,0.198816901,2.578e-5,17.840506569,0.760760306,2.220630133,4.952844324
157,42,63,1399.0,15456.0,0.09051501035196688,0.201424744,2.5021e-5,17.814439397,0.767553139,2.196934945,4.958506547
158,40,59,1399.0,15360.0,0.09108072916666667,0.202145126,2.565e-5,17.808712307,0.76137146,2.235801178,4.949559042
159,38,55,1399.0,15264.0,0.0916535639412998,0.201663393,2.4591e-5,17.784477195,0.766209648,2.249329555,4.964028527
160,36,53,1399.0,15168.0,0.09223364978902954,0.199579456,2.5461e-5,17.900752023,0.761934363,2.209582978,4.950507063
161,34,48,1399.0,15072.0,0.09282112526539278,0.159541692,2.5211e-5,17.769415534,0.935609132,2.216664395,4.962977201
162,32,44,1399.0,14976.0,0.09341613247863248,0.201979445,2.5581e-5,17.802148727,0.758630938,2.257162782,4.954367291
163,30,40,1399.0,14880.0,0.09401881720430108,0.203381244,2.5411e-5,17.808584074,0.768160516,2.239967841,4.949515694
164,28,35,1399.0,14784.0,0.09462932900432901,0.200707381,2.5071e-5,17.811958674,0.765546396,2.222827481,4.962523474
165,26,31,1399.0,14688.0,0.09524782135076253,0.203476579,2.4431e-5,17.791537057,0.759747517,2.210172596,4.96717851
166,24,29,1399.0,14592.0,0.09587445175438597,0.38619058,2.5161e-5,17.784565893,0.765981903,2.205094732,4.970469758
167,22,25,1399.0,14496.0,0.09650938189845475,0.209174268,2.6071e-5,17.886396985,0.762283972,2.251379768,4.9348063
168,20,21,1399.0,14400.0,0.09715277777777778,0.184182012,2.5331e-5,17.791795342,0.760972528,2.229551257,4.941190792
169,18,17,1399.0,14304.0,0.09780480984340045,0.203935864,2.572e-5,17.823665061,0.762353868,2.199132836,4.965200905
170,16,15,1399.0,14208.0,0.09846565315315316,0.200164969,2.4631e-5,17.792385586,0.76804392,2.174965407,4.972074439
171,14,13,1399.0,14112.0,0.09913548752834467,0.204567903,2.5071e-5,17.806154396,0.759505453,2.2340466,4.972671228
172,12,11,1399.0,14016.0,0.09981449771689498,0.201861418,2.5971e-5,18.529840195,0.789347616,2.23167521,4.947890089
173,10,9,1399.0,13920.0,0.1005028735632184,0.202902727,2.4951e-5,17.865867105,0.761004999,2.194876208,4.93177029
174,8,7,1399.0,13824.0,0.10120081018518519,0.198079003,2.4651e-5,17.791197743,0.767399089,2.226370372,4.951979965
1 operations graph_nodes graph_edges graph_ce graph_dt graph_ci gen_func_t cpu_compile_t cpu_st_t cpu_mt_t gpu_compile_t gpu_t
2 0 356 493 1399.0 30528.0 0.0458267819706499 0.077070556 2.6761e-5 17.804336617 0.960385595 10.618577031 4.95440474
3 1 354 491 1399.0 30432.0 0.04597134595162986 1.030851104 2.37e-5 17.726472964 0.933074463 2.174912444 4.959474851
4 2 352 489 1399.0 30336.0 0.04611682489451477 0.376282553 2.3861e-5 17.935912907 0.968087391 2.238665483 4.912705328
5 3 350 487 1399.0 30240.0 0.04626322751322751 0.076651194 4.2451e-5 17.976779783 0.977130996 2.246167674 4.954520005
6 4 348 485 1399.0 30144.0 0.04641056263269639 0.223709216 2.8031e-5 17.67129111 0.97799748 2.175788856 4.923999491
7 5 346 483 1399.0 30048.0 0.04655883919062833 0.076034997 4.3191e-5 17.766336956 0.967055891 2.187609178 4.922574669
8 6 344 481 1399.0 29952.0 0.04670806623931624 0.398917781 4.3422e-5 17.709032771 0.971142926 2.170963978 4.917191185
9 7 342 479 1399.0 29856.0 0.04685825294748124 0.352569343 4.3801e-5 17.690255833 0.952966242 2.159295978 4.945842152
10 8 340 477 1399.0 29760.0 0.04700940860215054 0.117620751 4.2992e-5 17.905787431 0.749896479 2.19940915 4.922882222
11 9 338 475 1399.0 29664.0 0.04716154261057174 0.318053898 2.3481e-5 17.522775542 0.745113955 2.202366151 4.928734427
12 10 336 473 1399.0 29568.0 0.047314664502164504 0.184069985 2.3381e-5 17.529935879 0.74637911 2.238397648 4.919919125
13 11 334 471 1399.0 29472.0 0.047468783930510315 0.086029218 2.365e-5 17.560859257 0.75559668 2.249242933 4.956561058
14 12 332 469 1399.0 29376.0 0.04762391067538126 0.077326472 2.4361e-5 17.559317648 0.746726769 2.1818156 4.938490196
15 13 330 467 1399.0 29280.0 0.047780054644808743 0.169738661 2.342e-5 17.517109121 0.751453942 2.187781478 4.923659727
16 14 328 465 1399.0 29184.0 0.047937225877192985 0.077817676 2.315e-5 17.533304215 0.745481303 2.209343496 4.960503415
17 15 326 463 1399.0 29088.0 0.04809543454345434 0.171584444 2.352e-5 17.579912576 0.754778436 2.210370024 4.934281254
18 16 324 461 1399.0 28992.0 0.04825469094922737 0.084223667 2.305e-5 17.570464754 0.751290178 2.22797709 4.939806799
19 17 322 459 1399.0 28896.0 0.04841500553709856 0.123005102 2.3661e-5 17.605650973 0.756929676 2.269940175 4.937928844
20 18 320 457 1399.0 28800.0 0.04857638888888889 0.086677986 2.37e-5 17.5539199 0.746367967 2.264938904 4.959258096
21 19 318 455 1399.0 28704.0 0.04873885172798216 0.12293158 2.3711e-5 17.609395222 0.755783994 2.264754078 4.92827168
22 20 316 453 1399.0 28608.0 0.04890240492170023 0.124475123 2.4281e-5 17.597716228 0.75106304 2.20218749 4.933120236
23 21 314 451 1399.0 28512.0 0.04906705948372615 0.112172177 2.6391e-5 17.623178954 0.755694751 2.186417905 4.921509117
24 22 312 449 1399.0 28416.0 0.04923282657657658 0.219362642 2.321e-5 17.593459902 0.747914841 2.168628993 4.952994795
25 23 310 447 1399.0 28320.0 0.049399717514124294 0.080729209 2.358e-5 17.571675834 0.755489634 2.209531477 4.951190234
26 24 308 445 1399.0 28224.0 0.049567743764172334 0.080235835 2.3271e-5 17.615791747 0.750314688 2.21464245 4.949496195
27 25 306 443 1399.0 28128.0 0.049736916951080776 0.124106403 2.374e-5 17.60716179 0.753826187 2.186184237 4.920128786
28 26 304 441 1399.0 28032.0 0.04990724885844749 0.080715608 2.3781e-5 17.581988477 0.750266997 2.209826064 4.937813884
29 27 302 439 1399.0 27936.0 0.05007875143184422 0.080606465 2.4071e-5 17.633096607 0.749125265 2.198599437 4.935320693
30 28 300 437 1399.0 27840.0 0.0502514367816092 0.081056137 2.3781e-5 17.564695624 0.746230293 2.225110355 4.939656214
31 29 298 435 1399.0 27744.0 0.05042531718569781 0.096545225 2.379e-5 17.58144781 0.747458632 2.263551336 4.924245431
32 30 296 433 1399.0 27648.0 0.050600405092592594 0.120638697 2.383e-5 17.574370836 0.748933285 2.234417803 4.915183371
33 31 294 431 1399.0 27552.0 0.0507767131242741 0.125073582 2.393e-5 17.627352699 0.754384428 2.214199106 4.938130459
34 32 292 429 1399.0 27456.0 0.05095425407925408 0.12314953 2.468e-5 17.697160429 0.796488763 2.261473826 4.956976138
35 33 290 427 1399.0 27360.0 0.051133040935672516 0.125481487 2.354e-5 17.636971006 0.748416796 2.222200724 4.948970096
36 34 288 425 1399.0 27264.0 0.051313086854460094 0.094052012 2.4301e-5 17.62971842 0.805139938 2.205015347 4.959455536
37 35 286 423 1399.0 27168.0 0.051494405182567725 0.08136377 2.4041e-5 17.621304482 0.747718686 2.244362062 4.941432169
38 36 284 421 1399.0 27072.0 0.05167700945626478 0.080217839 2.3921e-5 17.61427713 0.747754586 2.212103901 4.933185029
39 37 282 417 1399.0 26976.0 0.051860913404507714 0.126372199 2.376e-5 17.601417663 0.750036789 2.163344775 4.926698186
40 38 280 414 1399.0 26880.0 0.052046130952380955 0.125444544 2.476e-5 17.612452443 0.748155225 2.195259021 4.91594575
41 39 278 412 1399.0 26784.0 0.05223267622461171 0.083158944 2.4551e-5 17.599589645 0.741671021 2.208064301 4.9351555
42 40 276 410 1399.0 26688.0 0.05242056354916067 0.083321959 2.4101e-5 17.567124159 0.748238012 2.197233222 4.954754226
43 41 274 408 1399.0 26592.0 0.052609807460890494 0.084803792 2.3901e-5 17.549365204 0.754817994 2.229499405 4.94957165
44 42 272 405 1399.0 26496.0 0.05280042270531401 0.127648261 2.3851e-5 17.582852416 0.750759497 2.230398721 4.937220319
45 43 270 401 1399.0 26400.0 0.052992424242424244 0.128445184 2.428e-5 17.596647819 0.75777713 2.160922996 4.937371146
46 44 268 399 1399.0 26304.0 0.053185827250608275 0.129526096 2.5081e-5 17.594476326 0.746906342 2.219401891 4.93357998
47 45 266 397 1399.0 26208.0 0.05338064713064713 0.129819495 2.4731e-5 17.568331366 0.750368555 2.18948505 4.922275732
48 46 264 394 1399.0 26112.0 0.05357689950980392 0.087649075 2.462e-5 17.585414218 0.751605626 2.198684054 4.941424565
49 47 262 391 1399.0 26016.0 0.05377460024600246 0.089110637 2.4551e-5 17.614139291 0.750622403 2.168793662 4.953321773
50 48 260 389 1399.0 25920.0 0.053973765432098766 0.090307061 2.45e-5 17.633806293 0.749096576 2.224521298 4.930813246
51 49 258 387 1399.0 25824.0 0.054174411400247834 0.133480181 2.461e-5 17.634768586 0.756613261 2.201452177 4.972809945
52 50 256 385 1399.0 25728.0 0.05437655472636816 0.134254424 2.425e-5 17.606323938 0.748779206 2.216818872 4.939295094
53 51 254 382 1399.0 25632.0 0.05458021223470662 0.134016868 2.4531e-5 17.5926305 0.75625873 2.227679889 4.968213894
54 52 252 379 1399.0 25536.0 0.054785401002506263 0.135650945 2.4601e-5 17.642803637 0.751975585 2.226011125 4.9285844
55 53 250 375 1399.0 25440.0 0.054992138364779876 0.136647933 2.4161e-5 17.799738254 0.76667472 2.165144989 4.930427128
56 54 248 373 1399.0 25344.0 0.05520044191919192 0.123103164 2.4461e-5 17.745879754 0.760526742 2.161495227 4.940492285
57 55 246 370 1399.0 25248.0 0.05541032953105197 0.09476826 2.3511e-5 17.596131758 0.756924114 2.180021837 4.954121771
58 56 244 365 1399.0 25152.0 0.05562181933842239 0.095345787 2.4171e-5 17.612023424 0.747989147 2.215139082 4.945396527
59 57 242 362 1399.0 25056.0 0.05583492975734355 0.139570128 2.3801e-5 17.630922372 0.750668446 2.186529739 4.961981394
60 58 240 359 1399.0 24960.0 0.05604967948717949 0.097466916 2.4451e-5 17.61078772 0.7485922 2.217673752 4.95291513
61 59 238 357 1399.0 24864.0 0.05626608751608752 0.138599302 2.3601e-5 17.586404505 0.756929027 2.233374301 4.935342135
62 60 236 352 1399.0 24768.0 0.05648417312661499 0.147210964 2.4911e-5 17.650436019 0.74908103 2.157077946 4.937714591
63 61 234 350 1399.0 24672.0 0.05670395590142672 0.099491094 2.3601e-5 17.608002511 0.756924473 2.165309665 4.932434479
64 62 232 348 1399.0 24576.0 0.056925455729166664 0.141929827 2.454e-5 17.605756917 0.749178717 2.234082435 4.957629943
65 63 230 344 1399.0 24480.0 0.057148692810457515 0.142483983 2.4211e-5 17.623883273 0.758216784 2.210078838 4.930940098
66 64 228 341 1399.0 24384.0 0.057373687664041995 0.101524943 2.4371e-5 17.662312587 0.751128917 2.22449657 4.96708528
67 65 226 339 1399.0 24288.0 0.05760046113306983 0.102619253 2.3831e-5 17.610112922 0.758167777 2.187456785 4.957519684
68 66 224 337 1399.0 24192.0 0.05782903439153439 0.10351088 2.3401e-5 17.611932402 0.749178457 2.236980212 4.933450322
69 67 222 335 1399.0 24096.0 0.05805942895086321 0.148780402 2.3711e-5 17.636035095 0.75707833 2.252138664 4.951632995
70 68 220 333 1399.0 24000.0 0.058291666666666665 0.148311059 2.4851e-5 17.617252052 0.750104986 2.22330739 4.9243139
71 69 218 329 1399.0 23904.0 0.05852576974564926 0.151678794 2.4181e-5 17.627742278 0.755299894 2.248062201 4.951401482
72 70 216 326 1399.0 23808.0 0.05876176075268817 0.15082361 2.3851e-5 17.647410652 0.752445605 2.240948426 4.949599133
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data/qed_ke-kkke_reduction_optimizer.csv Normal file
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@ -0,0 +1,82 @@
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24,252,347,927.0,26496.0,0.034986413043478264,0.103568232,1.7471e-5,10.363216025,0.602207417,1.943794016,2.811132729
25,247,342,927.0,26016.0,0.035631918819188195,0.102006829,1.669e-5,10.360319761,0.588967585,1.942523675,2.838431844
26,246,340,921.0,26016.0,0.03540129151291513,0.103244544,1.672e-5,10.140255758,0.565172778,1.980058606,2.776594151
27,245,339,921.0,25920.0,0.03553240740740741,0.102991317,1.723e-5,10.166352736,0.588556746,2.025713505,2.754827976
28,244,337,915.0,25920.0,0.03530092592592592,0.102527335,1.6261e-5,9.965044496,0.527648944,1.966870364,2.708992883
29,243,335,909.0,25920.0,0.035069444444444445,0.101020632,1.6541e-5,9.899918186,0.530837495,1.99964346,2.686936268
30,242,334,909.0,25824.0,0.03519981412639405,0.099846559,1.614e-5,9.924451078,0.532149983,1.992832633,2.667590089
31,241,333,909.0,25728.0,0.035331156716417914,0.103293156,1.634e-5,9.893503718,0.500188044,1.971455575,2.661440862
32,236,328,909.0,25248.0,0.036002851711026615,0.110948742,1.5851e-5,9.916889596,0.515528547,2.014256204,2.691654688
33,235,326,903.0,25248.0,0.03576520912547528,0.099799239,1.658e-5,9.667648582,0.561210643,1.981308261,2.647665444
34,234,324,897.0,25248.0,0.035527566539923956,0.099455409,1.6561e-5,9.588166052,0.544847505,1.932560182,2.56349283
35,233,323,897.0,25152.0,0.035663167938931296,0.103335368,1.6271e-5,9.590387462,0.542413718,1.965145602,2.559435691
36,232,321,891.0,25152.0,0.03542461832061069,0.097770562,1.6571e-5,9.362808632,0.543288523,2.017894491,2.498672404
37,231,320,891.0,25056.0,0.03556034482758621,0.100428616,1.5941e-5,9.340302395,0.548822639,1.994799194,2.525394
38,230,319,891.0,24960.0,0.03569711538461538,0.056667955,1.5341e-5,9.356871677,0.537041949,1.921246656,2.507595034
39,225,314,891.0,24480.0,0.036397058823529414,0.099323026,1.636e-5,9.383625024,0.506403697,1.972101141,2.529248938
40,220,309,836.0,24480.0,0.03415032679738562,0.096789665,1.645e-5,9.524601658,0.473707387,1.980933173,2.524768525
41,215,304,836.0,24000.0,0.034833333333333334,0.053463925,1.671e-5,9.520567128,0.487585179,1.942542795,2.535491481
42,214,302,830.0,24000.0,0.034583333333333334,0.096303802,1.6011e-5,9.137262758,0.4297148,1.950560163,2.478408276
43,213,301,830.0,23904.0,0.034722222222222224,0.070596338,1.6901e-5,9.143790565,0.492842898,1.949332161,2.476752284
44,212,299,824.0,23904.0,0.034471218206157964,0.09696925,1.612e-5,9.089211511,0.456930617,2.022026121,2.419473874
45,211,297,818.0,23904.0,0.03422021419009371,0.052526649,1.536e-5,8.807671694,0.471203239,1.970488502,2.372441242
46,210,296,818.0,23808.0,0.03435819892473118,0.096716114,1.5701e-5,8.806210783,0.451452844,1.960073481,2.387451098
47,209,295,818.0,23712.0,0.034497300944669365,0.05145174,1.6061e-5,8.867215342,0.450895098,1.968012818,2.394204111
48,204,290,818.0,23232.0,0.03521005509641873,0.093248236,1.9521e-5,8.844517253,0.476030278,1.963827031,2.389413849
49,203,288,812.0,23232.0,0.034951790633608815,0.093881584,1.527e-5,8.849095772,0.446415074,1.974782212,2.332439097
50,202,287,812.0,23136.0,0.03509681881051176,0.050473481,1.5851e-5,8.784636116,0.469233287,1.953068913,2.321316886
51,201,285,806.0,23136.0,0.034837482710926695,0.092750242,1.5541e-5,8.632088328,0.491467054,1.945455141,2.29300329
52,200,284,806.0,23040.0,0.03498263888888889,0.092540087,1.7161e-5,8.637677414,0.471865872,1.975464118,2.259260411
53,199,282,800.0,23040.0,0.034722222222222224,0.092944049,1.5261e-5,8.624992966,0.478249573,1.931707577,2.232058939
54,198,281,800.0,22944.0,0.03486750348675035,0.091660013,1.575e-5,8.680034605,0.429976994,2.022314921,2.224544849
55,197,279,794.0,22944.0,0.03460599721059972,0.092591389,1.582e-5,8.266084761,0.442472956,1.949268775,2.165130527
56,196,278,794.0,22848.0,0.03475140056022409,0.090376966,1.529e-5,8.26930839,0.438461132,1.960119483,2.169387658
57,191,273,739.0,22848.0,0.03234418767507003,0.090398736,1.589e-5,8.061516101,0.468233752,1.825342557,2.144808638
58,186,268,739.0,22368.0,0.03303826895565093,0.090566151,1.5781e-5,8.051685873,0.472555774,1.827021946,2.175475243
59,185,266,733.0,22368.0,0.03277002861230329,0.046301524,1.4931e-5,7.809555195,0.466519375,1.819191936,2.095906173
60,184,264,727.0,22368.0,0.03250178826895565,0.087977349,1.4771e-5,7.825535183,0.452072238,1.820734702,2.06485156
61,183,263,727.0,22272.0,0.032641882183908046,0.08908488,1.4591e-5,7.77560322,0.445728609,1.804235078,2.06763398
62,182,262,727.0,22176.0,0.03278318903318903,0.076517376,1.461e-5,7.754359737,0.421063625,1.812681957,2.076417548
63,181,260,721.0,22176.0,0.032512626262626264,0.088983767,1.4091e-5,7.616158878,0.422402602,1.868182992,2.016601005
64,180,259,721.0,22080.0,0.03265398550724638,0.089172453,1.467e-5,7.63910266,0.402654247,1.844390793,2.031385412
65,175,254,666.0,22080.0,0.03016304347826087,0.091971222,1.3851e-5,7.35822511,0.443635961,1.719023302,2.007792679
66,170,249,666.0,21600.0,0.030833333333333334,0.073480651,1.3871e-5,7.291999508,0.434965958,1.750073777,1.999358953
67,169,247,660.0,21600.0,0.030555555555555555,0.085309774,1.7211e-5,7.245192983,0.412650069,1.744681817,1.962798523
68,168,245,654.0,21600.0,0.03027777777777778,0.089043539,1.367e-5,7.024436477,0.421292773,1.722710908,1.890918459
69,167,243,648.0,21600.0,0.03,0.084353527,1.428e-5,6.8832018,0.415786727,1.715216258,1.830282141
70,166,242,648.0,21504.0,0.030133928571428572,0.084367977,1.3441e-5,6.899982477,0.419080281,1.707637056,1.843529005
71,165,241,648.0,21408.0,0.030269058295964126,0.085701815,1.4031e-5,6.936174291,0.377346024,1.704252961,1.85218872
72,164,240,648.0,21312.0,0.030405405405405407,0.083910355,1.3601e-5,6.9051589,0.389477478,1.75740328,1.867258596
73,159,235,593.0,21312.0,0.0278246996996997,0.082135195,1.3351e-5,7.031037571,0.356084586,1.631072,1.797434919
74,154,230,593.0,20832.0,0.028465821812596007,0.080356395,1.358e-5,7.040766129,0.405151789,1.620631997,1.781269114
75,153,228,587.0,20832.0,0.02817780337941628,0.066967517,1.3391e-5,6.644186555,0.395240289,1.641155866,1.743666486
76,152,226,581.0,20832.0,0.02788978494623656,0.080763676,1.298e-5,6.633937959,0.388869331,1.630064054,1.701302723
77,151,225,581.0,20736.0,0.028018904320987654,0.080671833,1.2781e-5,6.622133299,0.392564435,1.625932508,1.711411428
78,150,224,581.0,20640.0,0.02814922480620155,0.080368195,1.358e-5,6.599986437,0.397419271,1.657700695,1.694756709
79,149,222,575.0,20640.0,0.027858527131782947,0.080015475,1.298e-5,6.281191715,0.37819019,1.622522233,1.656839741
80,148,221,575.0,20544.0,0.027988707165109036,0.065331671,1.334e-5,6.313635402,0.380955078,1.627111603,1.638795233
1 operations graph_nodes graph_edges graph_ce graph_dt graph_ci gen_func_t cpu_compile_t cpu_st_t cpu_mt_t gpu_compile_t gpu_t
2 0 356 493 1399.0 30528.0 0.0458267819706499 0.084389903 2.4971e-5 17.802549835 0.960409581 2.406448706 4.927079076
3 1 351 483 1369.0 30528.0 0.044844077568134175 0.126855933 2.9211e-5 16.868735557 0.927387188 2.257632484 4.697683068
4 2 346 478 1369.0 30048.0 0.04556043663471779 0.08319682 3.5431e-5 16.871399152 0.834869326 2.264361993 4.701280771
5 3 341 473 1314.0 30048.0 0.04373003194888179 0.124422234 2.392e-5 16.454231193 0.856669072 2.271991539 4.68580348
6 4 336 463 1284.0 30048.0 0.042731629392971246 0.121696991 2.2921e-5 15.881542683 0.816430136 2.213686135 4.449106524
7 5 331 458 1284.0 29568.0 0.04342532467532467 0.124024888 2.314e-5 15.879200155 0.799333453 2.194093083 4.435654931
8 6 326 448 1254.0 29568.0 0.04241071428571429 0.121610951 2.2e-5 15.325702423 0.833341953 2.203843882 4.199677306
9 7 321 438 1224.0 29568.0 0.041396103896103896 0.118972208 2.1631e-5 14.367273685 0.711553932 2.16189756 3.948872646
10 8 316 433 1224.0 29088.0 0.04207920792079208 0.074826839 2.2031e-5 14.367107152 0.792981221 2.169096496 3.961630969
11 9 311 428 1169.0 29088.0 0.04018839383938394 0.116237162 2.15e-5 14.416973472 0.788583102 2.092186151 3.946339564
12 10 306 418 1139.0 29088.0 0.03915704070407041 0.114647398 2.031e-5 13.671420757 0.745657392 2.037551329 3.657411205
13 11 301 408 1109.0 29088.0 0.03812568756875687 0.11434652 1.951e-5 13.093103664 0.686554396 2.065489584 3.441139671
14 12 296 403 1109.0 28608.0 0.03876538031319911 0.112282663 1.8991e-5 13.11525848 0.705183633 2.0639299 3.422598036
15 13 291 398 1109.0 28128.0 0.039426905574516495 0.111549203 1.9661e-5 13.08100601 0.700772882 2.065935946 3.41679234
16 14 286 388 1079.0 28128.0 0.0383603526734926 0.109881396 1.907e-5 11.871746271 0.665244638 2.063828106 3.187580585
17 15 281 378 1049.0 28128.0 0.037293799772468716 0.108444747 1.7961e-5 10.963517612 0.62180291 2.037926216 2.935137574
18 16 276 373 1049.0 27648.0 0.03794126157407408 0.107959773 1.874e-5 11.021594456 0.541779823 2.003876106 2.931304737
19 17 271 368 1049.0 27168.0 0.03861160188457008 0.105629068 1.8241e-5 11.017450178 0.581974375 2.017201027 2.952118903
20 18 266 363 1049.0 26688.0 0.0393060551558753 0.107303406 1.8301e-5 11.028597789 0.556078309 2.037535226 2.911405619
21 19 261 358 994.0 26688.0 0.03724520383693045 0.106584986 1.7111e-5 10.789192026 0.525275525 2.011931363 2.931360979
22 20 256 353 939.0 26688.0 0.035184352517985615 0.105743463 1.7521e-5 10.50283261 0.535253087 1.962456949 2.941274646
23 21 255 351 933.0 26688.0 0.03495953237410072 0.105189187 1.7471e-5 10.739591259 0.555102576 2.013201521 2.896175037
24 22 254 350 933.0 26592.0 0.035085740072202165 0.105895137 1.6631e-5 10.68514711 0.571809578 1.974934611 2.890503396
25 23 253 348 927.0 26592.0 0.0348601083032491 0.104181459 1.817e-5 10.344271645 0.572483889 2.002875753 2.842241926
26 24 252 347 927.0 26496.0 0.034986413043478264 0.103568232 1.7471e-5 10.363216025 0.602207417 1.943794016 2.811132729
27 25 247 342 927.0 26016.0 0.035631918819188195 0.102006829 1.669e-5 10.360319761 0.588967585 1.942523675 2.838431844
28 26 246 340 921.0 26016.0 0.03540129151291513 0.103244544 1.672e-5 10.140255758 0.565172778 1.980058606 2.776594151
29 27 245 339 921.0 25920.0 0.03553240740740741 0.102991317 1.723e-5 10.166352736 0.588556746 2.025713505 2.754827976
30 28 244 337 915.0 25920.0 0.03530092592592592 0.102527335 1.6261e-5 9.965044496 0.527648944 1.966870364 2.708992883
31 29 243 335 909.0 25920.0 0.035069444444444445 0.101020632 1.6541e-5 9.899918186 0.530837495 1.99964346 2.686936268
32 30 242 334 909.0 25824.0 0.03519981412639405 0.099846559 1.614e-5 9.924451078 0.532149983 1.992832633 2.667590089
33 31 241 333 909.0 25728.0 0.035331156716417914 0.103293156 1.634e-5 9.893503718 0.500188044 1.971455575 2.661440862
34 32 236 328 909.0 25248.0 0.036002851711026615 0.110948742 1.5851e-5 9.916889596 0.515528547 2.014256204 2.691654688
35 33 235 326 903.0 25248.0 0.03576520912547528 0.099799239 1.658e-5 9.667648582 0.561210643 1.981308261 2.647665444
36 34 234 324 897.0 25248.0 0.035527566539923956 0.099455409 1.6561e-5 9.588166052 0.544847505 1.932560182 2.56349283
37 35 233 323 897.0 25152.0 0.035663167938931296 0.103335368 1.6271e-5 9.590387462 0.542413718 1.965145602 2.559435691
38 36 232 321 891.0 25152.0 0.03542461832061069 0.097770562 1.6571e-5 9.362808632 0.543288523 2.017894491 2.498672404
39 37 231 320 891.0 25056.0 0.03556034482758621 0.100428616 1.5941e-5 9.340302395 0.548822639 1.994799194 2.525394
40 38 230 319 891.0 24960.0 0.03569711538461538 0.056667955 1.5341e-5 9.356871677 0.537041949 1.921246656 2.507595034
41 39 225 314 891.0 24480.0 0.036397058823529414 0.099323026 1.636e-5 9.383625024 0.506403697 1.972101141 2.529248938
42 40 220 309 836.0 24480.0 0.03415032679738562 0.096789665 1.645e-5 9.524601658 0.473707387 1.980933173 2.524768525
43 41 215 304 836.0 24000.0 0.034833333333333334 0.053463925 1.671e-5 9.520567128 0.487585179 1.942542795 2.535491481
44 42 214 302 830.0 24000.0 0.034583333333333334 0.096303802 1.6011e-5 9.137262758 0.4297148 1.950560163 2.478408276
45 43 213 301 830.0 23904.0 0.034722222222222224 0.070596338 1.6901e-5 9.143790565 0.492842898 1.949332161 2.476752284
46 44 212 299 824.0 23904.0 0.034471218206157964 0.09696925 1.612e-5 9.089211511 0.456930617 2.022026121 2.419473874
47 45 211 297 818.0 23904.0 0.03422021419009371 0.052526649 1.536e-5 8.807671694 0.471203239 1.970488502 2.372441242
48 46 210 296 818.0 23808.0 0.03435819892473118 0.096716114 1.5701e-5 8.806210783 0.451452844 1.960073481 2.387451098
49 47 209 295 818.0 23712.0 0.034497300944669365 0.05145174 1.6061e-5 8.867215342 0.450895098 1.968012818 2.394204111
50 48 204 290 818.0 23232.0 0.03521005509641873 0.093248236 1.9521e-5 8.844517253 0.476030278 1.963827031 2.389413849
51 49 203 288 812.0 23232.0 0.034951790633608815 0.093881584 1.527e-5 8.849095772 0.446415074 1.974782212 2.332439097
52 50 202 287 812.0 23136.0 0.03509681881051176 0.050473481 1.5851e-5 8.784636116 0.469233287 1.953068913 2.321316886
53 51 201 285 806.0 23136.0 0.034837482710926695 0.092750242 1.5541e-5 8.632088328 0.491467054 1.945455141 2.29300329
54 52 200 284 806.0 23040.0 0.03498263888888889 0.092540087 1.7161e-5 8.637677414 0.471865872 1.975464118 2.259260411
55 53 199 282 800.0 23040.0 0.034722222222222224 0.092944049 1.5261e-5 8.624992966 0.478249573 1.931707577 2.232058939
56 54 198 281 800.0 22944.0 0.03486750348675035 0.091660013 1.575e-5 8.680034605 0.429976994 2.022314921 2.224544849
57 55 197 279 794.0 22944.0 0.03460599721059972 0.092591389 1.582e-5 8.266084761 0.442472956 1.949268775 2.165130527
58 56 196 278 794.0 22848.0 0.03475140056022409 0.090376966 1.529e-5 8.26930839 0.438461132 1.960119483 2.169387658
59 57 191 273 739.0 22848.0 0.03234418767507003 0.090398736 1.589e-5 8.061516101 0.468233752 1.825342557 2.144808638
60 58 186 268 739.0 22368.0 0.03303826895565093 0.090566151 1.5781e-5 8.051685873 0.472555774 1.827021946 2.175475243
61 59 185 266 733.0 22368.0 0.03277002861230329 0.046301524 1.4931e-5 7.809555195 0.466519375 1.819191936 2.095906173
62 60 184 264 727.0 22368.0 0.03250178826895565 0.087977349 1.4771e-5 7.825535183 0.452072238 1.820734702 2.06485156
63 61 183 263 727.0 22272.0 0.032641882183908046 0.08908488 1.4591e-5 7.77560322 0.445728609 1.804235078 2.06763398
64 62 182 262 727.0 22176.0 0.03278318903318903 0.076517376 1.461e-5 7.754359737 0.421063625 1.812681957 2.076417548
65 63 181 260 721.0 22176.0 0.032512626262626264 0.088983767 1.4091e-5 7.616158878 0.422402602 1.868182992 2.016601005
66 64 180 259 721.0 22080.0 0.03265398550724638 0.089172453 1.467e-5 7.63910266 0.402654247 1.844390793 2.031385412
67 65 175 254 666.0 22080.0 0.03016304347826087 0.091971222 1.3851e-5 7.35822511 0.443635961 1.719023302 2.007792679
68 66 170 249 666.0 21600.0 0.030833333333333334 0.073480651 1.3871e-5 7.291999508 0.434965958 1.750073777 1.999358953
69 67 169 247 660.0 21600.0 0.030555555555555555 0.085309774 1.7211e-5 7.245192983 0.412650069 1.744681817 1.962798523
70 68 168 245 654.0 21600.0 0.03027777777777778 0.089043539 1.367e-5 7.024436477 0.421292773 1.722710908 1.890918459
71 69 167 243 648.0 21600.0 0.03 0.084353527 1.428e-5 6.8832018 0.415786727 1.715216258 1.830282141
72 70 166 242 648.0 21504.0 0.030133928571428572 0.084367977 1.3441e-5 6.899982477 0.419080281 1.707637056 1.843529005
73 71 165 241 648.0 21408.0 0.030269058295964126 0.085701815 1.4031e-5 6.936174291 0.377346024 1.704252961 1.85218872
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82 80 148 221 575.0 20544.0 0.027988707165109036 0.065331671 1.334e-5 6.313635402 0.380955078 1.627111603 1.638795233

79
data/qed_ke-kkkkke_reduction_optimizer.csv Normal file
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@ -0,0 +1,79 @@
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1 operations graph_nodes graph_edges graph_ce graph_dt graph_ci gen_func_t cpu_compile_t cpu_st_t cpu_mt_t gpu_compile_t gpu_t
2 0 15866 21617 66249.0 1.314048e6 0.050415966540035065 6.468999136 0.001398329 8.478099553 0.43958521 0.0 0.0
3 10 14676 19713 60656.0 1.279776e6 0.0473957942639962 5.993535435 0.000745961 7.192805963 0.417393835 0.0 0.0
4 20 13774 18527 56334.0 1.243296e6 0.04531020770596865 5.489738392 0.000682889 6.652182167 0.336339503 0.0 0.0
5 30 13352 17940 53276.0 1.236672e6 0.04308013765978368 5.169906767 0.000675318 6.370526843 0.313517861 0.0 0.0
6 40 12714 17168 51163.0 1.199712e6 0.042646068389746876 4.845906388 0.000634457 6.124306725 0.311820244 0.0 0.0
7 50 12004 16270 48473.0 1.163232e6 0.04167096503534978 4.433653313 0.000596017 5.760561483 0.320897852 0.0 0.0
8 60 11750 15983 48022.0 1.144224e6 0.04196905501020779 4.316924709 0.000596237 5.738809149 0.283214404 0.0 0.0
9 70 11538 15697 47325.0 1.133184e6 0.04176285581158929 4.201152631 0.000554855 5.438337093 0.313985744 0.0 0.0
10 80 11434 15550 46814.0 1.129536e6 0.04144533684628024 4.216359254 0.000553545 5.429706297 0.268223845 0.0 0.0
11 90 11066 15085 46232.0 1.10352e6 0.041895026823256486 3.924567625 0.000560535 5.412444055 0.274917428 0.0 0.0
12 100 10848 14847 44297.0 1.100352e6 0.04025711772232885 3.848048388 0.000527955 5.127227854 0.294706757 0.0 0.0
13 110 10462 14382 42261.0 1.084512e6 0.038967756926617685 3.674674179 0.000509054 4.922064369 0.276530272 0.0 0.0
14 120 10304 14191 41810.0 1.07472e6 0.038903156170909635 3.58233155 0.000516074 5.02371138 0.266906519 0.0 0.0
15 130 10200 14067 41437.0 1.068864e6 0.03876732680677804 3.529160319 0.000501634 4.863804478 0.24639169 0.0 0.0
16 140 10042 13871 40956.0 1.059552e6 0.03865407266467337 3.346890818 0.000488403 4.753116119 0.254509861 0.0 0.0
17 150 9956 13765 40583.0 1.055424e6 0.038451844945727974 3.41847396 0.000500654 4.756966153 0.255966291 0.0 0.0
18 160 9906 13690 40433.0 1.053024e6 0.03839703558513386 3.405093274 0.000496774 4.812050085 0.24421971 0.0 0.0
19 170 9838 13597 40283.0 1.048896e6 0.038405142168527674 3.348340057 0.000481363 4.669473296 0.234701411 0.0 0.0
20 180 9242 12790 37708.0 1.02336e6 0.03684724828017511 3.063089187 0.000449352 4.335668832 0.228471471 0.0 0.0
21 190 9120 12648 37082.0 1.017984e6 0.03642689865459575 2.994073054 0.000429002 4.181894908 0.224361729 0.0 0.0
22 200 9052 12555 36932.0 1.013856e6 0.03642726383233911 3.046147594 0.000427282 4.151250123 0.212513705 0.0 0.0
23 210 8912 12405 36366.0 1.005792e6 0.03615658108237091 2.937579863 0.000433982 4.261727394 0.214012817 0.0 0.0
24 220 8808 12281 35993.0 999936.0 0.035995303699436765 2.892146284 0.000432382 4.198423468 0.219749812 0.0 0.0
25 230 8626 12061 35765.0 986112.0 0.03626869970145379 2.752333211 0.000414672 4.035044142 0.241721263 0.0 0.0
26 240 8426 11841 34336.0 980256.0 0.03502758463095355 2.714773746 0.000414522 4.036870861 0.235365769 0.0 0.0
27 250 8118 11464 33416.0 961728.0 0.03474579090969588 2.579966689 0.000402461 3.870568035 0.20937257 0.0 0.0
28 260 7942 11242 32634.0 953664.0 0.034219599355747934 2.520293442 0.000391581 3.72881432 0.191238985 0.0 0.0
29 270 7838 11100 32153.0 949536.0 0.0338618019748593 2.456319106 0.000383211 3.635092003 0.187908484 0.0 0.0
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32 300 7376 10529 30487.0 924480.0 0.0329774575977847 2.279512925 0.00036552 3.504723807 0.191079171 0.0 0.0
33 310 7218 10310 29868.0 917376.0 0.03255807869401423 2.207692656 0.000355539 3.30937664 0.181261073 0.0 0.0
34 320 7078 10137 29417.0 909312.0 0.03235083227759009 2.147511905 0.000352659 3.30461376 0.18005858 0.0 0.0
35 330 6860 9848 28991.0 895200.0 0.032384941912421805 2.078259266 0.00033941 3.211808988 0.172834084 0.0 0.0
36 340 6702 9611 28264.0 889824.0 0.03176358470888625 2.069880378 0.000318959 3.033092324 0.154811992 0.0 0.0
37 350 6616 9505 27891.0 885696.0 0.03149048883589855 2.005510172 0.000326369 3.008426711 0.173417779 0.0 0.0
38 360 6512 9391 27325.0 881088.0 0.03101279327377061 1.968347618 0.000315789 2.921325386 0.168873786 0.0 0.0
39 370 6426 9280 27175.0 875232.0 0.03104891046031224 1.92734893 0.000315548 2.990437001 0.181187901 0.0 0.0
40 380 6358 9187 27025.0 871104.0 0.031023850194695467 1.889258172 0.000308689 2.846738111 0.181651873 0.0 0.0
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4
docs/Project.toml Normal file
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@ -0,0 +1,4 @@
[deps]
Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
DocumenterTools = "35a29f4d-8980-5a13-9543-d66fff28ecb8"
MetagraphOptimization = "3e869610-d48d-4942-ba70-c1b702a33ca4"

34
docs/make.jl Normal file
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@ -0,0 +1,34 @@
using Documenter
using MetagraphOptimization
makedocs(
#format = Documenter.LaTeX(platform=""),
root = "docs",
source = "src",
build = "build",
warnonly = true,
clean = true,
doctest = true,
modules = Module[MetagraphOptimization],
#repo = "https://code.woubery.com/Rubydragon/MetagraphOptimization.jl/src/branch/{commit}{path}#L{line}",
remotes = nothing,
sitename = "MetagraphOptimization.jl",
pages = [
"index.md",
"Manual" => "manual.md",
"Library" => [
"Public" => "lib/public.md",
"Graph" => "lib/internals/graph.md",
"Node" => "lib/internals/node.md",
"Task" => "lib/internals/task.md",
"Operation" => "lib/internals/operation.md",
"Models" => "lib/internals/models.md",
"Diff" => "lib/internals/diff.md",
"Utility" => "lib/internals/utility.md",
"Code Generation" => "lib/internals/code_gen.md",
"Devices" => "lib/internals/devices.md",
],
"Contribution" => "contribution.md",
],
)

259
docs/src/QED-Bhabha-DAG.drawio Normal file
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@ -0,0 +1,259 @@
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# Contribution
This is currently in development for a diploma thesis and is therefore private and impossible to contribute to.

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# MetagraphOptimization.jl
*A domain-specific DAG-optimizer*
## Package Features
- Read a DAG from a file
- Analyze its properties
- Mute the graph using the operations NodeFusion, NodeReduction and NodeSplit
## Coming Soon:
- Add Code Generation from finished DAG
- Add optimization algorithms and strategies
## Library Outline
```@contents
Pages = [
"lib/public.md",
"lib/internals.md"
]
```
### [Index](@id main-index)
```@index
Pages = ["lib/public.md"]
```

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# Code Generation
## Types
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["code_gen/type.jl"]
Order = [:type, :constant, :function]
```
## Function Generation
Implementations for generation of a callable function. A function generated this way cannot immediately be called. One Julia World Age has to pass before this is possible, which happens when the global Julia scope advances. If the DAG and therefore the generated function becomes too large, use the tape machine instead, since compiling large functions becomes infeasible.
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["code_gen/function.jl"]
Order = [:function]
```
## Tape Machine
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["code_gen/tabe_machine.jl"]
Order = [:function]
```

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# Devices
## Interface
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["devices/interface.jl"]
Order = [:type, :constant, :function]
```
## Detect
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["devices/detect.jl"]
Order = [:function]
```
## Measure
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["devices/measure.jl"]
Order = [:function]
```
## Implementations
### General
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["devices/impl.jl"]
Order = [:type, :function]
```
### NUMA
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["devices/numa/impl.jl"]
Order = [:type, :function]
```
### CUDA
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["devices/cuda/impl.jl"]
Order = [:type, :function]
```
### ROCm
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["devices/rocm/impl.jl"]
Order = [:type, :function]
```
### oneAPI
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["devices/oneapi/impl.jl"]
Order = [:type, :function]
```

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# Diff
## Type
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["diff/type.jl"]
Order = [:type]
```
## Properties
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["diff/properties.jl"]
Order = [:function]
```
## Printing
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["diff/print.jl"]
Order = [:function]
```

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# Estimation
## Interface
The interface that has to be implemented for an estimator.
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["estimator/interafce.jl"]
Order = [:type, :constant, :function]
```
## Global Metric Estimator
Implementation of a global metric estimator. It uses the graph properties compute effort, data transfer, and compute intensity.
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["estimator/global_metric.jl"]
Order = [:type, :function]
```

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# Graph
## Type
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["graph/type.jl"]
Order = [:type]
```
## Interface
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["graph/interface.jl"]
Order = [:function]
```
## Compare
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["graph/compare.jl"]
Order = [:function]
```
## Mute
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["graph/mute.jl"]
Order = [:function]
```
## Print
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["graph/print.jl"]
Order = [:function]
```
## Properties
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["graph/properties.jl"]
Order = [:function]
```
## Validate
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["graph/validate.jl"]
Order = [:function]
```

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# Models
## Interface
The interface that has to be implemented for a model to be usable is defined in `src/models/interface.jl`.
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["models/interface.jl"]
Order = [:type, :constant, :function]
```
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["models/print.jl"]
Order = [:function]
```
## ABC-Model
### Types
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["models/abc/types.jl"]
Order = [:type, :constant]
```
### Particle
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["models/abc/particle.jl"]
Order = [:type, :constant, :function]
```
### Parse
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["models/abc/parse.jl"]
Order = [:function]
```
### Properties
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["models/abc/properties.jl"]
Order = [:function]
```
### Create
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["models/abc/create.jl"]
Order = [:function]
```
### Compute
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["models/abc/compute.jl"]
Order = [:function]
```
### Print
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["models/abc/print.jl"]
Order = [:function]
```
## QED-Model
### Feynman Diagrams
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["models/qed/diagrams.jl"]
Order = [:type, :function, :constant]
```
### Types
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["models/qed/types.jl"]
Order = [:type, :constant]
```
### Particle
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["models/qed/particle.jl"]
Order = [:type, :constant, :function]
```
### Parse
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["models/qed/parse.jl"]
Order = [:function]
```
### Properties
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["models/qed/properties.jl"]
Order = [:function]
```
### Create
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["models/qed/create.jl"]
Order = [:function]
```
### Compute
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["models/qed/compute.jl"]
Order = [:function]
```
### Print
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["models/qed/print.jl"]
Order = [:function]
```

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# Node
## Type
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["node/type.jl"]
Order = [:type]
```
## Create
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["node/create.jl"]
Order = [:function]
```
## Compare
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["node/compare.jl"]
Order = [:function]
```
## Properties
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["node/properties.jl"]
Order = [:function]
```
## Print
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["node/print.jl"]
Order = [:function]
```
## Validate
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["node/validate.jl"]
Order = [:function]
```

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@ -0,0 +1,57 @@
# Operation
## Types
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["operation/type.jl"]
Order = [:type]
```
## Find
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["operation/find.jl"]
Order = [:function]
```
## Apply
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["operation/apply.jl"]
Order = [:function]
```
## Get
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["operation/get.jl"]
Order = [:function]
```
## Clean
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["operation/clean.jl"]
Order = [:function]
```
## Utility
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["operation/utility.jl"]
Order = [:function]
```
## Print
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["operation/print.jl"]
Order = [:function]
```
## Validate
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["operation/validate.jl"]
Order = [:function]
```

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# Optimization
## Interface
The interface that has to be implemented for an optimization algorithm.
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["optimization/interafce.jl"]
Order = [:type, :constant, :function]
```
## Random Walk Optimizer
Implementation of a random walk algorithm.
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["estimator/random_walk.jl"]
Order = [:type, :function]
```
## Reduction Optimizer
Implementation of a an optimizer that reduces as far as possible.
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["estimator/reduce.jl"]
Order = [:type, :function]
```
## Greedy Optimizer
Implementation of a greedy optimization algorithm.
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["estimator/greedy.jl"]
Order = [:type, :function]
```

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@ -0,0 +1,22 @@
# Properties
## Type
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["properties/type.jl"]
Order = [:type]
```
## Create
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["properties/create.jl"]
Order = [:function]
```
## Utility
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["properties/utility.jl"]
Order = [:function]
```

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@ -0,0 +1,22 @@
# Scheduler
## Interface
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["scheduler/interface.jl"]
Order = [:type, :function]
```
## Types
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["scheduler/type.jl"]
Order = [:type, :function]
```
## Greedy
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["scheduler/greedy.jl"]
Order = [:type, :function]
```

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@ -0,0 +1,36 @@
# Task
## Type
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["task/type.jl"]
Order = [:type]
```
## Create
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["task/create.jl"]
Order = [:function]
```
## Compare
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["task/compare.jl"]
Order = [:function]
```
## Compute
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["task/compute.jl"]
Order = [:function]
```
## Properties
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["task/properties.jl"]
Order = [:function]
```

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@ -0,0 +1,17 @@
# Utility
## Helper Functions
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["./utility.jl"]
Order = [:type, :function]
```
## Trie Helper
This is a simple implementation of a [Trie Data Structure](https://en.wikipedia.org/wiki/Trie) to greatly improve the performance of the Node Reduction search.
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["trie.jl"]
Order = [:type, :function]
```

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@ -0,0 +1,24 @@
# Public Documentation
Documentation for `MetagraphOptimization.jl`'s public interface.
See the Internals section of the manual for documentation of everything else.
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["MetagraphOptimization.jl"]
Order = [:module]
```
## Contents
```@contents
Pages = ["public.md"]
Depth = 2
```
## Index
```@index
Pages = ["public.md"]
```

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@ -0,0 +1,7 @@
# Manual
## Jupyter Notebooks
In the `notebooks` directory are notebooks containing some examples of the usage of this repository.
- `abc_model_showcase`: A simple showcase of the intended usage of the ABC Model implementation.

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8
examples/Project.toml
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@ -1,7 +1,9 @@
[deps]
BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
MetagraphOptimization = "3e869610-d48d-4942-ba70-c1b702a33ca4"
PProf = "e4faabce-9ead-11e9-39d9-4379958e3056"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
ProfileView = "c46f51b8-102a-5cf2-8d2c-8597cb0e0da7"
Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
QEDprocesses = "46de9c38-1bb3-4547-a1ec-da24d767fdad"
StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"

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@ -0,0 +1,33 @@
using MetagraphOptimization
using BenchmarkTools
println("Getting machine info")
@time machine = get_machine_info()
println("Making model")
@time model = ABCModel()
println("Making process")
process_str = "AB->ABBBBB"
@time process = parse_process(process_str, model)
println("Parsing DAG")
@time graph = parse_dag("input/$process_str.txt", model)
println("Generating input data")
@time input_data = [gen_process_input(process) for _ in 1:1000]
println("Reducing graph")
@time optimize_to_fixpoint!(ReductionOptimizer(), graph)
println("Generating compute function")
@time compute_func = get_compute_function(graph, process, machine)
println("First run, single argument")
@time compute_func(input_data[1])
println("\nBenchmarking function, 1 input")
display(@benchmark compute_func($(input_data[1])))
println("\nBenchmarking function, 1000 inputs")
display(@benchmark compute_func.($input_data))

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using MetagraphOptimization
using BenchmarkTools
println("Getting machine info")
@time machine = get_machine_info()
println("Making model")
@time model = ABCModel()
println("Making process")
process_str = "AB->ABBBBBBB"
@time process = parse_process(process_str, model)
println("Parsing DAG")
@time graph = parse_dag("input/$process_str.txt", model)
println("Generating input data")
@time input_data = [gen_process_input(process) for _ in 1:1000]
println("Reducing graph")
@time optimize_to_fixpoint!(ReductionOptimizer(), graph)
println("Generating compute function")
@time compute_func = get_compute_function(graph, process, machine)
println("First run, single argument")
@time compute_func(input_data[1])
println("\nBenchmarking function, 1 input")
display(@benchmark compute_func($(input_data[1])))
println("\nBenchmarking function, 1000 inputs")
display(@benchmark compute_func.($input_data))

11
examples/import_bench.jl
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@ -13,16 +13,15 @@ function bench_txt(filepath::String, bench::Bool = true)
return
end
model = ABCModel()
println(name, ":")
g = parse_abc(filepath)
g = parse_dag(filepath, model)
print(g)
println(
" Graph size in memory: ",
bytes_to_human_readable(MetagraphOptimization.mem(g)),
)
println(" Graph size in memory: ", bytes_to_human_readable(MetagraphOptimization.mem(g)))
if (bench)
@btime parse_abc($filepath)
@btime parse_dag($filepath, $model)
end
println(" Get Operations: ")

23
examples/plot_chain.jl
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@ -12,7 +12,7 @@ function gen_plot(filepath)
return
end
g = parse_abc(filepath)
g = parse_dag(filepath, ABCModel())
Random.seed!(1)
@ -41,30 +41,17 @@ function gen_plot(filepath)
i = i - 1
end
props = graph_properties(g)
props = get_properties(g)
push!(x, props.data)
push!(y, props.compute_effort)
push!(y, props.computeEffort)
end
println("\rDone.")
plot(
[x[1], x[2]],
[y[1], y[2]],
linestyle = :solid,
linewidth = 1,
color = :red,
legend = false,
)
plot([x[1], x[2]], [y[1], y[2]], linestyle = :solid, linewidth = 1, color = :red, legend = false)
# Create lines connecting the reference point to each data point
for i in 3:length(x)
plot!(
[x[i - 1], x[i]],
[y[i - 1], y[i]],
linestyle = :solid,
linewidth = 1,
color = :red,
)
plot!([x[i - 1], x[i]], [y[i - 1], y[i]], linestyle = :solid, linewidth = 1, color = :red)
end
return gui()

62
examples/plot_star.jl
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@ -12,7 +12,7 @@ function gen_plot(filepath)
return
end
g = parse_abc(filepath)
g = parse_dag(filepath, ABCModel())
Random.seed!(1)
@ -44,9 +44,9 @@ function gen_plot(filepath)
props = graph_properties(g)
props = get_properties(g)
x0 = props.data
y0 = props.compute_effort
y0 = props.computeEffort
x = Vector{Float64}()
y = Vector{Float64}()
@ -55,70 +55,36 @@ function gen_plot(filepath)
opt = get_operations(g)
for op in opt.nodeFusions
push_operation!(g, op)
props = graph_properties(g)
props = get_properties(g)
push!(x, props.data)
push!(y, props.compute_effort)
push!(y, props.computeEffort)
pop_operation!(g)
push!(
names,
"NF: (" *
string(props.data) *
", " *
string(props.compute_effort) *
")",
)
push!(names, "NF: (" * string(props.data) * ", " * string(props.computeEffort) * ")")
end
for op in opt.nodeReductions
push_operation!(g, op)
props = graph_properties(g)
props = get_properties(g)
push!(x, props.data)
push!(y, props.compute_effort)
push!(y, props.computeEffort)
pop_operation!(g)
push!(
names,
"NR: (" *
string(props.data) *
", " *
string(props.compute_effort) *
")",
)
push!(names, "NR: (" * string(props.data) * ", " * string(props.computeEffort) * ")")
end
for op in opt.nodeSplits
push_operation!(g, op)
props = graph_properties(g)
props = get_properties(g)
push!(x, props.data)
push!(y, props.compute_effort)
push!(y, props.computeEffort)
pop_operation!(g)
push!(
names,
"NS: (" *
string(props.data) *
", " *
string(props.compute_effort) *
")",
)
push!(names, "NS: (" * string(props.data) * ", " * string(props.computeEffort) * ")")
end
plot(
[x0, x[1]],
[y0, y[1]],
linestyle = :solid,
linewidth = 1,
color = :red,
legend = false,
)
plot([x0, x[1]], [y0, y[1]], linestyle = :solid, linewidth = 1, color = :red, legend = false)
# Create lines connecting the reference point to each data point
for i in 2:length(x)
plot!(
[x0, x[i]],
[y0, y[i]],
linestyle = :solid,
linewidth = 1,
color = :red,
)
plot!([x0, x[i]], [y0, y[i]], linestyle = :solid, linewidth = 1, color = :red)
end
#scatter!(x, y, label=names)

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@ -1,36 +0,0 @@
function test_random_walk(g::DAG, n::Int64)
# the purpose here is to do "random" operations and reverse them again and validate that the graph stays the same and doesn't diverge
reset_graph!(g)
properties = graph_properties(g)
for i in 1:n
# choose push or pop
if rand(Bool)
# push
opt = get_operations(g)
# choose one of fuse/split/reduce
option = rand(1:3)
if option == 1 && !isempty(opt.nodeFusions)
push_operation!(g, rand(collect(opt.nodeFusions)))
elseif option == 2 && !isempty(opt.nodeReductions)
push_operation!(g, rand(collect(opt.nodeReductions)))
elseif option == 3 && !isempty(opt.nodeSplits)
push_operation!(g, rand(collect(opt.nodeSplits)))
else
i = i - 1
end
else
# pop
if (can_pop(g))
pop_operation!(g)
else
i = i - 1
end
end
end
return reset_graph!(g)
end

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using MetagraphOptimization
using LIKWID
using CUDA
using UUIDs
function cpu_bench(compute_function, inputs)
compute_function.(inputs[begin:10]) # make sure it's compiled
time = @elapsed Threads.@threads for i in eachindex(inputs)
@invokelatest compute_function(inputs[i])
end
rate = length(inputs) / time
return (time, rate)
end
function gpu_bench(compute_function, inputs)
CUDA.@sync compute_function.(inputs[begin:10]) # make sure it's compiled
time = @elapsed CUDA.@sync compute_function.(inputs)
rate = length(inputs) / time
return (time, rate)
end
function bench_process(
process::MetagraphOptimization.AbstractProcessDescription,
func,
io::IO = stdout;
use_likwid = true,
)
println(io, "\n--- Benchmarking $(process) ---")
NFLOPs = GraphProperties(graph).computeEffort
if use_likwid
input = gen_process_input(process)
func(input) # compile first
_, events = @perfmon "FLOPS_DP" func(input)
NFLOPs = first(events["FLOPS_DP"])["RETIRED_SSE_AVX_FLOPS_ALL"]
end
nInputs = 10000000 # ten million
println(io, "Generating $nInputs inputs with $(Threads.nthreads()) threads...")
inputs = Vector{typeof(gen_process_input(process))}()
resize!(inputs, nInputs)
processes = Vector{typeof(process)}()
for i in 1:Threads.nthreads()
push!(processes, copy(process))
end
Threads.@threads for i in eachindex(inputs)
inputs[i] = gen_process_input(processes[Threads.nthreads()])
end
println(io, "Benchmarking CPU with $(Threads.nthreads()) threads...")
(time_cpu, rate_cpu) = cpu_bench(func, inputs)
flops_cpu = (rate_cpu * NFLOPs) / 1024^3
println(io, "Benchmarking GPU...")
cuInputs = CuArray(inputs)
(time_gpu, rate_gpu) = gpu_bench(func, cuInputs)
flops_gpu = (rate_gpu * NFLOPs) / 1024^3
println(io, "\nBenchmark Summary for $(process):")
if use_likwid
println(io, "Measured FLOPS by LIKWID: $NFLOPs")
else
println(io, "Total graph compute effort: $NFLOPs")
end
println(io, "Total input size: $(bytes_to_human_readable(Base.summarysize(inputs)))")
println(io, "CPU, $(Threads.nthreads()) threads")
println(io, " Time: $time_cpu")
println(io, " Rate: $rate_cpu")
println(io, " GFLOPS: $flops_cpu")
println(io, "GPU, $(name(first(CUDA.devices())))")
println(io, " Time: $time_gpu")
println(io, " Rate: $rate_gpu")
return println(io, " GFLOPS: $flops_gpu")
end
# use "mock" machine that only uses cpu
machine = Machine(
[
MetagraphOptimization.NumaNode(
0,
1,
MetagraphOptimization.default_strategy(MetagraphOptimization.NumaNode),
-1.0,
UUIDs.uuid1(),
),
],
[-1.0;;],
)
optimizer = ReductionOptimizer()
# sadly cannot put these in functions because the world age must increase after the function is created which happens only in the global scope
# compton
process = parse_process("ke->ke", QEDModel())
graph = gen_graph(process)
optimize_to_fixpoint!(optimizer, graph)
compute_func = get_compute_function(graph, process, machine)
bench_process(process, compute_func)
# 2-photon compton
process = parse_process("ke->kke", QEDModel())
graph = gen_graph(process)
optimize_to_fixpoint!(optimizer, graph)
compute_func = get_compute_function(graph, process, machine)
bench_process(process, compute_func)
# 3-photon compton
process = parse_process("ke->kkke", QEDModel())
graph = gen_graph(process)
optimize_to_fixpoint!(optimizer, graph)
compute_func = get_compute_function(graph, process, machine)
bench_process(process, compute_func)
# AB->AB
process = parse_process("AB->AB", ABCModel())
graph = parse_dag("input/AB->AB.txt", ABCModel())
optimize_to_fixpoint!(optimizer, graph)
compute_func = get_compute_function(graph, process, machine)
bench_process(process, compute_func)
# AB->AB^3
process = parse_process("AB->ABBB", ABCModel())
graph = parse_dag("input/AB->ABBB.txt", ABCModel())
optimize_to_fixpoint!(optimizer, graph)
compute_func = get_compute_function(graph, process, machine)
bench_process(process, compute_func)
exit(0)
# 4-photon compton
process = parse_process("ke->kkkke", QEDModel())
graph = gen_graph(process)
optimize_to_fixpoint!(optimizer, graph)
compute_func = get_compute_function(graph, process, machine)
bench_process(process, compute_func)
# AB->AB^5
process = parse_process("AB->ABBBBB", ABCModel())
graph = parse_dag("input/AB->ABBBBB.txt", ABCModel())
optimize_to_fixpoint!(optimizer, graph)
compute_func = get_compute_function(graph, process, machine)
bench_process(process, compute_func)

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@ -0,0 +1,429 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"using MetagraphOptimization"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Get machine and set dictionary caching strategy\n",
"machine = get_machine_info()\n",
"MetagraphOptimization.set_cache_strategy(machine.devices[1], MetagraphOptimization.LocalVariables())"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Graph:\n",
" Nodes: Total: 7854, DataTask: 3931, ComputeTaskABC_S1: 1230, \n",
" ComputeTaskABC_Sum: 1, ComputeTaskABC_U: 8, ComputeTaskABC_P: 8, \n",
" ComputeTaskABC_V: 1956, ComputeTaskABC_S2: 720\n",
" Edges: 11241\n",
" Total Compute Effort: 33915.0\n",
" Total Data Transfer: 322464.0\n",
" Total Compute Intensity: 0.10517453111044954\n"
]
}
],
"source": [
"model = ABCModel()\n",
"process_str = \"AB->ABBBBB\"\n",
"process = parse_process(process_str, model)\n",
"graph = parse_dag(\"../input/$process_str.txt\", model)\n",
"print(graph)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"compute__8bced4be_8f2e_11ee_37d9_3f851690d249 (generic function with 1 method)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"compute_AB_AB5 = get_compute_function(graph, process, machine)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.184484 seconds (2.75 M allocations: 153.561 MiB, 15.46% gc time)\n",
"Graph:\n",
" Nodes: Total: 4998, DataTask: 2503, ComputeTaskABC_S1: 516, \n",
" ComputeTaskABC_Sum: 1, ComputeTaskABC_U: 8, ComputeTaskABC_P: 8, \n",
" ComputeTaskABC_V: 1242, ComputeTaskABC_S2: 720\n",
" Edges: 7671\n",
" Total Compute Effort: 21777.0\n",
" Total Data Transfer: 253920.0\n",
" Total Compute Intensity: 0.0857632325141777\n"
]
}
],
"source": [
"@time optimize_to_fixpoint!(ReductionOptimizer(), graph)\n",
"print(graph)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.822702 seconds (574.85 k allocations: 48.098 MiB, 0.90% gc time)\n"
]
},
{
"data": {
"text/plain": [
"compute__8dffb17a_8f2e_11ee_2d70_13a063f6b2e1 (generic function with 1 method)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"@time compute_AB_AB5_reduced = get_compute_function(graph, process, machine)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.054193 seconds (108.22 k allocations: 6.222 MiB, 92.26% compilation time)\n"
]
},
{
"data": {
"text/plain": [
"1000-element Vector{ABCProcessInput}:\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [5.53824935935883, 0.0, 0.0, 5.447220021849539]\n",
" B: [5.53824935935883, 0.0, 0.0, -5.447220021849539]\n",
" 6 Outgoing Particles:\n",
" A: [-1.3103925957044282, 0.7331872395687581, 0.24174619498761993, 0.34802873993327305]\n",
" B: [-1.7235347423723115, -0.9221216475500805, -0.5368654338299067, 0.9121618174658171]\n",
" B: [-3.2983236636246445, -1.4122494078132704, -0.264394674616116, -2.7954581120438933]\n",
" B: [-1.4663199369248787, -0.21617929792622487, -0.41022326537895987, 0.9669940750145931]\n",
" B: [-1.1596695896410607, 0.40971989086421784, 0.1871290088754596, -0.3767570864705371]\n",
" B: [-2.118258190450336, 1.4076432228565998, 0.7826081699619032, 0.945030566100747]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [6.406766539805908, 0.0, 0.0, 6.328242844232241]\n",
" B: [6.406766539805908, 0.0, 0.0, -6.328242844232241]\n",
" 6 Outgoing Particles:\n",
" A: [-1.6009185206411505, -0.5320720115654639, 1.09590848570997, -0.2807562558330809]\n",
" B: [-3.146359037361951, -0.17028519968266745, 1.7773008494544373, -2.389933018577465]\n",
" B: [-1.010135923448664, 0.06427364329577855, -0.1146419285663243, -0.05568402673627389]\n",
" B: [-3.6289281421436512, 0.6465018878980286, -0.8216898266580996, 3.328059584585744]\n",
" B: [-1.3592677632187082, 0.8038563415980269, -0.35192233894694247, -0.27852199472993183]\n",
" B: [-2.06792369279769, -0.8122746615437029, -1.5849552409930403, -0.323164288708993]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [4.592675400586894, 0.0, 0.0, 4.482484504731276]\n",
" B: [4.592675400586894, 0.0, 0.0, -4.482484504731276]\n",
" 6 Outgoing Particles:\n",
" A: [-1.1473149674649585, -0.35076892712815855, -0.170139004859497, -0.4053955023873595]\n",
" B: [-2.058220554606089, -0.8121547455466859, -1.4272449393744948, 0.7346076529133699]\n",
" B: [-2.0024960896606476, 1.3172479417787402, 0.7582221815549833, -0.8366286944540325]\n",
" B: [-1.0179814720237987, 0.162899519872391, -0.09860388948222289, -0.0052246328160273445]\n",
" B: [-1.834456765054589, -0.0990687609983643, 1.3606293642672649, 0.7100033355854413]\n",
" B: [-1.1248809523637056, -0.2181550279779225, -0.42286371210603335, -0.19736215884139197]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [4.037101162257922, 0.0, 0.0, 3.9112895308714055]\n",
" B: [4.037101162257922, 0.0, 0.0, -3.9112895308714055]\n",
" 6 Outgoing Particles:\n",
" A: [-1.7053110482506162, -0.23947337333507246, -1.2744970749813946, 0.47581034101100217]\n",
" B: [-1.3631569288619594, 0.7221467297219651, 0.42638713494656166, -0.3935669251960867]\n",
" B: [-1.0326521624735496, -0.11131042747240362, 0.20341304874809626, 0.11226579619908084]\n",
" B: [-1.195196392865049, -0.5445059949974184, -0.16637078706558947, 0.32299907142385453]\n",
" B: [-1.1830550739590457, 0.24824882865433953, -0.423307203181585, -0.39850073880304915]\n",
" B: [-1.5948307181056223, -0.07510576257141027, 1.2343748815339113, -0.11900754463480165]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [7.636716907339512, 0.0, 0.0, 7.57096064729207]\n",
" B: [7.636716907339512, 0.0, 0.0, -7.57096064729207]\n",
" 6 Outgoing Particles:\n",
" A: [-1.8228350224036067, -0.22313230508453247, 0.05829362440621317, -1.5064997001932685]\n",
" B: [-2.467409891320565, 1.6506915327402656, -0.771321444516658, 1.3298091083892047]\n",
" B: [-3.7191367050304223, 1.01401048234514, -0.8448690579747132, -3.3301586819963456]\n",
" B: [-1.086062092991359, 0.018065163049532738, 0.4218324659828878, 0.035523096142663795]\n",
" B: [-3.708627500490809, -3.0248517041401413, 1.3840072581447456, 1.2995052961646025]\n",
" B: [-2.4693626024422626, 0.5652168310897357, -0.24794284604247502, 2.171820881493144]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [4.844757462595395, 0.0, 0.0, 4.740429819264681]\n",
" B: [4.844757462595395, 0.0, 0.0, -4.740429819264681]\n",
" 6 Outgoing Particles:\n",
" A: [-1.3377157678137663, -0.44312783214029056, -0.34462836811169034, -0.6887325226333468]\n",
" B: [-1.0287552354600262, 0.10884372468923921, -0.0798214909694111, 0.20029704855940197]\n",
" B: [-1.237602042094568, -0.1707812371296387, -0.708500409075891, -0.02279811352743621]\n",
" B: [-1.2285767946957649, -0.45314793159826366, 0.5376309116329622, -0.12251895938933055]\n",
" B: [-2.3944375695065316, 0.5631279933752329, -1.4234056115727505, 1.5460060162511446]\n",
" B: [-2.4624275156201336, 0.3950852828037212, 2.0187249680967807, -0.9122534692604332]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [6.914095647194839, 0.0, 0.0, 6.841397417089481]\n",
" B: [6.914095647194839, 0.0, 0.0, -6.841397417089481]\n",
" 6 Outgoing Particles:\n",
" A: [-1.8747539146164607, -1.15195487912761, 1.0796978964166692, -0.14817101368775237]\n",
" B: [-2.0219963752169967, -0.8963094934108238, -1.380862038576808, 0.6150761447412909]\n",
" B: [-2.4839643051342004, -0.5463241040770312, 0.28470426735854887, -2.1887329948244236]\n",
" B: [-1.0870998264481033, 0.03306160941873628, 0.20168848226668348, -0.3741854069403313]\n",
" B: [-2.4584897964753116, 0.9082805780526032, -1.8726214974559325, -0.844089567623928]\n",
" B: [-3.9018870764986056, 1.6532462891441266, 1.6873928899908393, 2.9401028383351444]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [4.882838018892802, 0.0, 0.0, 4.77934170349275]\n",
" B: [4.882838018892802, 0.0, 0.0, -4.77934170349275]\n",
" 6 Outgoing Particles:\n",
" A: [-1.3368922715636002, -0.024254114235374817, -0.17993280734873465, 0.8685141729118435]\n",
" B: [-1.336032053759296, 0.44580739433740213, 0.4009862518446777, -0.6522633223307408]\n",
" B: [-1.1917158881102905, 0.11587748600254362, 0.21032579337862262, -0.6020981870524788]\n",
" B: [-1.8590179700604674, -0.4659878149612763, 1.4629321849562218, 0.3140582613697155]\n",
" B: [-1.2740128533657533, -0.3900331968801154, 0.6651639498517544, 0.16893719451393388]\n",
" B: [-2.7680050009261956, 0.3185902457368207, -2.559475372682542, -0.09714811941227354]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [4.215107110349817, 0.0, 0.0, 4.094768363622244]\n",
" B: [4.215107110349817, 0.0, 0.0, -4.094768363622244]\n",
" 6 Outgoing Particles:\n",
" A: [-1.3241447475687065, 0.7510738166043768, -0.3909856211208319, 0.19072933335458914]\n",
" B: [-1.7731907344857587, 0.036019000265901324, 1.4622797510086056, -0.06816114931690141]\n",
" B: [-1.019387957593508, 0.014655316462798782, 0.19300767940790514, -0.04104954903058491]\n",
" B: [-1.6169881803397028, 0.04956396056952302, -1.0323879934365006, -0.7391679242087841]\n",
" B: [-1.6537900060652204, -1.1032956801849205, -0.08849835738509954, 0.7140924778952892]\n",
" B: [-1.0427125946467377, 0.2519835862823207, -0.14341545847407883, -0.056443188693607704]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [7.2720657357811564, 0.0, 0.0, 7.202981331748843]\n",
" B: [7.2720657357811564, 0.0, 0.0, -7.202981331748843]\n",
" 6 Outgoing Particles:\n",
" A: [-1.110939233644008, -0.268184416567738, 0.24360224044987097, 0.3208131044822848]\n",
" B: [-2.6388927199644003, 0.8314814079287018, -0.21777668284358856, 2.2858186218857472]\n",
" B: [-3.473898607870094, 2.051862236379928, 2.4003392500206266, -1.046997796315806]\n",
" B: [-3.152819934613197, -1.9424358511984305, -2.028267056813039, -1.0263280422556738]\n",
" B: [-2.275152937944009, -1.7654922583464505, 0.7703768739716074, -0.6825521583027478]\n",
" B: [-1.8924280375266047, 1.0927688818039885, -1.1682746247854774, 0.14924627050619674]\n",
"\n",
" ⋮\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [6.22966038636724, 0.0, 0.0, 6.148875387375584]\n",
" B: [6.22966038636724, 0.0, 0.0, -6.148875387375584]\n",
" 6 Outgoing Particles:\n",
" A: [-1.4304429070664482, -0.33884344128192095, 0.8653360836289696, -0.42725343187224885]\n",
" B: [-1.9749814666096197, 1.3609392980219706, -0.9441991051819204, -0.39608593805462516]\n",
" B: [-2.2715747343865793, 1.2408591011012648, 1.6172984936557957, 0.06830847338590983]\n",
" B: [-1.661609068228756, -0.4012681871023404, -1.1964016761233542, 0.4105503221395213]\n",
" B: [-1.746963024762814, 1.345279186098992, -0.06451410595930414, 0.48779263162695097]\n",
" B: [-3.373749571680263, -3.2069659568379674, -0.2775196900201868, -0.1433120572255088]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [4.358722688789774, 0.0, 0.0, 4.242459602373458]\n",
" B: [4.358722688789774, 0.0, 0.0, -4.242459602373458]\n",
" 6 Outgoing Particles:\n",
" A: [-1.0452779390743625, -0.2727572224505045, -0.0754336299872278, 0.11188938726967125]\n",
" B: [-1.7048247824379945, 0.4983084694471347, 0.872827621048126, 0.9467249611304639]\n",
" B: [-1.2899467751023526, 0.29644307338358544, -0.46128198344041976, -0.602746313628815]\n",
" B: [-2.1244189851466975, -1.8139000349895653, -0.4266469607437963, -0.20222526648433034]\n",
" B: [-1.4709803178987078, 1.0687795622551313, -0.1466043527374882, 0.0007118353293400601]\n",
" B: [-1.0819965779194327, 0.22312615235421782, 0.23713930586080637, -0.25435460361632983]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [4.946953336826144, 0.0, 0.0, 4.844826861378569]\n",
" B: [4.946953336826144, 0.0, 0.0, -4.844826861378569]\n",
" 6 Outgoing Particles:\n",
" A: [-1.0798321354813016, -0.05701177676898147, 0.3748038410417432, -0.1493625751924078]\n",
" B: [-2.535607459805834, 0.2786802518140389, -2.1413493157456154, 0.8753659894167939]\n",
" B: [-1.1465622434125131, 0.048325266102822936, -0.30303094935893476, 0.46951239643469417]\n",
" B: [-1.0565850692648957, -0.15422821749644713, -0.2946016814579471, -0.0761282786060691]\n",
" B: [-1.3897397103611828, 0.8757386144485694, 0.40183039146109456, 0.054687093694094344]\n",
" B: [-2.6855800553265587, -0.9915041381000028, 1.96234771405966, -1.1740746257471053]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [5.263219273050624, 0.0, 0.0, 5.1673472029864165]\n",
" B: [5.263219273050624, 0.0, 0.0, -5.1673472029864165]\n",
" 6 Outgoing Particles:\n",
" A: [-2.399019535788919, -1.2110047848361276, -1.812263889139395, -0.06679625979229631]\n",
" B: [-2.017935306086244, -0.3374680394916718, 1.6282821358219384, 0.5539634536990483]\n",
" B: [-1.6695031594114513, 0.8270762338660977, -0.06260699981442713, 1.0484589005931164]\n",
" B: [-2.2597097606741916, 0.7611180237287621, 0.18055687193684328, -1.869327893238054]\n",
" B: [-1.073204850363539, -0.22248377596385552, 0.3188604064962904, -0.024447115284049005]\n",
" B: [-1.1070659337769053, 0.18276234269679548, -0.25282852530124955, 0.3581489140222342]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [4.459941032222146, 0.0, 0.0, 4.346386316343583]\n",
" B: [4.459941032222146, 0.0, 0.0, -4.346386316343583]\n",
" 6 Outgoing Particles:\n",
" A: [-1.9579957774892203, 0.01711251988645602, -0.9941971785148113, 1.3583175610150744]\n",
" B: [-2.2086526478827153, 0.26811947256465357, -0.29730202477347406, -1.9281778894844153]\n",
" B: [-1.1393295497986875, -0.09576318262839165, 0.3418914140864091, 0.4147426875441645]\n",
" B: [-1.5437833884502452, -0.2526758526831343, 1.1436052762387854, 0.10765238541055888]\n",
" B: [-1.029324601398587, -0.04086809209820055, -0.11666716588470447, -0.21030384327692128]\n",
" B: [-1.040796099424839, 0.10407513495861721, -0.07733032115220424, 0.25776909879153836]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [5.6127229037846575, 0.0, 0.0, 5.522921183094041]\n",
" B: [5.6127229037846575, 0.0, 0.0, -5.522921183094041]\n",
" 6 Outgoing Particles:\n",
" A: [-1.3401191006255044, 0.07455340773270878, 0.8329539127008466, 0.3107229836576332]\n",
" B: [-2.2407608326391446, 1.9616328357565815, 0.2748188274329855, 0.3122184153114968]\n",
" B: [-1.9353505325144305, 0.5041718248979296, 0.4986811623094062, -1.4975678792765024]\n",
" B: [-1.1665291383852119, -0.5919830552573446, -0.0003589073718047799, 0.10171609595055851]\n",
" B: [-1.3532183234755, -0.2764818233423043, 0.8493370095656062, 0.18271364627008788]\n",
" B: [-3.1894678799295257, -1.671893189787572, -2.45543200463704, 0.5901967380867258]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [4.8915558702989275, 0.0, 0.0, 4.788247991933574]\n",
" B: [4.8915558702989275, 0.0, 0.0, -4.788247991933574]\n",
" 6 Outgoing Particles:\n",
" A: [-1.7166600698631052, -0.6792891539923208, 0.6748994636717233, 1.0148885429772172]\n",
" B: [-2.5106233942424825, -0.7525848308448442, -1.9630692909736174, 0.9397897950798489]\n",
" B: [-1.0591214238384126, 0.22224342472975844, 0.26723772059994233, -0.030496742226701214]\n",
" B: [-2.107615205886531, 1.2019506202258687, 1.111787687227206, -0.8725163042331971]\n",
" B: [-1.1276654384352531, 0.3419112314983172, -0.15371273194576066, -0.3620751950278375]\n",
" B: [-1.2614262083320695, -0.33423129161677956, 0.06285715142050609, -0.689590096569332]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [7.730105975946025, 0.0, 0.0, 7.665150905191394]\n",
" B: [7.730105975946025, 0.0, 0.0, -7.665150905191394]\n",
" 6 Outgoing Particles:\n",
" A: [-1.5069861693238755, -0.14569717271308374, -1.0624243147247645, 0.3478997325070473]\n",
" B: [-1.3943234172777221, -0.04432112759455558, 0.08353004942916775, 0.9670554071303941]\n",
" B: [-2.959534510858716, -2.3414048211285614, 1.2349523309699664, 0.8669260203682391]\n",
" B: [-3.9504084752062516, -1.3395798731389539, -0.8585843373250325, -3.4747785282176675]\n",
" B: [-3.4956434330579116, 2.5236614743308494, -0.431975773525167, 2.1596418001942994]\n",
" B: [-2.153315946167574, 1.3473415202443053, 1.0345020451758309, -0.8667444319823133]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [5.140973354732315, 0.0, 0.0, 5.042777710158126]\n",
" B: [5.140973354732315, 0.0, 0.0, -5.042777710158126]\n",
" 6 Outgoing Particles:\n",
" A: [-2.1212395395513415, 0.5721186152245487, -1.464674439391297, 1.013442776314144]\n",
" B: [-1.4152359585953729, 0.6568206137784666, 0.5137348552056548, -0.5545773150462135]\n",
" B: [-1.6621060291271548, -0.07490000906447869, -1.013680695206552, 0.8540713605247167]\n",
" B: [-1.602034710373159, -1.201656230753467, -0.11487974312683813, 0.3306662379967043]\n",
" B: [-1.6826459861655199, -0.324056691191041, 0.7444127790391002, -1.082651555236741]\n",
" B: [-1.7986844856520843, 0.3716737020059716, 1.3350872434799315, -0.5609515045526104]\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"@time inputs = [gen_process_input(process) for _ in 1:1000]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BenchmarkTools.Trial: 231 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m18.197 ms\u001b[22m\u001b[39m … \u001b[35m27.498 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 8.36%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m21.868 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m0.00%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m21.644 ms\u001b[22m\u001b[39m ± \u001b[32m 1.609 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m1.21% ± 2.71%\n",
"\n",
" \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[32m \u001b[39m\u001b[39m▃\u001b[34m█\u001b[39m\u001b[39m▁\u001b[39m \u001b[39m▅\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
" \u001b[39m▃\u001b[39m▃\u001b[39m▁\u001b[39m▅\u001b[39m▇\u001b[39m▃\u001b[39m▅\u001b[39m▅\u001b[39m▅\u001b[39m▃\u001b[39m▄\u001b[39m▃\u001b[39m▃\u001b[39m▅\u001b[39m▄\u001b[39m▅\u001b[39m▃\u001b[39m▅\u001b[39m▄\u001b[39m▃\u001b[39m▅\u001b[39m▄\u001b[39m▃\u001b[39m▅\u001b[39m▇\u001b[39m▅\u001b[39m▅\u001b[32m▆\u001b[39m\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m▆\u001b[39m▇\u001b[39m▄\u001b[39m▅\u001b[39m▄\u001b[39m▅\u001b[39m▅\u001b[39m▄\u001b[39m▃\u001b[39m▅\u001b[39m▃\u001b[39m▁\u001b[39m▁\u001b[39m▃\u001b[39m▄\u001b[39m▁\u001b[39m▄\u001b[39m▁\u001b[39m▃\u001b[39m▃\u001b[39m▁\u001b[39m▃\u001b[39m▁\u001b[39m▁\u001b[39m▃\u001b[39m \u001b[39m▃\n",
" 18.2 ms\u001b[90m Histogram: frequency by time\u001b[39m 25.6 ms \u001b[0m\u001b[1m<\u001b[22m\n",
"\n",
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m6.78 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m17003\u001b[39m."
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"using BenchmarkTools\n",
"#compute_bench = @benchmark compute_AB_AB5.(inputs)\n",
"compute_bench_reduced = @benchmark compute_AB_AB5_reduced.(inputs)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.9.4",
"language": "julia",
"name": "julia-1.9"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.9.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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@ -0,0 +1,407 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "20768e45-df62-4638-ba33-b0ccf239f1aa",
"metadata": {},
"outputs": [],
"source": [
"using Revise\n",
"using MetagraphOptimization\n",
"using BenchmarkTools"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ff5f4a49",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1 NUMA nodes\n",
"CUDA is non-functional\n"
]
},
{
"data": {
"text/plain": [
"Machine(MetagraphOptimization.AbstractDevice[MetagraphOptimization.NumaNode(0x0000, 0x0001, MetagraphOptimization.LocalVariables(), -1.0, UUID(\"a89974f6-6212-11ee-0866-0f591a3b69ea\"))], [-1.0;;])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get our machine's info\n",
"machine = get_machine_info()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9df482a4-ca44-44c5-9ea7-7a2977d529be",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ABCModel()"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a model identifier\n",
"model = ABCModel()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "30b16872-07f7-4d47-8ff8-8c3a849c9d4e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ABC Process: 'AB->ABBB'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a process in our model\n",
"process_str = \"AB->ABBB\"\n",
"process = parse_process(process_str, model)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "043bd9e2-f89a-4362-885a-8c89d4cdd76f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total: 280, ComputeTaskABC_P"
]
},
{
"data": {
"text/plain": [
"Graph:\n",
" Nodes: \n",
" Edges: 385\n",
" Total Compute Effort: 1075.0\n",
" Total Data Transfer: 10944.0\n",
" Total Compute Intensity: 0.09822733918128655\n"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
": 6, ComputeTaskABC_U: 6, \n",
" ComputeTaskABC_V: 64, ComputeTaskABC_Sum: 1, ComputeTaskABC_S2: 24, \n",
" ComputeTaskABC_S1: 36, DataTask: 143"
]
}
],
"source": [
"# Read the graph (of the same process) from a file\n",
"graph = parse_dag(\"../input/$process_str.txt\", model)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "02f01ad3-fd10-48d5-a0e0-c03dc83c80a4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Input for ABC Process: 'AB->ABBB':\n",
" 2 Incoming particles:\n",
" A: [5.77986599979293, 0.0, 0.0, 5.692701553354288]\n",
" B: [5.77986599979293, 0.0, 0.0, -5.692701553354288]\n",
" 4 Outgoing Particles:\n",
" A: [-3.8835293143673746, -1.4292027910861678, 2.8576090179942106, 1.968057422378813]\n",
" B: [-1.1554024905063585, -0.1464656500147254, -0.2082400426692148, 0.5197487980391896]\n",
" B: [-2.849749730594798, -1.0177034035100576, -2.464951858896686, -0.09677625137882176]\n",
" B: [-3.6710504641173287, 2.5933718446109513, -0.1844171164283155, -2.391029969039186]\n"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Generate some random input data for our process\n",
"input_data = gen_process_input(process)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "083fb1be-ce2a-47f9-afb9-60a6fdfaed0b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"compute__af4450a2_6212_11ee_2601_cde7cf2aedc1 (generic function with 1 method)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get the function computing the result of the process from a ProcessInput\n",
"AB_AB3_compute = get_compute_function(graph, process, machine)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "a40c9500-8f79-4f04-b3c5-59b72a6b7ba9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-1.8924431710735022e-13"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Actually compute a result using the generated function and the input data\n",
"result = AB_AB3_compute(input_data)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "80c70010",
"metadata": {},
"outputs": [],
"source": [
"# We can also mute the graph by applying some operations to it\n",
"optimize_to_fixpoint!(ReductionOptimizer(), graph)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "5b192b44",
"metadata": {},
"outputs": [],
"source": [
"# The result should be the same as before (we can use execute to save having to generate the function ourselves)\n",
"@assert result ≈ execute(graph, process, machine, input_data)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "9b2f4a3f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1000-element Vector{Float64}:\n",
" -2.1491995259940396e-11\n",
" -1.04995646459455e-11\n",
" 5.821760691187782e-15\n",
" -6.556969485683705e-14\n",
" -1.3588086164373753e-14\n",
" -1.8789662441593694e-13\n",
" -2.131973301835892e-13\n",
" -5.3359759072004825e-12\n",
" -9.053914191490223e-13\n",
" -5.61107901706923e-13\n",
" -5.063492275603428e-11\n",
" 2.9168508985811397e-15\n",
" -1.6420151378194157e-13\n",
" ⋮\n",
" 1.0931677247833436e-13\n",
" -7.704755306462797e-16\n",
" -1.8385907037491397e-12\n",
" -6.036215596560059e-14\n",
" -9.98872401400362e-12\n",
" 3.4861755637292935e-13\n",
" -1.1051119822969222e-10\n",
" -2.496572513216201e-12\n",
" -3.8682427847201926e-11\n",
" 7.904149696653438e-15\n",
" -7.606811743178716e-11\n",
" -5.100594937480292e-13"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now we can generate a function and use it on lots of inputs\n",
"inputs = [gen_process_input(process) for _ in 1:1000]\n",
"AB_AB3_reduced_compute = get_compute_function(graph, process, machine)\n",
"\n",
"results = AB_AB3_reduced_compute.(inputs)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d43e4ff0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BenchmarkTools.Trial: 879 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m4.567 ms\u001b[22m\u001b[39m … \u001b[35m14.334 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 54.51%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m4.998 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m0.00%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m5.686 ms\u001b[22m\u001b[39m ± \u001b[32m 1.414 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m9.09% ± 14.49%\n",
"\n",
" \u001b[39m \u001b[39m \u001b[39m▃\u001b[39m▇\u001b[39m█\u001b[34m▅\u001b[39m\u001b[39m▄\u001b[39m▁\u001b[39m \u001b[39m▁\u001b[39m \u001b[39m \u001b[32m \u001b[39m\u001b[39m \u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\u001b[39m▁\u001b[39m \u001b[39m▁\u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
" \u001b[39m▆\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m█\u001b[39m▇\u001b[39m▇\u001b[32m█\u001b[39m\u001b[39m▆\u001b[39m█\u001b[39m█\u001b[39m▆\u001b[39m▆\u001b[39m▇\u001b[39m▅\u001b[39m▅\u001b[39m▄\u001b[39m▁\u001b[39m▄\u001b[39m▅\u001b[39m▅\u001b[39m▆\u001b[39m▅\u001b[39m▅\u001b[39m▄\u001b[39m▁\u001b[39m▄\u001b[39m▄\u001b[39m▁\u001b[39m▅\u001b[39m▄\u001b[39m▄\u001b[39m▆\u001b[39m▇\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▄\u001b[39m▅\u001b[39m▆\u001b[39m▅\u001b[39m▅\u001b[39m▅\u001b[39m▁\u001b[39m▅\u001b[39m▄\u001b[39m▄\u001b[39m▅\u001b[39m▁\u001b[39m▄\u001b[39m \u001b[39m▇\n",
" 4.57 ms\u001b[90m \u001b[39m\u001b[90mHistogram: \u001b[39m\u001b[90m\u001b[1mlog(\u001b[22m\u001b[39m\u001b[90mfrequency\u001b[39m\u001b[90m\u001b[1m)\u001b[22m\u001b[39m\u001b[90m by time\u001b[39m 10 ms \u001b[0m\u001b[1m<\u001b[22m\n",
"\n",
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m6.17 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m143006\u001b[39m."
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@benchmark results = AB_AB3_compute.($inputs)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "e18d9546",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BenchmarkTools.Trial: 1089 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m3.637 ms\u001b[22m\u001b[39m … \u001b[35m10.921 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m 0.00% … 59.52%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m4.098 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m 0.00%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m4.587 ms\u001b[22m\u001b[39m ± \u001b[32m 1.334 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m10.21% ± 15.77%\n",
"\n",
" \u001b[39m \u001b[39m▂\u001b[39m▆\u001b[39m▆\u001b[39m▇\u001b[34m█\u001b[39m\u001b[39m▆\u001b[39m▂\u001b[39m \u001b[39m \u001b[39m \u001b[32m \u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\u001b[39m▁\u001b[39m \u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
" \u001b[39m▆\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m█\u001b[39m▇\u001b[32m▆\u001b[39m\u001b[39m▅\u001b[39m▇\u001b[39m▅\u001b[39m▅\u001b[39m▅\u001b[39m▄\u001b[39m▆\u001b[39m▄\u001b[39m▅\u001b[39m▅\u001b[39m▅\u001b[39m▅\u001b[39m▆\u001b[39m▄\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▄\u001b[39m▆\u001b[39m▆\u001b[39m▆\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m█\u001b[39m█\u001b[39m▆\u001b[39m▆\u001b[39m▆\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m▆\u001b[39m▄\u001b[39m▄\u001b[39m \u001b[39m█\n",
" 3.64 ms\u001b[90m \u001b[39m\u001b[90mHistogram: \u001b[39m\u001b[90m\u001b[1mlog(\u001b[22m\u001b[39m\u001b[90mfrequency\u001b[39m\u001b[90m\u001b[1m)\u001b[22m\u001b[39m\u001b[90m by time\u001b[39m 8.78 ms \u001b[0m\u001b[1m<\u001b[22m\n",
"\n",
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m5.26 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m123006\u001b[39m."
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@benchmark results = AB_AB3_reduced_compute.($inputs)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "13efed12-3547-400b-a7a2-5dfae9a973a2",
"metadata": {},
"outputs": [],
"source": [
"# Set a different caching strategy\n",
"MetagraphOptimization.set_cache_strategy(machine.devices[1], MetagraphOptimization.Dictionary())"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "ef62716b-a219-4f6e-9150-f984d3734839",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BenchmarkTools.Trial: 331 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m12.148 ms\u001b[22m\u001b[39m … \u001b[35m24.164 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m 0.00% … 13.35%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m15.412 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m17.47%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m15.117 ms\u001b[22m\u001b[39m ± \u001b[32m 2.194 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m12.31% ± 8.95%\n",
"\n",
" \u001b[39m \u001b[39m▄\u001b[39m█\u001b[39m▄\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[32m▄\u001b[39m\u001b[39m▄\u001b[34m▂\u001b[39m\u001b[39m \u001b[39m▂\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
" \u001b[39m▅\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▅\u001b[39m▃\u001b[39m▃\u001b[39m▂\u001b[39m▃\u001b[39m▂\u001b[39m▅\u001b[39m▂\u001b[39m▃\u001b[39m▁\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▃\u001b[39m▂\u001b[39m▃\u001b[32m█\u001b[39m\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m▇\u001b[39m█\u001b[39m▄\u001b[39m▆\u001b[39m▄\u001b[39m▆\u001b[39m▄\u001b[39m▄\u001b[39m▆\u001b[39m▅\u001b[39m▄\u001b[39m▃\u001b[39m▄\u001b[39m▂\u001b[39m▂\u001b[39m▃\u001b[39m▃\u001b[39m▄\u001b[39m▃\u001b[39m▂\u001b[39m▂\u001b[39m▁\u001b[39m▂\u001b[39m▂\u001b[39m▃\u001b[39m▂\u001b[39m▂\u001b[39m▁\u001b[39m▂\u001b[39m▁\u001b[39m▃\u001b[39m▃\u001b[39m▂\u001b[39m▂\u001b[39m▁\u001b[39m▂\u001b[39m \u001b[39m▃\n",
" 12.1 ms\u001b[90m Histogram: frequency by time\u001b[39m 21 ms \u001b[0m\u001b[1m<\u001b[22m\n",
"\n",
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m27.46 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m118013\u001b[39m."
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ... and bench again\n",
"AB_AB3_reduced_dict_compute = get_compute_function(graph, process, machine)\n",
"@benchmark results = AB_AB3_reduced_dict_compute.($inputs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5461ffd4-6a0e-4f1f-b1f1-3a2854a8ae88",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.9.4",
"language": "julia",
"name": "julia-1.9"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.9.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"using Revise; using QEDbase; using QEDprocesses; using MetagraphOptimization; using BenchmarkTools; using DataStructures\n",
"import MetagraphOptimization.gen_diagrams"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Diagram 1: Initial Particles: [k_i_1, e_i_1, k_o_1, e_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_i_1 -> e_i_2, k_o_1 + e_o_1 -> e_o_2]\n",
" Tie: e_i_2 -- e_o_2\n",
"\n",
"Diagram 2: Initial Particles: [k_i_1, e_i_1, k_o_1, e_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_o_1 -> e_o_2, e_i_1 + k_o_1 -> e_i_2]\n",
" Tie: e_o_2 -- e_i_2\n",
"\n"
]
}
],
"source": [
"# Compton Scattering\n",
"fd = FeynmanDiagram(parse_process(\"ke->ke\", QEDModel()))\n",
"\n",
"diagrams = gen_diagrams(fd)\n",
"\n",
"c = 1\n",
"for d in diagrams\n",
" println(\"Diagram $c: $d\")\n",
" c += 1\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BenchmarkTools.Trial: 6044 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m490.857 μs\u001b[22m\u001b[39m … \u001b[35m 3.657 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 77.38%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m800.314 μs \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m0.00%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m825.263 μs\u001b[22m\u001b[39m ± \u001b[32m208.306 μs\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m1.62% ± 5.53%\n",
"\n",
" \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▃\u001b[39m█\u001b[39m▂\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\u001b[39m \u001b[39m▂\u001b[39m▃\u001b[39m▃\u001b[39m▂\u001b[39m▃\u001b[39m▃\u001b[39m▄\u001b[39m▅\u001b[34m▅\u001b[39m\u001b[39m▅\u001b[39m▃\u001b[32m▂\u001b[39m\u001b[39m▁\u001b[39m \u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▃\u001b[39m▆\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
" \u001b[39m▂\u001b[39m▂\u001b[39m▁\u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▂\u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▃\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[32m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m▆\u001b[39m▆\u001b[39m▅\u001b[39m▅\u001b[39m▄\u001b[39m▄\u001b[39m▄\u001b[39m▅\u001b[39m▇\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▅\u001b[39m▄\u001b[39m▃\u001b[39m \u001b[39m▅\n",
" 491 μs\u001b[90m Histogram: frequency by time\u001b[39m 1.04 ms \u001b[0m\u001b[1m<\u001b[22m\n",
"\n",
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m280.03 KiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m2709\u001b[39m."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 6 Diagrams for 2-Photon Compton\n",
"Diagram 1: Initial Particles: [k_i_1, k_i_2, e_i_1, k_o_1, e_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_i_1 -> e_i_2, k_i_2 + e_o_1 -> e_o_2]\n",
" Virtuality Level 2 Vertices: [k_o_1 + e_i_2 -> e_i_3]\n",
" Tie: e_o_2 -- e_i_3\n",
"\n"
]
}
],
"source": [
"# 2-Photon Compton Scattering\n",
"two_k_compton = FeynmanDiagram(parse_process(\"kke->ke\", QEDModel()))\n",
"\n",
"display(@benchmark gen_diagrams(two_k_compton))\n",
"diagrams = gen_diagrams(two_k_compton)\n",
"\n",
"println(\"Found $(length(diagrams)) Diagrams for 2-Photon Compton\")\n",
"println(\"Diagram 1: $(first(diagrams))\")"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BenchmarkTools.Trial: 1167 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m2.581 ms\u001b[22m\u001b[39m … \u001b[35m 7.394 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 38.39%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m4.278 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m0.00%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m4.284 ms\u001b[22m\u001b[39m ± \u001b[32m550.104 μs\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m1.84% ± 6.28%\n",
"\n",
" \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▃\u001b[39m▃\u001b[39m▅\u001b[39m▅\u001b[34m▃\u001b[39m\u001b[39m▃\u001b[39m▇\u001b[39m█\u001b[39m▄\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
" \u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▂\u001b[39m▂\u001b[39m▁\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▄\u001b[39m█\u001b[39m▄\u001b[39m▄\u001b[39m▄\u001b[39m▃\u001b[39m▃\u001b[39m▄\u001b[39m▆\u001b[39m▇\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▆\u001b[39m▄\u001b[39m▃\u001b[39m▃\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▂\u001b[39m▃\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m \u001b[39m▃\n",
" 2.58 ms\u001b[90m Histogram: frequency by time\u001b[39m 6.46 ms \u001b[0m\u001b[1m<\u001b[22m\n",
"\n",
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m1.71 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m15410\u001b[39m."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 24 Diagrams for 3-Photon Compton\n",
"Diagram 1: Initial Particles: [k_i_1, k_i_2, k_i_3, e_i_1, k_o_1, e_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_2 + e_o_1 -> e_o_2, k_i_3 + e_i_1 -> e_i_2]\n",
" Virtuality Level 2 Vertices: [k_i_1 + e_o_2 -> e_o_3, k_o_1 + e_i_2 -> e_i_3]\n",
" Tie: e_o_3 -- e_i_3\n",
"\n"
]
}
],
"source": [
"# 3-Photon Compton Scattering\n",
"three_k_compton = FeynmanDiagram(parse_process(\"kkke->ke\", QEDModel()))\n",
"\n",
"display(@benchmark gen_diagrams(three_k_compton))\n",
"diagrams = gen_diagrams(three_k_compton)\n",
"\n",
"println(\"Found $(length(diagrams)) Diagrams for 3-Photon Compton\")\n",
"println(\"Diagram 1: $(first(diagrams))\")"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BenchmarkTools.Trial: 141 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m31.255 ms\u001b[22m\u001b[39m … \u001b[35m42.658 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 4.92%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m35.749 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m4.34%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m35.690 ms\u001b[22m\u001b[39m ± \u001b[32m 2.009 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m3.04% ± 2.83%\n",
"\n",
" \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▆\u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▃\u001b[39m▁\u001b[39m▁\u001b[39m \u001b[39m \u001b[39m▁\u001b[39m \u001b[39m▃\u001b[39m▁\u001b[39m▃\u001b[39m▁\u001b[39m \u001b[39m█\u001b[34m▆\u001b[39m\u001b[39m▁\u001b[39m▁\u001b[39m▆\u001b[39m▁\u001b[39m▁\u001b[39m▃\u001b[39m \u001b[39m▁\u001b[39m \u001b[39m▃\u001b[39m▆\u001b[39m▁\u001b[39m▆\u001b[39m█\u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
" \u001b[39m▇\u001b[39m▄\u001b[39m▄\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▄\u001b[39m▇\u001b[39m▇\u001b[39m▄\u001b[39m▄\u001b[39m▄\u001b[39m▇\u001b[39m▄\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m▄\u001b[39m▇\u001b[39m▇\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m▁\u001b[39m█\u001b[39m▄\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▄\u001b[39m█\u001b[39m▇\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▇\u001b[39m▇\u001b[39m▁\u001b[39m█\u001b[39m▄\u001b[39m▁\u001b[39m▄\u001b[39m▇\u001b[39m█\u001b[39m▇\u001b[39m▄\u001b[39m \u001b[39m▄\n",
" 31.3 ms\u001b[90m Histogram: frequency by time\u001b[39m 39.2 ms \u001b[0m\u001b[1m<\u001b[22m\n",
"\n",
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m23.29 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m171048\u001b[39m."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 120 Diagrams for 4-Photon Compton\n",
"Diagram 1: Initial Particles: [k_i_1, k_i_2, k_i_3, k_i_4, e_i_1, k_o_1, e_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_o_1 -> e_o_2, e_i_1 + k_o_1 -> e_i_2]\n",
" Virtuality Level 2 Vertices: [k_i_3 + e_o_2 -> e_o_3, k_i_2 + e_i_2 -> e_i_3]\n",
" Virtuality Level 3 Vertices: [k_i_4 + e_o_3 -> e_o_4]\n",
" Tie: e_i_3 -- e_o_4\n",
"\n"
]
}
],
"source": [
"# 4-Photon Compton Scattering\n",
"four_k_compton = FeynmanDiagram(parse_process(\"kkkke->ke\", QEDModel()))\n",
"\n",
"display(@benchmark gen_diagrams(four_k_compton))\n",
"diagrams = gen_diagrams(four_k_compton)\n",
"\n",
"println(\"Found $(length(diagrams)) Diagrams for 4-Photon Compton\")\n",
"println(\"Diagram 1: $(first(diagrams))\")"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BenchmarkTools.Trial: 10 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m471.789 ms\u001b[22m\u001b[39m … \u001b[35m527.196 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m6.00% … 7.35%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m499.068 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m6.98%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m502.132 ms\u001b[22m\u001b[39m ± \u001b[32m 17.383 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m6.79% ± 0.77%\n",
"\n",
" \u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m█\u001b[39m▁\u001b[39m \u001b[34m▁\u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[32m \u001b[39m\u001b[39m \u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\u001b[39m▁\u001b[39m \u001b[39m \n",
" \u001b[39m█\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m█\u001b[39m█\u001b[39m▁\u001b[34m█\u001b[39m\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[32m▁\u001b[39m\u001b[39m▁\u001b[39m█\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m█\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m█\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m█\u001b[39m█\u001b[39m \u001b[39m▁\n",
" 472 ms\u001b[90m Histogram: frequency by time\u001b[39m 527 ms \u001b[0m\u001b[1m<\u001b[22m\n",
"\n",
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m627.12 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m3747679\u001b[39m."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 720 Diagrams for 5-Photon Compton\n",
"Diagram 1: Initial Particles: [k_i_1, k_i_2, k_i_3, k_i_4, k_i_5, e_i_1, k_o_1, e_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_i_1 -> e_i_2, k_i_4 + e_o_1 -> e_o_2]\n",
" Virtuality Level 2 Vertices: [k_i_3 + e_i_2 -> e_i_3, k_i_5 + e_o_2 -> e_o_3]\n",
" Virtuality Level 3 Vertices: [k_i_2 + e_i_3 -> e_i_4, k_o_1 + e_o_3 -> e_o_4]\n",
" Tie: e_i_4 -- e_o_4\n",
"\n"
]
}
],
"source": [
"# 5-Photon Compton Scattering\n",
"five_k_compton = FeynmanDiagram(parse_process(\"kkkkke->ke\", QEDModel()))\n",
"\n",
"display(@benchmark gen_diagrams(five_k_compton))\n",
"diagrams = gen_diagrams(five_k_compton)\n",
"\n",
"println(\"Found $(length(diagrams)) Diagrams for 5-Photon Compton\")\n",
"println(\"Diagram 1: $(first(diagrams))\")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Diagram 1: Initial Particles: [p_i_1, e_i_1, e_o_1, p_o_1]\n",
" Virtuality Level 1 Vertices: [p_i_1 + e_i_1 -> k_o_2, e_o_1 + p_o_1 -> k_o_1]\n",
" Tie: k_o_2 -- k_o_1\n",
"\n",
"Diagram 2: Initial Particles: [p_i_1, e_i_1, e_o_1, p_o_1]\n",
" Virtuality Level 1 Vertices: [p_i_1 + p_o_1 -> k_o_1, e_i_1 + e_o_1 -> k_o_2]\n",
" Tie: k_o_1 -- k_o_2\n",
"\n"
]
}
],
"source": [
"# Bhabha Scattering\n",
"fd = FeynmanDiagram(parse_process(\"ep->ep\", QEDModel()))\n",
"\n",
"diagrams = gen_diagrams(fd)\n",
"\n",
"c = 1\n",
"for d in diagrams\n",
" println(\"Diagram $c: $d\")\n",
" c += 1\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Diagram 1: Initial Particles: [e_i_1, e_i_2, e_o_1, e_o_2]\n",
" Virtuality Level 1 Vertices: [e_i_2 + e_o_2 -> k_o_2, e_i_1 + e_o_1 -> k_o_1]\n",
" Tie: k_o_2 -- k_o_1\n",
"\n",
"Diagram 2: Initial Particles: [e_i_1, e_i_2, e_o_1, e_o_2]\n",
" Virtuality Level 1 Vertices: [e_i_1 + e_o_2 -> k_o_1, e_i_2 + e_o_1 -> k_o_2]\n",
" Tie: k_o_1 -- k_o_2\n",
"\n"
]
}
],
"source": [
"# Moller Scattering\n",
"fd = FeynmanDiagram(parse_process(\"ee->ee\", QEDModel()))\n",
"\n",
"diagrams = gen_diagrams(fd)\n",
"\n",
"c = 1\n",
"for d in diagrams\n",
" println(\"Diagram $c: $d\")\n",
" c += 1\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Diagram 1: Initial Particles: [p_i_1, e_i_1, k_o_1, k_o_2]\n",
" Virtuality Level 1 Vertices: [e_i_1 + k_o_2 -> e_i_2, p_i_1 + k_o_1 -> e_o_1]\n",
" Tie: e_i_2 -- e_o_1\n",
"\n",
"Diagram 2: Initial Particles: [p_i_1, e_i_1, k_o_1, k_o_2]\n",
" Virtuality Level 1 Vertices: [e_i_1 + k_o_1 -> e_i_2, p_i_1 + k_o_2 -> e_o_1]\n",
" Tie: e_i_2 -- e_o_1\n",
"\n"
]
}
],
"source": [
"# Pair annihilation\n",
"fd = FeynmanDiagram(parse_process(\"ep->kk\", QEDModel()))\n",
"\n",
"diagrams = gen_diagrams(fd)\n",
"\n",
"c = 1\n",
"for d in diagrams\n",
" println(\"Diagram $c: $d\")\n",
" c += 1\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Diagram 1: Initial Particles: [k_i_1, k_i_2, e_o_1, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + p_o_1 -> e_i_1, k_i_2 + e_o_1 -> e_o_2]\n",
" Tie: e_i_1 -- e_o_2\n",
"\n",
"Diagram 2: Initial Particles: [k_i_1, k_i_2, e_o_1, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_o_1 -> e_o_2, k_i_2 + p_o_1 -> e_i_1]\n",
" Tie: e_o_2 -- e_i_1\n",
"\n"
]
}
],
"source": [
"# Pair production\n",
"fd = FeynmanDiagram(parse_process(\"kk->pe\", QEDModel()))\n",
"\n",
"diagrams = gen_diagrams(fd)\n",
"\n",
"c = 1\n",
"for d in diagrams\n",
" println(\"Diagram $c: $d\")\n",
" c += 1\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 8 diagrams:\n",
"Diagram 1: Initial Particles: [k_i_1, e_i_1, e_o_1, e_o_2, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_o_1 -> e_o_3, e_i_1 + e_o_2 -> k_o_1]\n",
" Virtuality Level 2 Vertices: [p_o_1 + k_o_1 -> e_i_2]\n",
" Tie: e_o_3 -- e_i_2\n",
"\n",
"Diagram 2: Initial Particles: [k_i_1, e_i_1, e_o_1, e_o_2, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + p_o_1 -> e_i_2, e_i_1 + e_o_2 -> k_o_1]\n",
" Virtuality Level 2 Vertices: [e_o_1 + e_i_2 -> k_o_2]\n",
" Tie: k_o_1 -- k_o_2\n",
"\n",
"Diagram 3: Initial Particles: [k_i_1, e_i_1, e_o_1, e_o_2, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_o_2 -> e_o_3, e_i_1 + e_o_1 -> k_o_1]\n",
" Virtuality Level 2 Vertices: [p_o_1 + e_o_3 -> k_o_2]\n",
" Tie: k_o_1 -- k_o_2\n",
"\n",
"Diagram 4: Initial Particles: [k_i_1, e_i_1, e_o_1, e_o_2, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_i_1 -> e_i_2, e_o_2 + p_o_1 -> k_o_1]\n",
" Virtuality Level 2 Vertices: [e_o_1 + e_i_2 -> k_o_2]\n",
" Tie: k_o_1 -- k_o_2\n",
"\n",
"Diagram 5: Initial Particles: [k_i_1, e_i_1, e_o_1, e_o_2, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_o_1 -> e_o_3, e_o_2 + p_o_1 -> k_o_1]\n",
" Virtuality Level 2 Vertices: [e_i_1 + k_o_1 -> e_i_2]\n",
" Tie: e_o_3 -- e_i_2\n",
"\n",
"Diagram 6: Initial Particles: [k_i_1, e_i_1, e_o_1, e_o_2, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_o_2 -> e_o_3, e_o_1 + p_o_1 -> k_o_1]\n",
" Virtuality Level 2 Vertices: [e_i_1 + e_o_3 -> k_o_2]\n",
" Tie: k_o_1 -- k_o_2\n",
"\n",
"Diagram 7: Initial Particles: [k_i_1, e_i_1, e_o_1, e_o_2, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + p_o_1 -> e_i_2, e_i_1 + e_o_1 -> k_o_1]\n",
" Virtuality Level 2 Vertices: [e_o_2 + k_o_1 -> e_o_3]\n",
" Tie: e_i_2 -- e_o_3\n",
"\n",
"Diagram 8: Initial Particles: [k_i_1, e_i_1, e_o_1, e_o_2, p_o_1]\n",
" Virtuality Level 1 Vertices: [k_i_1 + e_i_1 -> e_i_2, e_o_1 + p_o_1 -> k_o_1]\n",
" Virtuality Level 2 Vertices: [e_o_2 + k_o_1 -> e_o_3]\n",
" Tie: e_i_2 -- e_o_3\n",
"\n"
]
}
],
"source": [
"# Trident\n",
"fd = FeynmanDiagram(parse_process(\"ke->epe\", QEDModel()))\n",
"\n",
"diagrams = gen_diagrams(fd)\n",
"\n",
"println(\"Found $(length(diagrams)) diagrams:\")\n",
"c = 1\n",
"for d in diagrams\n",
" println(\"Diagram $c: $d\")\n",
" c += 1\n",
"end"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.9.4",
"language": "julia",
"name": "julia-1.9"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.9.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

111
notebooks/diagram_gen_profiling.ipynb Normal file
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@ -0,0 +1,111 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "595a07c5-0ecc-4f3e-8cbe-63fc64b456da",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling MetagraphOptimization [3e869610-d48d-4942-ba70-c1b702a33ca4]\n"
]
},
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using BenchmarkTools; using Profile; using PProf; using Revise; using MetagraphOptimization;\n",
"Threads.nthreads()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "163f84be-1e2e-480e-9944-1fa4e0eedf3b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1 NUMA nodes\n",
"CUDA is non-functional\n"
]
},
{
"data": {
"text/plain": [
"QED Process: 'ke->kkkkke'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"machine = get_machine_info()\n",
"model = QEDModel()\n",
"process = parse_process(\"ke->kkkkke\", model)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "6c2eef40-5df0-4396-8e62-5204c4de61f3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"profile.pb.gz\""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Main binary filename not available.\n",
"Serving web UI on http://localhost:57599\n"
]
}
],
"source": [
"gen_graph(parse_process(\"ke->kke\", model))\n",
"Profile.clear()\n",
"@profile gen_graph(process)\n",
"pprof()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.9.4",
"language": "julia",
"name": "julia-1.9"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.9.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

155
notebooks/large_compton.ipynb Normal file
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@ -0,0 +1,155 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"12"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"using MetagraphOptimization\n",
"using BenchmarkTools\n",
"\n",
"Threads.nthreads()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Graph:\n",
" Nodes: Total: 15866, DataTask: 7937, ComputeTaskQED_S2: 720, \n",
" ComputeTaskQED_Sum: 1, ComputeTaskQED_V: 4320, ComputeTaskQED_S1: 2880, \n",
" ComputeTaskQED_U: 8\n",
" Edges: 21617\n",
" Total Compute Effort: 66249.0\n",
" Total Data Transfer: 1.314048e6\n",
" Total Compute Intensity: 0.050415966540035065\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"machine = get_machine_info()\n",
"model = QEDModel()\n",
"process = parse_process(\"ke->kkkkke\", model)\n",
"\n",
"inputs = [gen_process_input(process) for _ in 1:1e3];\n",
"graph = gen_graph(process)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Graph:\n",
" Nodes: Total: 2234, DataTask: 1121, ComputeTaskQED_S2: 720, \n",
" ComputeTaskQED_Sum: 1, ComputeTaskQED_V: 312, ComputeTaskQED_S1: 72, \n",
" ComputeTaskQED_U: 8\n",
" Edges: 3977\n",
" Total Compute Effort: 11313.0\n",
" Total Data Transfer: 659712.0\n",
" Total Compute Intensity: 0.017148392025611175\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"optimizer = ReductionOptimizer()\n",
"\n",
"compute_compton = get_compute_function(graph, process, machine)\n",
"optimize_to_fixpoint!(optimizer, graph)\n",
"graph"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Calculated 133942.0 results/s, 11162.0 results/s per thread for QED Process: 'ke->kkkkke' (12 threads)\n"
]
}
],
"source": [
"compute_compton_reduced = get_compute_function(graph, process, machine)\n",
"outputs = [zero(ComplexF64) for _ in 1:1e6]\n",
"\n",
"bench_result = @benchmark begin\n",
" Threads.@threads :static for i in eachindex(inputs)\n",
" outputs[i] = compute_compton_reduced(inputs[i])\n",
" end\n",
"end\n",
"\n",
"rate = length(inputs) / (mean(bench_result.times) / 1.0e9)\n",
"rate_per_thread = rate / Threads.nthreads()\n",
"println(\"Calculated $(round(rate)) results/s, $(round(rate_per_thread)) results/s per thread for $(process) ($(Threads.nthreads()) threads)\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Calculated 17124.0 results/s, 1427.0 results/s per thread for QED Process: 'ke->kkkkke' (12 threads)\n"
]
}
],
"source": [
"bench_result = @benchmark begin\n",
" Threads.@threads :static for i in eachindex(inputs)\n",
" outputs[i] = compute_compton(inputs[i])\n",
" end\n",
"end\n",
"\n",
"rate = length(inputs) / (mean(bench_result.times) / 1.0e9)\n",
"rate_per_thread = rate / Threads.nthreads()\n",
"println(\"Calculated $(round(rate)) results/s, $(round(rate_per_thread)) results/s per thread for $(process) ($(Threads.nthreads()) threads)\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.9.4",
"language": "julia",
"name": "julia-1.9"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.9.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

69
notebooks/profiling.ipynb Normal file
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@ -0,0 +1,69 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"using Revise; using MetagraphOptimization; using BenchmarkTools; using ProfileView\n",
"using Base.Threads\n",
"nthreads()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = ABCModel()\n",
"process_str = \"AB->ABBBBB\"\n",
"process = parse_process(process_str, model)\n",
"graph = parse_dag(\"../input/$process_str.txt\", model)\n",
"print(graph)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"@ProfileView.profview optimize_to_fixpoint!(ReductionOptimizer(), graph)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"@ProfileView.profview comp_func = get_compute_function(graph, process, get_machine_info())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.9.4",
"language": "julia",
"name": "julia-1.9"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.9.4"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}

62
results/FWK8999_QED.txt Normal file
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@ -0,0 +1,62 @@
CPU: AMD EPYC 7452 32 Cores, 64 Threads | 122.8 GFLOPS (?, source: https://www.cpubenchmark.net/cpu.php?cpu=AMD+EPYC+7452)
GPU: A30 24GB | 5.161 TFLOPS (source: https://www.techpowerup.com/gpu-specs/a30-pcie.c3792)
Benchmark Summary for QED Process: 'ke->ke':
Measured FLOPS by LIKWID: 5657.0
Total input size: 1.394 GiB
CPU, 64 threads
Time: 0.594810558
Rate: 1.681207548437632e7
GFLOPS: 88.57428190774921
GPU, NVIDIA A30
Time: 1.547648257
Rate: 6.461416510353748e6
GFLOPS: 34.041919930904314
Benchmark Summary for QED Process: 'ke->kke':
Measured FLOPS by LIKWID: 16256.0
Total input size: 1.768 GiB
CPU, 64 threads
Time: 1.294064702
Rate: 7.7275888790914565e6
GFLOPS: 116.99244828756034
GPU, NVIDIA A30
Time: 4.973188906
Rate: 2.0107822544072892e6
GFLOPS: 30.442398346629826
Benchmark Summary for QED Process: 'ke->kkke':
Measured FLOPS by LIKWID: 43433.0
Total input size: 2.632 GiB
CPU, 64 threads
Time: 3.232029091
Rate: 3.094031556784648e6
GFLOPS: 125.15398916399816
GPU, NVIDIA A30
Time: 14.597070187
Rate: 685068.9810963502
GFLOPS: 27.711131662091034
Benchmark Summary for ABC Process: 'AB->AB':
Measured FLOPS by LIKWID: 41.0
Total input size: 2.201 GiB
CPU, 64 threads
Time: 0.688079611
Rate: 1.453320203089116e7
GFLOPS: 0.5549390644454747
GPU, NVIDIA A30
Time: 0.013803574
Rate: 7.244500590933913e8
GFLOPS: 27.662564462822903
Benchmark Summary for ABC Process: 'AB->ABBB':
Measured FLOPS by LIKWID: 899.0
Total input size: 3.079 GiB
CPU, 64 threads
Time: 0.855687624
Rate: 1.1686507692204276e7
GFLOPS: 9.784633680518386
GPU, NVIDIA A30
Time: 0.014804518
Rate: 6.754694749265056e8
GFLOPS: 565.542893445984

40
results/FWKHIP8999
View File

@ -4,8 +4,8 @@ Run with 32 Threads
AB->AB:
Graph:
Nodes: Total: 34, ComputeTaskS2: 2, ComputeTaskU: 4,
ComputeTaskSum: 1, ComputeTaskV: 4, ComputeTaskP: 4,
Nodes: Total: 34, ComputeTaskABC_S2: 2, ComputeTaskABC_U: 4,
ComputeTaskABC_Sum: 1, ComputeTaskABC_V: 4, ComputeTaskABC_P: 4,
DataTask: 19
Edges: 37
Total Compute Effort: 185
@ -27,9 +27,9 @@ Waiting...
AB->ABBB:
Graph:
Nodes: Total: 280, ComputeTaskS2: 24, ComputeTaskU: 6,
ComputeTaskV: 64, ComputeTaskSum: 1, ComputeTaskP: 6,
ComputeTaskS1: 36, DataTask: 143
Nodes: Total: 280, ComputeTaskABC_S2: 24, ComputeTaskABC_U: 6,
ComputeTaskABC_V: 64, ComputeTaskABC_Sum: 1, ComputeTaskABC_P: 6,
ComputeTaskABC_S1: 36, DataTask: 143
Edges: 385
Total Compute Effort: 2007
Total Data Transfer: 1176
@ -50,9 +50,9 @@ Waiting...
AB->ABBBBB:
Graph:
Nodes: Total: 7854, ComputeTaskS2: 720, ComputeTaskU: 8,
ComputeTaskV: 1956, ComputeTaskSum: 1, ComputeTaskP: 8,
ComputeTaskS1: 1230, DataTask: 3931
Nodes: Total: 7854, ComputeTaskABC_S2: 720, ComputeTaskABC_U: 8,
ComputeTaskABC_V: 1956, ComputeTaskABC_Sum: 1, ComputeTaskABC_P: 8,
ComputeTaskABC_S1: 1230, DataTask: 3931
Edges: 11241
Total Compute Effort: 58789
Total Data Transfer: 34826
@ -73,9 +73,9 @@ Waiting...
AB->ABBBBBBB:
Graph:
Nodes: Total: 438436, ComputeTaskS2: 40320, ComputeTaskU: 10,
ComputeTaskV: 109600, ComputeTaskSum: 1, ComputeTaskP: 10,
ComputeTaskS1: 69272, DataTask: 219223
Nodes: Total: 438436, ComputeTaskABC_S2: 40320, ComputeTaskABC_U: 10,
ComputeTaskABC_V: 109600, ComputeTaskABC_Sum: 1, ComputeTaskABC_P: 10,
ComputeTaskABC_S1: 69272, DataTask: 219223
Edges: 628665
Total Compute Effort: 3288131
Total Data Transfer: 1949004
@ -96,9 +96,9 @@ Waiting...
AB->ABBBBBBBBB:
Graph:
Nodes: Total: 39456442, ComputeTaskS2: 3628800, ComputeTaskU: 12,
ComputeTaskV: 9864100, ComputeTaskSum: 1, ComputeTaskP: 12,
ComputeTaskS1: 6235290, DataTask: 19728227
Nodes: Total: 39456442, ComputeTaskABC_S2: 3628800, ComputeTaskABC_U: 12,
ComputeTaskABC_V: 9864100, ComputeTaskABC_Sum: 1, ComputeTaskABC_P: 12,
ComputeTaskABC_S1: 6235290, DataTask: 19728227
Edges: 56578129
Total Compute Effort: 295923153
Total Data Transfer: 175407750
@ -119,9 +119,9 @@ Waiting...
ABAB->ABAB:
Graph:
Nodes: Total: 3218, ComputeTaskS2: 288, ComputeTaskU: 8,
ComputeTaskV: 796, ComputeTaskSum: 1, ComputeTaskP: 8,
ComputeTaskS1: 504, DataTask: 1613
Nodes: Total: 3218, ComputeTaskABC_S2: 288, ComputeTaskABC_U: 8,
ComputeTaskABC_V: 796, ComputeTaskABC_Sum: 1, ComputeTaskABC_P: 8,
ComputeTaskABC_S1: 504, DataTask: 1613
Edges: 4581
Total Compute Effort: 24009
Total Data Transfer: 14144
@ -142,9 +142,9 @@ Waiting...
ABAB->ABC:
Graph:
Nodes: Total: 817, ComputeTaskS2: 72, ComputeTaskU: 7,
ComputeTaskV: 198, ComputeTaskSum: 1, ComputeTaskP: 7,
ComputeTaskS1: 120, DataTask: 412
Nodes: Total: 817, ComputeTaskABC_S2: 72, ComputeTaskABC_U: 7,
ComputeTaskABC_V: 198, ComputeTaskABC_Sum: 1, ComputeTaskABC_P: 7,
ComputeTaskABC_S1: 120, DataTask: 412
Edges: 1151
Total Compute Effort: 6028
Total Data Transfer: 3538

8
scripts/bench_threads.fish
View File

@ -6,20 +6,20 @@ julia --project=./examples -t 4 -e 'import Pkg; Pkg.instantiate()'
#for i in $(seq $minthreads $maxthreads)
# printf "(AB->AB, $i) "
# julia --project=./examples -t $i -O3 -e 'using MetagraphOptimization; using BenchmarkTools; @btime get_operations(graph) setup=(graph = parse_abc("input/AB->AB.txt"))'
# julia --project=./examples -t $i -O3 -e 'using MetagraphOptimization; using BenchmarkTools; @btime get_operations(graph) setup=(graph = parse_dag("input/AB->AB.txt"), ABCModel())'
#end
#for i in $(seq $minthreads $maxthreads)
# printf "(AB->ABBB, $i) "
# julia --project=./examples -t $i -O3 -e 'using MetagraphOptimization; using BenchmarkTools; @btime get_operations(graph) setup=(graph = parse_abc("input/AB->ABBB.txt"))'
# julia --project=./examples -t $i -O3 -e 'using MetagraphOptimization; using BenchmarkTools; @btime get_operations(graph) setup=(graph = parse_dag("input/AB->ABBB.txt"), ABCModel())'
#end
#for i in $(seq $minthreads $maxthreads)
# printf "(AB->ABBBBB, $i) "
# julia --project=./examples -t $i -O3 -e 'using MetagraphOptimization; using BenchmarkTools; @btime get_operations(graph) setup=(graph = parse_abc("input/AB->ABBBBB.txt"))'
# julia --project=./examples -t $i -O3 -e 'using MetagraphOptimization; using BenchmarkTools; @btime get_operations(graph) setup=(graph = parse_dag("input/AB->ABBBBB.txt"), ABCModel())'
#end
for i in $(seq $minthreads $maxthreads)
printf "(AB->ABBBBBBB, $i) "
julia --project=./examples -t $i -O3 -e 'using MetagraphOptimization; using BenchmarkTools; @btime get_operations(graph) setup=(graph = parse_abc("input/AB->ABBBBBBB.txt"))'
julia --project=./examples -t $i -O3 -e 'using MetagraphOptimization; using BenchmarkTools; @btime get_operations(graph) setup=(graph = parse_dag("input/AB->ABBBBBBB.txt"), ABCModel())'
end

180
src/MetagraphOptimization.jl
View File

@ -1,43 +1,111 @@
"""
MetagraphOptimization
A module containing tools to work on DAGs.
"""
module MetagraphOptimization
export Node, Edge, ComputeTaskNode, DataTaskNode, DAG
export AbstractTask,
AbstractComputeTask, AbstractDataTask, DataTask, FusedComputeTask
export make_node,
make_edge,
insert_node,
insert_edge,
is_entry_node,
is_exit_node,
parents,
children,
compute,
graph_properties,
get_exit_node,
is_valid
export NodeFusion,
NodeReduction,
NodeSplit,
push_operation!,
pop_operation!,
can_pop,
reset_graph!,
get_operations
export parse_abc,
ComputeTaskP,
ComputeTaskS1,
ComputeTaskS2,
ComputeTaskV,
ComputeTaskU,
ComputeTaskSum
using QEDbase
# graph types
export DAG
export Node
export Edge
export ComputeTaskNode
export DataTaskNode
export AbstractTask
export AbstractComputeTask
export AbstractDataTask
export DataTask
export FusedComputeTask
export PossibleOperations
export GraphProperties
# graph functions
export make_node
export make_edge
export insert_node
export insert_edge
export is_entry_node
export is_exit_node
export parents
export children
export compute
export data
export compute_effort
export task
export get_properties
export get_exit_node
export operation_stack_length
export is_valid, is_scheduled
# graph operation related
export Operation
export AppliedOperation
export NodeFusion
export NodeReduction
export NodeSplit
export push_operation!
export pop_operation!
export can_pop
export reset_graph!
export get_operations
# ABC model
export ParticleValue
export ParticleA, ParticleB, ParticleC
export ABCParticle, ABCProcessDescription, ABCProcessInput, ABCModel
export ComputeTaskABC_P
export ComputeTaskABC_S1
export ComputeTaskABC_S2
export ComputeTaskABC_V
export ComputeTaskABC_U
export ComputeTaskABC_Sum
# QED model
export FeynmanDiagram, FeynmanVertex, FeynmanTie, FeynmanParticle
export PhotonStateful, FermionStateful, AntiFermionStateful
export QEDParticle, QEDProcessDescription, QEDProcessInput, QEDModel
export ComputeTaskQED_P
export ComputeTaskQED_S1
export ComputeTaskQED_S2
export ComputeTaskQED_V
export ComputeTaskQED_U
export ComputeTaskQED_Sum
export gen_graph
# code generation related
export execute
export parse_dag, parse_process
export gen_process_input
export get_compute_function
export gen_tape, execute_tape
# estimator
export cost_type, graph_cost, operation_effect
export GlobalMetricEstimator, CDCost
# optimization
export AbstractOptimizer, GreedyOptimizer, ReductionOptimizer, RandomWalkOptimizer
export optimize_step!, optimize!
export fixpoint_reached, optimize_to_fixpoint!
# machine info
export Machine
export get_machine_info
export ==, in, show, isempty, delete!, length
export bytes_to_human_readable
# TODO: this is probably not good
import QEDprocesses.compute
import Base.length
import Base.show
import Base.==
import Base.+
import Base.-
import Base.in
import Base.copy
import Base.isempty
@ -46,11 +114,14 @@ import Base.insert!
import Base.collect
include("devices/interface.jl")
include("task/type.jl")
include("node/type.jl")
include("diff/type.jl")
include("properties/type.jl")
include("operation/type.jl")
include("graph/type.jl")
include("scheduler/type.jl")
include("trie.jl")
include("utility.jl")
@ -72,6 +143,7 @@ include("node/properties.jl")
include("node/validate.jl")
include("operation/utility.jl")
include("operation/iterate.jl")
include("operation/apply.jl")
include("operation/clean.jl")
include("operation/find.jl")
@ -79,12 +151,58 @@ include("operation/get.jl")
include("operation/print.jl")
include("operation/validate.jl")
include("properties/create.jl")
include("properties/utility.jl")
include("task/create.jl")
include("task/compare.jl")
include("task/print.jl")
include("task/compute.jl")
include("task/properties.jl")
include("estimator/interface.jl")
include("estimator/global_metric.jl")
include("optimization/interface.jl")
include("optimization/greedy.jl")
include("optimization/random_walk.jl")
include("optimization/reduce.jl")
include("models/interface.jl")
include("models/print.jl")
include("models/abc/types.jl")
include("models/abc/particle.jl")
include("models/abc/compute.jl")
include("models/abc/create.jl")
include("models/abc/properties.jl")
include("models/abc/parse.jl")
include("models/abc/print.jl")
include("models/qed/types.jl")
include("models/qed/particle.jl")
include("models/qed/diagrams.jl")
include("models/qed/compute.jl")
include("models/qed/create.jl")
include("models/qed/properties.jl")
include("models/qed/parse.jl")
include("models/qed/print.jl")
include("devices/measure.jl")
include("devices/detect.jl")
include("devices/impl.jl")
include("devices/numa/impl.jl")
include("devices/cuda/impl.jl")
# can currently not use AMDGPU because of incompatability with the newest rocm drivers
# include("devices/rocm/impl.jl")
# oneapi seems also broken for now
# include("devices/oneapi/impl.jl")
include("scheduler/interface.jl")
include("scheduler/greedy.jl")
include("code_gen/type.jl")
include("code_gen/tape_machine.jl")
include("code_gen/function.jl")
end # module MetagraphOptimization

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"""
get_compute_function(graph::DAG, process::AbstractProcessDescription, machine::Machine)
Return a function of signature `compute_<id>(input::AbstractProcessInput)`, which will return the result of the DAG computation on the given input.
"""
function get_compute_function(graph::DAG, process::AbstractProcessDescription, machine::Machine)
tape = gen_tape(graph, process, machine)
initCaches = Expr(:block, tape.initCachesCode...)
assignInputs = Expr(:block, expr_from_fc.(tape.inputAssignCode)...)
code = Expr(:block, expr_from_fc.(tape.computeCode)...)
functionId = to_var_name(UUIDs.uuid1(rng[1]))
resSym = eval(gen_access_expr(entry_device(tape.machine), tape.outputSymbol))
expr = Meta.parse(
"function compute_$(functionId)(data_input::AbstractProcessInput) $(initCaches); $(assignInputs); $code; return $resSym; end",
)
func = eval(expr)
return func
end
"""
execute(graph::DAG, process::AbstractProcessDescription, machine::Machine, input::AbstractProcessInput)
Execute the code of the given `graph` on the given input particles.
This is essentially shorthand for
```julia
tape = gen_tape(graph, process, machine)
return execute_tape(tape, input)
```
See also: [`parse_dag`](@ref), [`parse_process`](@ref), [`gen_process_input`](@ref)
"""
function execute(graph::DAG, process::AbstractProcessDescription, machine::Machine, input::AbstractProcessInput)
tape = gen_tape(graph, process, machine)
return execute_tape(tape, input)
end

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function call_fc(fc::FunctionCall{VectorT, 0}, cache::Dict{Symbol, Any}) where {VectorT <: SVector{1}}
cache[fc.return_symbol] = fc.func(cache[fc.arguments[1]])
return nothing
end
function call_fc(fc::FunctionCall{VectorT, 1}, cache::Dict{Symbol, Any}) where {VectorT <: SVector{1}}
cache[fc.return_symbol] = fc.func(fc.additional_arguments[1], cache[fc.arguments[1]])
return nothing
end
function call_fc(fc::FunctionCall{VectorT, 0}, cache::Dict{Symbol, Any}) where {VectorT <: SVector{2}}
cache[fc.return_symbol] = fc.func(cache[fc.arguments[1]], cache[fc.arguments[2]])
return nothing
end
function call_fc(fc::FunctionCall{VectorT, 1}, cache::Dict{Symbol, Any}) where {VectorT <: SVector{2}}
cache[fc.return_symbol] = fc.func(fc.additional_arguments[1], cache[fc.arguments[1]], cache[fc.arguments[2]])
return nothing
end
function call_fc(fc::FunctionCall{VectorT, 1}, cache::Dict{Symbol, Any}) where {VectorT}
cache[fc.return_symbol] = fc.func(fc.additional_arguments[1], getindex.(Ref(cache), fc.arguments)...)
return nothing
end
"""
call_fc(fc::FunctionCall, cache::Dict{Symbol, Any})
Execute the given [`FunctionCall`](@ref) on the dictionary.
Several more specialized versions of this function exist to reduce vector unrolling work for common cases.
"""
function call_fc(fc::FunctionCall{VectorT, M}, cache::Dict{Symbol, Any}) where {VectorT, M}
cache[fc.return_symbol] = fc.func(fc.additional_arguments..., getindex.(Ref(cache), fc.arguments)...)
return nothing
end
function expr_from_fc(fc::FunctionCall{VectorT, 0}) where {VectorT}
return Meta.parse(
"$(eval(gen_access_expr(fc.device, fc.return_symbol))) = $(fc.func)($(unroll_symbol_vector(eval.(gen_access_expr.(Ref(fc.device), fc.arguments)))))",
)
end
"""
expr_from_fc(fc::FunctionCall)
For a given function call, return an expression evaluating it.
"""
function expr_from_fc(fc::FunctionCall{VectorT, M}) where {VectorT, M}
func_call = Expr(
:call,
Symbol(fc.func),
fc.additional_arguments...,
eval.(gen_access_expr.(Ref(fc.device), fc.arguments))...,
)
expr = :($(eval(gen_access_expr(fc.device, fc.return_symbol))) = $func_call)
return expr
end
"""
gen_cache_init_code(machine::Machine)
For each [`AbstractDevice`](@ref) in the given [`Machine`](@ref), returning a `Vector{Expr}` doing the initialization.
"""
function gen_cache_init_code(machine::Machine)
initializeCaches = Vector{Expr}()
for device in machine.devices
push!(initializeCaches, gen_cache_init_code(device))
end
return initializeCaches
end
"""
part_from_x(type::Type, index::Int, x::AbstractProcessInput)
Return the [`ParticleValue`](@ref) of the given type of particle with the given `index` from the given process input.
Function is wrapped into a [`FunctionCall`](@ref) in [`gen_input_assignment_code`](@ref).
"""
part_from_x(type::Type, index::Int, x::AbstractProcessInput) =
ParticleValue{type, ComplexF64}(get_particle(x, type, index), one(ComplexF64))
"""
gen_input_assignment_code(
inputSymbols::Dict{String, Vector{Symbol}},
processDescription::AbstractProcessDescription,
machine::Machine,
processInputSymbol::Symbol = :input,
)
Return a `Vector{Expr}` doing the input assignments from the given `processInputSymbol` onto the `inputSymbols`.
"""
function gen_input_assignment_code(
inputSymbols::Dict{String, Vector{Symbol}},
processDescription::AbstractProcessDescription,
machine::Machine,
processInputSymbol::Symbol = :input,
)
@assert length(inputSymbols) >=
sum(values(in_particles(processDescription))) + sum(values(out_particles(processDescription))) "Number of input Symbols is smaller than the number of particles in the process description"
assignInputs = Vector{FunctionCall}()
for (name, symbols) in inputSymbols
(type, index) = type_index_from_name(model(processDescription), name)
# make a function for this, since we can't use anonymous functions in the FunctionCall
for symbol in symbols
device = entry_device(machine)
push!(
assignInputs,
FunctionCall(
# x is the process input
part_from_x,
SVector{1, Symbol}(processInputSymbol),
SVector{2, Any}(type, index),
symbol,
device,
),
)
end
end
return assignInputs
end
"""
gen_tape(graph::DAG, process::AbstractProcessDescription, machine::Machine)
Generate the code for a given graph. The return value is a [`Tape`](@ref).
See also: [`execute`](@ref), [`execute_tape`](@ref)
"""
function gen_tape(graph::DAG, process::AbstractProcessDescription, machine::Machine)
schedule = schedule_dag(GreedyScheduler(), graph, machine)
# get inSymbols
inputSyms = Dict{String, Vector{Symbol}}()
for node in get_entry_nodes(graph)
if !haskey(inputSyms, node.name)
inputSyms[node.name] = Vector{Symbol}()
end
push!(inputSyms[node.name], Symbol("$(to_var_name(node.id))_in"))
end
# get outSymbol
outSym = Symbol(to_var_name(get_exit_node(graph).id))
initCaches = gen_cache_init_code(machine)
assignInputs = gen_input_assignment_code(inputSyms, process, machine, :input)
return Tape(initCaches, assignInputs, schedule, inputSyms, outSym, Dict(), process, machine)
end
"""
execute_tape(tape::Tape, input::AbstractProcessInput)
Execute the given tape with the given input.
For implementation reasons, this disregards the set [`CacheStrategy`](@ref) of the devices and always uses a dictionary.
"""
function execute_tape(tape::Tape, input::AbstractProcessInput)
cache = Dict{Symbol, Any}()
cache[:input] = input
# simply execute all the code snippets here
# TODO: `@assert` that process input fits the tape.process
for expr in tape.initCachesCode
@eval $expr
end
for function_call in tape.inputAssignCode
call_fc(function_call, cache)
end
for function_call in tape.computeCode
call_fc(function_call, cache)
end
return cache[tape.outputSymbol]
end

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"""
Tape
TODO: update docs
- `code::Vector{Expr}`: The julia expression containing the code for the whole graph.
- `inputSymbols::Dict{String, Vector{Symbol}}`: A dictionary of symbols mapping the names of the input nodes of the graph to the symbols their inputs should be provided on.
- `outputSymbol::Symbol`: The symbol of the final calculated value
"""
struct Tape
initCachesCode::Vector{Expr}
inputAssignCode::Vector{FunctionCall}
computeCode::Vector{FunctionCall}
inputSymbols::Dict{String, Vector{Symbol}}
outputSymbol::Symbol
cache::Dict{Symbol, Any}
process::AbstractProcessDescription
machine::Machine
end

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using CUDA
"""
CUDAGPU <: AbstractGPU
Representation of a specific CUDA GPU that code can run on. Implements the [`AbstractDevice`](@ref) interface.
"""
mutable struct CUDAGPU <: AbstractGPU
device::Any # TODO: what's the cuda device type?
cacheStrategy::CacheStrategy
FLOPS::Float64
end
push!(DEVICE_TYPES, CUDAGPU)
CACHE_STRATEGIES[CUDAGPU] = [LocalVariables()]
default_strategy(::Type{T}) where {T <: CUDAGPU} = LocalVariables()
function measure_device!(device::CUDAGPU; verbose::Bool)
if verbose
println("Measuring CUDA GPU $(device.device)")
end
# TODO implement
return nothing
end
"""
get_devices(deviceType::Type{T}; verbose::Bool) where {T <: CUDAGPU}
Return a Vector of [`CUDAGPU`](@ref)s available on the current machine. If `verbose` is true, print some additional information.
"""
function get_devices(deviceType::Type{T}; verbose::Bool = false) where {T <: CUDAGPU}
devices = Vector{AbstractDevice}()
if !CUDA.functional()
if verbose
println("CUDA is non-functional")
end
return devices
end
CUDADevices = CUDA.devices()
if verbose
println("Found $(length(CUDADevices)) CUDA devices")
end
for device in CUDADevices
push!(devices, CUDAGPU(device, default_strategy(CUDAGPU), -1))
end
return devices
end

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"""
get_machine_info(verbose::Bool)
Return the [`Machine`](@ref) currently running on. The parameter `verbose` defaults to true when interactive.
"""
function get_machine_info(; verbose::Bool = Base.is_interactive)
devices = Vector{AbstractDevice}()
for device in device_types()
devs = get_devices(device, verbose = verbose)
for dev in devs
push!(devices, dev)
end
end
noDevices = length(devices)
@assert noDevices > 0 "No devices were found, but at least one NUMA node should always be available!"
transferRates = Matrix{Float64}(undef, noDevices, noDevices)
fill!(transferRates, -1)
return Machine(devices, transferRates)
end

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"""
device_types()
Return a vector of available and implemented device types.
See also: [`DEVICE_TYPES`](@ref)
"""
function device_types()
return DEVICE_TYPES
end
"""
entry_device(machine::Machine)
Return the "entry" device, i.e., the device that starts CPU threads and GPU kernels, and takes input values and returns the output value.
"""
function entry_device(machine::Machine)
return machine.devices[1]
end
"""
strategies(t::Type{T}) where {T <: AbstractDevice}
Return a vector of available [`CacheStrategy`](@ref)s for the given [`AbstractDevice`](@ref).
The caching strategies are used in code generation.
"""
function strategies(t::Type{T}) where {T <: AbstractDevice}
if !haskey(CACHE_STRATEGIES, t)
error("Trying to get strategies for $T, but it has no strategies defined!")
end
return CACHE_STRATEGIES[t]
end
"""
cache_strategy(device::AbstractDevice)
Returns the cache strategy set for this device.
"""
function cache_strategy(device::AbstractDevice)
return device.cacheStrategy
end
"""
set_cache_strategy(device::AbstractDevice, cacheStrategy::CacheStrategy)
Sets the device's cache strategy. After this call, [`cache_strategy`](@ref) should return `cacheStrategy` on the given device.
"""
function set_cache_strategy(device::AbstractDevice, cacheStrategy::CacheStrategy)
device.cacheStrategy = cacheStrategy
return nothing
end

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"""
AbstractDevice
Abstract base type for every device, like GPUs, CPUs or any other compute devices.
Every implementation needs to implement various functions and needs a member `cacheStrategy`.
"""
abstract type AbstractDevice end
abstract type AbstractCPU <: AbstractDevice end
abstract type AbstractGPU <: AbstractDevice end
"""
Machine
A representation of a machine to execute on. Contains information about its architecture (CPUs, GPUs, maybe more). This representation can be used to make a more accurate cost prediction of a [`DAG`](@ref) state.
See also: [`Scheduler`](@ref)
"""
struct Machine
devices::Vector{AbstractDevice}
transferRates::Matrix{Float64}
end
"""
CacheStrategy
Abstract base type for caching strategies.
See also: [`strategies`](@ref)
"""
abstract type CacheStrategy end
"""
LocalVariables <: CacheStrategy
A caching strategy relying solely on local variables for every input and output.
Implements the [`CacheStrategy`](@ref) interface.
"""
struct LocalVariables <: CacheStrategy end
"""
Dictionary <: CacheStrategy
A caching strategy relying on a dictionary of Symbols to store every input and output.
Implements the [`CacheStrategy`](@ref) interface.
"""
struct Dictionary <: CacheStrategy end
"""
DEVICE_TYPES::Vector{Type}
Global vector of available and implemented device types. Each implementation of a [`AbstractDevice`](@ref) should add its concrete type to this vector.
See also: [`device_types`](@ref), [`get_devices`](@ref)
"""
DEVICE_TYPES = Vector{Type}()
"""
CACHE_STRATEGIES::Dict{Type{AbstractDevice}, Symbol}
Global dictionary of available caching strategies per device. Each implementation of [`AbstractDevice`](@ref) should add its available strategies to the dictionary.
See also: [`strategies`](@ref)
"""
CACHE_STRATEGIES = Dict{Type, Vector{CacheStrategy}}()
"""
default_strategy(deviceType::Type{T}) where {T <: AbstractDevice}
Interface function that must be implemented for every subtype of [`AbstractDevice`](@ref). Returns the default [`CacheStrategy`](@ref) to use on the given device type.
See also: [`cache_strategy`](@ref), [`set_cache_strategy`](@ref)
"""
function default_strategy end
"""
get_devices(t::Type{T}; verbose::Bool) where {T <: AbstractDevice}
Interface function that must be implemented for every subtype of [`AbstractDevice`](@ref). Returns a `Vector{Type}` of the devices for the given [`AbstractDevice`](@ref) Type available on the current machine.
"""
function get_devices end
"""
measure_device!(device::AbstractDevice; verbose::Bool)
Interface function that must be implemented for every subtype of [`AbstractDevice`](@ref). Measures the compute speed of the given device and writes into it.
"""
function measure_device! end
"""
gen_cache_init_code(device::AbstractDevice)
Interface function that must be implemented for every subtype of [`AbstractDevice`](@ref) and at least one [`CacheStrategy`](@ref). Returns an `Expr` initializing this device's variable cache.
The strategy is a symbol
"""
function gen_cache_init_code end
"""
gen_access_expr(device::AbstractDevice, symbol::Symbol)
Interface function that must be implemented for every subtype of [`AbstractDevice`](@ref) and at least one [`CacheStrategy`](@ref).
Return an `Expr` or `QuoteNode` accessing the variable identified by [`symbol`].
"""
function gen_access_expr end

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"""
measure_devices(machine::Machine; verbose::Bool)
Measure FLOPS, RAM, cache sizes and what other properties can be extracted for the devices in the given machine.
"""
function measure_devices!(machine::Machine; verbose::Bool = Base.is_interactive())
for device in machine.devices
measure_device!(device; verbose = verbose)
end
return nothing
end
"""
measure_transfer_rates(machine::Machine; verbose::Bool)
Measure the transfer rates between devices in the machine.
"""
function measure_transfer_rates!(machine::Machine; verbose::Bool = Base.is_interactive())
# TODO implement
return nothing
end

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using NumaAllocators
"""
NumaNode <: AbstractCPU
Representation of a specific CPU that code can run on. Implements the [`AbstractDevice`](@ref) interface.
"""
mutable struct NumaNode <: AbstractCPU
numaId::UInt16
threads::UInt16
cacheStrategy::CacheStrategy
FLOPS::Float64
id::UUID
end
push!(DEVICE_TYPES, NumaNode)
CACHE_STRATEGIES[NumaNode] = [LocalVariables()]
default_strategy(::Type{T}) where {T <: NumaNode} = LocalVariables()
function measure_device!(device::NumaNode; verbose::Bool)
if verbose
println("Measuring Numa Node $(device.numaId)")
end
# TODO implement
return nothing
end
"""
get_devices(deviceType::Type{T}; verbose::Bool) where {T <: NumaNode}
Return a Vector of [`NumaNode`](@ref)s available on the current machine. If `verbose` is true, print some additional information.
"""
function get_devices(deviceType::Type{T}; verbose::Bool = false) where {T <: NumaNode}
devices = Vector{AbstractDevice}()
noNumaNodes = highest_numa_node()
if (verbose)
println("Found $(noNumaNodes + 1) NUMA nodes")
end
for i in 0:noNumaNodes
push!(devices, NumaNode(i, 1, default_strategy(NumaNode), -1, UUIDs.uuid1(rng[1])))
end
return devices
end
"""
gen_cache_init_code(device::NumaNode)
Generate code for initializing the [`LocalVariables`](@ref) strategy on a [`NumaNode`](@ref).
"""
function gen_cache_init_code(device::NumaNode)
if typeof(device.cacheStrategy) <: LocalVariables
# don't need to initialize anything
return Expr(:block)
elseif typeof(device.cacheStrategy) <: Dictionary
return Meta.parse("cache_$(to_var_name(device.id)) = Dict{Symbol, Any}()")
# TODO: sizehint?
end
return error("Unimplemented cache strategy \"$(device.cacheStrategy)\" for device \"$(device)\"")
end
"""
gen_access_expr(device::NumaNode, symbol::Symbol)
Generate code to access the variable designated by `symbol` on a [`NumaNode`](@ref), using the [`CacheStrategy`](@ref) set in the device.
"""
function gen_access_expr(device::NumaNode, symbol::Symbol)
return _gen_access_expr(device, device.cacheStrategy, symbol)
end
"""
_gen_access_expr(device::NumaNode, ::LocalVariables, symbol::Symbol)
Internal function for dispatch, used in [`gen_access_expr`](@ref).
"""
function _gen_access_expr(device::NumaNode, ::LocalVariables, symbol::Symbol)
s = Symbol("data_$symbol")
quoteNode = Meta.parse(":($s)")
return quoteNode
end
"""
_gen_access_expr(device::NumaNode, ::Dictionary, symbol::Symbol)
Internal function for dispatch, used in [`gen_access_expr`](@ref).
"""
function _gen_access_expr(device::NumaNode, ::Dictionary, symbol::Symbol)
accessStr = ":(cache_$(to_var_name(device.id))[:$symbol])"
quoteNode = Meta.parse(accessStr)
return quoteNode
end

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using oneAPI
"""
oneAPIGPU <: AbstractGPU
Representation of a specific Intel GPU that code can run on. Implements the [`AbstractDevice`](@ref) interface.
"""
mutable struct oneAPIGPU <: AbstractGPU
device::Any
cacheStrategy::CacheStrategy
FLOPS::Float64
end
push!(DEVICE_TYPES, oneAPIGPU)
CACHE_STRATEGIES[oneAPIGPU] = [LocalVariables()]
default_strategy(::Type{T}) where {T <: oneAPIGPU} = LocalVariables()
function measure_device!(device::oneAPIGPU; verbose::Bool)
if verbose
println("Measuring oneAPI GPU $(device.device)")
end
# TODO implement
return nothing
end
"""
get_devices(deviceType::Type{T}; verbose::Bool = false) where {T <: oneAPIGPU}
Return a Vector of [`oneAPIGPU`](@ref)s available on the current machine. If `verbose` is true, print some additional information.
"""
function get_devices(deviceType::Type{T}; verbose::Bool = false) where {T <: oneAPIGPU}
devices = Vector{AbstractDevice}()
if !oneAPI.functional()
if verbose
println("oneAPI is non-functional")
end
return devices
end
oneAPIDevices = oneAPI.devices()
if verbose
println("Found $(length(oneAPIDevices)) oneAPI devices")
end
for device in oneAPIDevices
push!(devices, oneAPIGPU(device, default_strategy(oneAPIGPU), -1))
end
return devices
end

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using AMDGPU
"""
ROCmGPU <: AbstractGPU
Representation of a specific AMD GPU that code can run on. Implements the [`AbstractDevice`](@ref) interface.
"""
mutable struct ROCmGPU <: AbstractGPU
device::Any
cacheStrategy::CacheStrategy
FLOPS::Float64
end
push!(DEVICE_TYPES, ROCmGPU)
CACHE_STRATEGIES[ROCmGPU] = [LocalVariables()]
default_strategy(::Type{T}) where {T <: ROCmGPU} = LocalVariables()
function measure_device!(device::ROCmGPU; verbose::Bool)
if verbose
println("Measuring ROCm GPU $(device.device)")
end
# TODO implement
return nothing
end
"""
get_devices(deviceType::Type{T}; verbose::Bool = false) where {T <: ROCmGPU}
Return a Vector of [`ROCmGPU`](@ref)s available on the current machine. If `verbose` is true, print some additional information.
"""
function get_devices(deviceType::Type{T}; verbose::Bool = false) where {T <: ROCmGPU}
devices = Vector{AbstractDevice}()
if !AMDGPU.functional()
if verbose
println("AMDGPU is non-functional")
end
return devices
end
AMDDevices = AMDGPU.devices()
if verbose
println("Found $(length(AMDDevices)) AMD devices")
end
for device in AMDDevices
push!(devices, ROCmGPU(device, default_strategy(ROCmGPU), -1))
end
return devices
end

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"""
show(io::IO, diff::Diff)
Pretty-print a [`Diff`](@ref). Called via print, println and co.
"""
function show(io::IO, diff::Diff)
print(io, "Nodes: ")
print(io, length(diff.addedNodes) + length(diff.removedNodes))
print(io, " Edges: ")
print(io, ", Edges: ")
return print(io, length(diff.addedEdges) + length(diff.removedEdges))
end

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@ -1,4 +1,9 @@
# return a namedtuple of the lengths of the added/removed nodes/edges
"""
length(diff::Diff)
Return a named tuple of the lengths of the added/removed nodes/edges.
The fields are `.addedNodes`, `.addedEdges`, `.removedNodes` and `.removedEdges`.
"""
function length(diff::Diff)
return (
addedNodes = length(diff.addedNodes),

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"""
Diff
A named tuple representing a difference of added and removed nodes and edges on a [`DAG`](@ref).
"""
const Diff = NamedTuple{
(:addedNodes, :removedNodes, :addedEdges, :removedEdges),
Tuple{Vector{Node}, Vector{Node}, Vector{Edge}, Vector{Edge}},
(:addedNodes, :removedNodes, :addedEdges, :removedEdges, :updatedChildren),
Tuple{Vector{Node}, Vector{Node}, Vector{Edge}, Vector{Edge}, Vector{Tuple{Node, AbstractTask}}},
}
function Diff()
@ -9,5 +14,8 @@ function Diff()
removedNodes = Vector{Node}(),
addedEdges = Vector{Edge}(),
removedEdges = Vector{Edge}(),
# children were updated in the task, updatedChildren[x][2] is the task before the update
updatedChildren = Vector{Tuple{Node, AbstractTask}}(),
)::Diff
end

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"""
CDCost
Representation of a [`DAG`](@ref)'s cost as estimated by the [`GlobalMetricEstimator`](@ref).
# Fields:
`.data`: The total data transfer.\\
`.computeEffort`: The total compute effort.\\
`.computeIntensity`: The compute intensity, will always equal `.computeEffort / .data`.
!!! note
Note that the `computeIntensity` doesn't necessarily make sense in the context of only operation costs.
For example, for node fusions this will always be 0, since the computeEffort is zero.
It will still work as intended when adding/subtracting to/from a `graph_cost` estimate.
"""
const CDCost = NamedTuple{(:data, :computeEffort, :computeIntensity), Tuple{Float64, Float64, Float64}}
function +(cost1::CDCost, cost2::CDCost)::CDCost
d = cost1.data + cost2.data
ce = computeEffort = cost1.computeEffort + cost2.computeEffort
return (data = d, computeEffort = ce, computeIntensity = ce / d)::CDCost
end
function -(cost1::CDCost, cost2::CDCost)::CDCost
d = cost1.data - cost2.data
ce = computeEffort = cost1.computeEffort - cost2.computeEffort
return (data = d, computeEffort = ce, computeIntensity = ce / d)::CDCost
end
function isless(cost1::CDCost, cost2::CDCost)::Bool
return cost1.data + cost1.computeEffort < cost2.data + cost2.computeEffort
end
function zero(type::Type{CDCost})
return (data = 0.0, computeEffort = 00.0, computeIntensity = 0.0)::CDCost
end
function typemax(type::Type{CDCost})
return (data = Inf, computeEffort = Inf, computeIntensity = 0.0)::CDCost
end
struct GlobalMetricEstimator <: AbstractEstimator end
function cost_type(estimator::GlobalMetricEstimator)::Type{CDCost}
return CDCost
end
function graph_cost(estimator::GlobalMetricEstimator, graph::DAG)
properties = get_properties(graph)
return (
data = properties.data,
computeEffort = properties.computeEffort,
computeIntensity = properties.computeIntensity,
)::CDCost
end
function operation_effect(estimator::GlobalMetricEstimator, graph::DAG, operation::NodeFusion)
return (data = -data(operation.input[2].task), computeEffort = 0.0, computeIntensity = 0.0)::CDCost
end
function operation_effect(estimator::GlobalMetricEstimator, graph::DAG, operation::NodeReduction)
s = length(operation.input) - 1
return (
data = s * -data(task(operation.input[1])),
computeEffort = s * -compute_effort(task(operation.input[1])),
computeIntensity = typeof(operation.input) <: DataTaskNode ? 0.0 : Inf,
)::CDCost
end
function operation_effect(estimator::GlobalMetricEstimator, graph::DAG, operation::NodeSplit)
s::Float64 = length(parents(operation.input)) - 1
d::Float64 = s * data(task(operation.input))
ce::Float64 = s * compute_effort(task(operation.input))
return (data = d, computeEffort = ce, computeIntensity = ce / d)::CDCost
end
function String(::GlobalMetricEstimator)
return "global_metric"
end

44
src/estimator/interface.jl Normal file
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@ -0,0 +1,44 @@
"""
AbstractEstimator
Abstract base type for an estimator. An estimator estimates the cost of a graph or the difference an operation applied to a graph will make to its cost.
Interface functions are
- [`graph_cost`](@ref)
- [`operation_effect`](@ref)
"""
abstract type AbstractEstimator end
"""
cost_type(estimator::AbstractEstimator)
Interface function returning a specific estimator's cost type, i.e., the type returned by its implementation of [`graph_cost`](@ref) and [`operation_effect`](@ref).
"""
function cost_type end
"""
graph_cost(estimator::AbstractEstimator, graph::DAG)
Get the total estimated cost of the graph. The cost's data type can be chosen by the implementation, but must have a usable lessthan comparison operator (<), basic math operators (+, -) and an implementation of `zero()` and `typemax()`.
"""
function graph_cost end
"""
operation_effect(estimator::AbstractEstimator, graph::DAG, operation::Operation)
Get the estimated effect on the cost of the graph, such that `graph_cost(estimator, graph) + operation_effect(estimator, graph, operation) ~= graph_cost(estimator, graph_with_operation_applied)`. There is no hard requirement for this, but the better the estimate, the better an optimization algorithm will be.
!!! note
There is a default implementation of this function, applying the operation, calling [`graph_cost`](@ref), then popping the operation again.
It can be much faster to overload this function for a specific estimator and directly compute the effects from the operation if possible.
"""
function operation_effect(estimator::AbstractEstimator, graph::DAG, operation::Operation)
# This is currently not stably working, see issue #16
cost = graph_cost(estimator, graph)
push_operation!(graph, operation)
cost_after = graph_cost(estimator, graph)
pop_operation!(graph)
return cost_after - cost
end

14
src/estimator/likwid.jl Normal file
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@ -0,0 +1,14 @@
using LIKWID
"""
LIKWIDEstimator <: AbstractEstimator
An estimator using LIKWID.jl to measure the total FLOPS needed to execute the graph.
"""
struct LIKWIDEstimator <: AbstractEstimator end
cost_type(::LIKWIDEstimator) = Float64
function graph_cost(::LIKWIDEstimator, graph::DAG)
end

21
src/graph/compare.jl
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@ -1,14 +1,21 @@
"""
in(node::Node, graph::DAG)
Check whether the node is part of the graph.
"""
in(node::Node, graph::DAG) = node in graph.nodes
in(edge::Edge, graph::DAG) = edge in graph.edges
function ==(n1::Node, n2::Node, g::DAG)
if typeof(n1) != typeof(n2)
return false
end
if !(n1 in g) || !(n2 in g)
"""
in(edge::Edge, graph::DAG)
Check whether the edge is part of the graph.
"""
function in(edge::Edge, graph::DAG)
n1 = edge.edge[1]
n2 = edge.edge[2]
if !(n1 in graph) || !(n2 in graph)
return false
end
return n1.task == n2.task && children(n1) == children(n2)
return n1 in children(n2)
end

30
src/graph/interface.jl
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@ -1,6 +1,10 @@
# user interface on the DAG
"""
push_operation!(graph::DAG, operation::Operation)
# applies a new operation to the end of the graph
Apply a new operation to the graph.
See also: [`DAG`](@ref), [`pop_operation!`](@ref)
"""
function push_operation!(graph::DAG, operation::Operation)
# 1.: Add the operation to the DAG
push!(graph.operationsToApply, operation)
@ -8,7 +12,13 @@ function push_operation!(graph::DAG, operation::Operation)
return nothing
end
# reverts the latest applied operation, essentially like a ctrl+z for
"""
pop_operation!(graph::DAG)
Revert the latest applied operation on the graph.
See also: [`DAG`](@ref), [`push_operation!`](@ref)
"""
function pop_operation!(graph::DAG)
# 1.: Remove the operation from the appliedChain of the DAG
if !isempty(graph.operationsToApply)
@ -23,10 +33,18 @@ function pop_operation!(graph::DAG)
return nothing
end
can_pop(graph::DAG) =
!isempty(graph.operationsToApply) || !isempty(graph.appliedOperations)
"""
can_pop(graph::DAG)
# reset the graph to its initial state with no operations applied
Return `true` if [`pop_operation!`](@ref) is possible, `false` otherwise.
"""
can_pop(graph::DAG) = !isempty(graph.operationsToApply) || !isempty(graph.appliedOperations)
"""
reset_graph!(graph::DAG)
Reset the graph to its initial state with no operations applied.
"""
function reset_graph!(graph::DAG)
while (can_pop(graph))
pop_operation!(graph)

220
src/graph/mute.jl
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@ -3,12 +3,19 @@
# 2: keep track of what was changed for the diff (if track == true)
# 3: invalidate operation caches
function insert_node!(
graph::DAG,
node::Node,
track = true,
invalidate_cache = true,
)
"""
insert_node!(graph::DAG, node::Node; track = true, invalidate_cache = true)
Insert the node into the graph.
## Keyword Arguments
`track::Bool`: Whether to add the changes to the [`DAG`](@ref)'s [`Diff`](@ref). Should be set `false` in parsing or graph creation functions for performance.
`invalidate_cache::Bool`: Whether to invalidate caches associated with the changes. Should also be turned off for graph creation or parsing.
See also: [`remove_node!`](@ref), [`insert_edge!`](@ref), [`remove_edge!`](@ref)
"""
function insert_node!(graph::DAG, node::Node; track = true, invalidate_cache = true)
# 1: mute
push!(graph.nodes, node)
@ -26,14 +33,20 @@ function insert_node!(
return node
end
function insert_edge!(
graph::DAG,
node1::Node,
node2::Node,
track = true,
invalidate_cache = true,
)
# @assert (node2 ∉ node1.parents) && (node1 ∉ node2.children) "Edge to insert already exists"
"""
insert_edge!(graph::DAG, node1::Node, node2::Node; track = true, invalidate_cache = true)
Insert the edge between node1 (child) and node2 (parent) into the graph.
## Keyword Arguments
`track::Bool`: Whether to add the changes to the [`DAG`](@ref)'s [`Diff`](@ref). Should be set `false` in parsing or graph creation functions for performance.
`invalidate_cache::Bool`: Whether to invalidate caches associated with the changes. Should also be turned off for graph creation or parsing.
See also: [`insert_node!`](@ref), [`remove_node!`](@ref), [`remove_edge!`](@ref)
"""
function insert_edge!(graph::DAG, node1::Node, node2::Node; track = true, invalidate_cache = true)
#@assert (node2 ∉ parents(node1)) && (node1 ∉ children(node2)) "Edge to insert already exists"
# 1: mute
# edge points from child to parent
@ -59,13 +72,20 @@ function insert_edge!(
return nothing
end
function remove_node!(
graph::DAG,
node::Node,
track = true,
invalidate_cache = true,
)
# @assert node in graph.nodes "Trying to remove a node that's not in the graph"
"""
remove_node!(graph::DAG, node::Node; track = true, invalidate_cache = true)
Remove the node from the graph.
## Keyword Arguments
`track::Bool`: Whether to add the changes to the [`DAG`](@ref)'s [`Diff`](@ref). Should be set `false` in parsing or graph creation functions for performance.
`invalidate_cache::Bool`: Whether to invalidate caches associated with the changes. Should also be turned off for graph creation or parsing.
See also: [`insert_node!`](@ref), [`insert_edge!`](@ref), [`remove_edge!`](@ref)
"""
function remove_node!(graph::DAG, node::Node; track = true, invalidate_cache = true)
#@assert node in graph.nodes "Trying to remove a node that's not in the graph"
# 1: mute
delete!(graph.nodes, node)
@ -86,27 +106,45 @@ function remove_node!(
return nothing
end
function remove_edge!(
graph::DAG,
node1::Node,
node2::Node,
track = true,
invalidate_cache = true,
)
"""
remove_edge!(graph::DAG, node1::Node, node2::Node; track = true, invalidate_cache = true)
Remove the edge between node1 (child) and node2 (parent) into the graph.
## Keyword Arguments
`track::Bool`: Whether to add the changes to the [`DAG`](@ref)'s [`Diff`](@ref). Should be set `false` in parsing or graph creation functions for performance.
`invalidate_cache::Bool`: Whether to invalidate caches associated with the changes. Should also be turned off for graph creation or parsing.
See also: [`insert_node!`](@ref), [`remove_node!`](@ref), [`insert_edge!`](@ref)
"""
function remove_edge!(graph::DAG, node1::Node, node2::Node; track = true, invalidate_cache = true)
# 1: mute
pre_length1 = length(node1.parents)
pre_length2 = length(node2.children)
filter!(x -> x != node2, node1.parents)
filter!(x -> x != node1, node2.children)
for i in eachindex(node1.parents)
if (node1.parents[i] == node2)
splice!(node1.parents, i)
break
end
end
for i in eachindex(node2.children)
if (node2.children[i] == node1)
splice!(node2.children, i)
break
end
end
#=@assert begin
removed = pre_length1 - length(node1.parents)
removed <= 1
removed = pre_length1 - length(node1.parents)
removed <= 1
end "removed more than one node from node1's parents"=#
#=@assert begin
removed = pre_length2 - length(node2.children)
removed <= 1
removed = pre_length2 - length(children(node2))
removed <= 1
end "removed more than one node from node2's children"=#
# 2: keep track
@ -131,25 +169,109 @@ function remove_edge!(
return nothing
end
# return the graph "difference" since last time this function was called
function replace_children!(task::FusedComputeTask, before, after)
replacedIn1 = length(findall(x -> x == before, task.t1_inputs))
replacedIn2 = length(findall(x -> x == before, task.t2_inputs))
#@assert replacedIn1 >= 1 || replacedIn2 >= 1 "Nothing to replace while replacing $before with $after in $(task.t1_inputs...) and $(task.t2_inputs...)"
replace!(task.t1_inputs, before => after)
replace!(task.t2_inputs, before => after)
# recursively descend down the tree, but only in the tasks where we're replacing things
if replacedIn1 > 0
replace_children!(task.first_task, before, after)
end
if replacedIn2 > 0
replace_children!(task.second_task, before, after)
end
return nothing
end
function replace_children!(task::AbstractTask, before, after)
return nothing
end
function update_child!(graph::DAG, n::Node, child_before::Symbol, child_after::Symbol; track = true)
# only need to update fused compute tasks
if !(typeof(task(n)) <: FusedComputeTask)
return nothing
end
taskBefore = copy(task(n))
#=if !((child_before in task(n).t1_inputs) || (child_before in task(n).t2_inputs))
println("------------------ Nothing to replace!! ------------------")
child_ids = Vector{String}()
for child in children(n)
push!(child_ids, "$(child.id)")
end
println("From $(child_before) to $(child_after) in $n with children $(child_ids)")
@assert false
end=#
replace_children!(task(n), child_before, child_after)
#=if !((child_after in task(n).t1_inputs) || (child_after in task(n).t2_inputs))
println("------------------ Did not replace anything!! ------------------")
child_ids = Vector{String}()
for child in children(n)
push!(child_ids, "$(child.id)")
end
println("From $(child_before) to $(child_after) in $n with children $(child_ids)")
@assert false
end=#
# keep track
if (track)
push!(graph.diff.updatedChildren, (n, taskBefore))
end
end
"""
get_snapshot_diff(graph::DAG)
Return the graph's [`Diff`](@ref) since last time this function was called.
See also: [`revert_diff!`](@ref), [`AppliedOperation`](@ref) and [`revert_operation!`](@ref)
"""
function get_snapshot_diff(graph::DAG)
return swapfield!(graph, :diff, Diff())
end
# function to invalidate the operation caches for a given NodeFusion
"""
invalidate_caches!(graph::DAG, operation::NodeFusion)
Invalidate the operation caches for a given [`NodeFusion`](@ref).
This deletes the operation from the graph's possible operations and from the involved nodes' own operation caches.
"""
function invalidate_caches!(graph::DAG, operation::NodeFusion)
delete!(graph.possibleOperations, operation)
# delete the operation from all caches of nodes involved in the operation
filter!(!=(operation), operation.input[1].nodeFusions)
filter!(!=(operation), operation.input[3].nodeFusions)
for n in [1, 3]
for i in eachindex(operation.input[n].nodeFusions)
if operation == operation.input[n].nodeFusions[i]
splice!(operation.input[n].nodeFusions, i)
break
end
end
end
operation.input[2].nodeFusion = missing
return nothing
end
# function to invalidate the operation caches for a given NodeReduction
"""
invalidate_caches!(graph::DAG, operation::NodeReduction)
Invalidate the operation caches for a given [`NodeReduction`](@ref).
This deletes the operation from the graph's possible operations and from the involved nodes' own operation caches.
"""
function invalidate_caches!(graph::DAG, operation::NodeReduction)
delete!(graph.possibleOperations, operation)
@ -160,7 +282,13 @@ function invalidate_caches!(graph::DAG, operation::NodeReduction)
return nothing
end
# function to invalidate the operation caches for a given NodeSplit
"""
invalidate_caches!(graph::DAG, operation::NodeSplit)
Invalidate the operation caches for a given [`NodeSplit`](@ref).
This deletes the operation from the graph's possible operations and from the involved nodes' own operation caches.
"""
function invalidate_caches!(graph::DAG, operation::NodeSplit)
delete!(graph.possibleOperations, operation)
@ -171,7 +299,11 @@ function invalidate_caches!(graph::DAG, operation::NodeSplit)
return nothing
end
# function to invalidate the operation caches of a ComputeTaskNode
"""
invalidate_operation_caches!(graph::DAG, node::ComputeTaskNode)
Invalidate the operation caches of the given node through calls to the respective [`invalidate_caches!`](@ref) functions.
"""
function invalidate_operation_caches!(graph::DAG, node::ComputeTaskNode)
if !ismissing(node.nodeReduction)
invalidate_caches!(graph, node.nodeReduction)
@ -185,7 +317,11 @@ function invalidate_operation_caches!(graph::DAG, node::ComputeTaskNode)
return nothing
end
# function to invalidate the operation caches of a DataTaskNode
"""
invalidate_operation_caches!(graph::DAG, node::DataTaskNode)
Invalidate the operation caches of the given node through calls to the respective [`invalidate_caches!`](@ref) functions.
"""
function invalidate_operation_caches!(graph::DAG, node::DataTaskNode)
if !ismissing(node.nodeReduction)
invalidate_caches!(graph, node.nodeReduction)

37
src/graph/print.jl
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@ -1,4 +1,9 @@
function show_nodes(io, graph::DAG)
"""
show_nodes(io::IO, graph::DAG)
Print a graph's nodes. Should only be used for small graphs as it prints every node in a list.
"""
function show_nodes(io::IO, graph::DAG)
print(io, "[")
first = true
for n in graph.nodes
@ -12,17 +17,23 @@ function show_nodes(io, graph::DAG)
return print(io, "]")
end
"""
show(io::IO, graph::DAG)
Print the given graph to io. If there are too many nodes it will print only a summary of them.
"""
function show(io::IO, graph::DAG)
apply_all!(graph)
println(io, "Graph:")
print(io, " Nodes: ")
nodeDict = Dict{Type, Int64}()
noEdges = 0
for node in graph.nodes
if haskey(nodeDict, typeof(node.task))
nodeDict[typeof(node.task)] = nodeDict[typeof(node.task)] + 1
if haskey(nodeDict, typeof(task(node)))
nodeDict[typeof(task(node))] = nodeDict[typeof(task(node))] + 1
else
nodeDict[typeof(node.task)] = 1
nodeDict[typeof(task(node))] = 1
end
noEdges += length(parents(node))
end
@ -30,7 +41,7 @@ function show(io::IO, graph::DAG)
if length(graph.nodes) <= 20
show_nodes(io, graph)
else
print("Total: ", length(graph.nodes), ", ")
print(io, "Total: ", length(graph.nodes), ", ")
first = true
i = 0
for (type, number) in zip(keys(nodeDict), values(nodeDict))
@ -38,22 +49,18 @@ function show(io::IO, graph::DAG)
if first
first = false
else
print(", ")
print(io, ", ")
end
if (i % 3 == 0)
print("\n ")
print(io, "\n ")
end
print(type, ": ", number)
print(io, type, ": ", number)
end
end
println(io)
println(io, " Edges: ", noEdges)
properties = graph_properties(graph)
println(io, " Total Compute Effort: ", properties.compute_effort)
properties = get_properties(graph)
println(io, " Total Compute Effort: ", properties.computeEffort)
println(io, " Total Data Transfer: ", properties.data)
return println(
io,
" Total Compute Intensity: ",
properties.compute_intensity,
)
return println(io, " Total Compute Intensity: ", properties.computeIntensity)
end

57
src/graph/properties.jl
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@ -1,28 +1,24 @@
function graph_properties(graph::DAG)
"""
get_properties(graph::DAG)
Return the graph's [`GraphProperties`](@ref).
"""
function get_properties(graph::DAG)
# make sure the graph is fully generated
apply_all!(graph)
d = 0
ce = 0
ed = 0
for node in graph.nodes
d += data(node.task) * length(node.parents)
ce += compute_effort(node.task)
ed += length(node.parents)
if (graph.properties.computeEffort == 0.0)
graph.properties = GraphProperties(graph)
end
ci = ce / d
result = (
data = d,
compute_effort = ce,
compute_intensity = ci,
nodes = length(graph.nodes),
edges = ed,
)
return result
return graph.properties
end
"""
get_exit_node(graph::DAG)
Return the graph's exit node. This assumes the graph only has a single exit node. If the graph has multiple exit nodes, the one encountered first will be returned.
"""
function get_exit_node(graph::DAG)
for node in graph.nodes
if (is_exit_node(node))
@ -31,3 +27,28 @@ function get_exit_node(graph::DAG)
end
@assert false "The given graph has no exit node! It is either empty or not acyclic!"
end
"""
get_entry_nodes(graph::DAG)
Return a vector of the graph's entry nodes.
"""
function get_entry_nodes(graph::DAG)
apply_all!(graph)
result = Vector{Node}()
for node in graph.nodes
if (is_entry_node(node))
push!(result, node)
end
end
return result
end
"""
operation_stack_length(graph::DAG)
Return the number of operations applied to the graph.
"""
function operation_stack_length(graph::DAG)
return length(graph.appliedOperations) + length(graph.operationsToApply)
end

47
src/graph/type.jl
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@ -1,23 +1,30 @@
using DataStructures
"""
PossibleOperations
A struct storing all possible operations on a [`DAG`](@ref).
To get the [`PossibleOperations`](@ref) on a [`DAG`](@ref), use [`get_operations`](@ref).
"""
mutable struct PossibleOperations
nodeFusions::Set{NodeFusion}
nodeReductions::Set{NodeReduction}
nodeSplits::Set{NodeSplit}
end
function PossibleOperations()
return PossibleOperations(
Set{NodeFusion}(),
Set{NodeReduction}(),
Set{NodeSplit}(),
)
end
"""
DAG
# The actual state of the DAG is the initial state given by the set of nodes
# but with all the operations in appliedChain applied in order
The representation of the graph as a set of [`Node`](@ref)s.
A DAG can be loaded using the appropriate parse_dag function, e.g. [`parse_dag`](@ref).
[`Operation`](@ref)s can be applied on it using [`push_operation!`](@ref) and reverted using [`pop_operation!`](@ref) like a stack.
To get the set of possible operations, use [`get_operations`](@ref).
The members of the object should not be manually accessed, instead always use the provided interface functions.
"""
mutable struct DAG
nodes::Set{Node}
nodes::Set{Union{DataTaskNode, ComputeTaskNode}}
# The operations currently applied to the set of nodes
appliedOperations::Stack{AppliedOperation}
@ -29,13 +36,30 @@ mutable struct DAG
possibleOperations::PossibleOperations
# The set of nodes whose possible operations need to be reevaluated
dirtyNodes::Set{Node}
dirtyNodes::Set{Union{DataTaskNode, ComputeTaskNode}}
# "snapshot" system: keep track of added/removed nodes/edges since last snapshot
# these are muted in insert_node! etc.
diff::Diff
# the cached properties of the DAG
properties::GraphProperties
end
"""
PossibleOperations()
Construct and return an empty [`PossibleOperations`](@ref) object.
"""
function PossibleOperations()
return PossibleOperations(Set{NodeFusion}(), Set{NodeReduction}(), Set{NodeSplit}())
end
"""
DAG()
Construct and return an empty [`DAG`](@ref).
"""
function DAG()
return DAG(
Set{Node}(),
@ -44,5 +68,6 @@ function DAG()
PossibleOperations(),
Set{Node}(),
Diff(),
GraphProperties(),
)
end

27
src/graph/validate.jl
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@ -1,4 +1,8 @@
# check whether the given graph is connected
"""
is_connected(graph::DAG)
Return whether the given graph is connected.
"""
function is_connected(graph::DAG)
nodeQueue = Deque{Node}()
push!(nodeQueue, get_exit_node(graph))
@ -16,6 +20,11 @@ function is_connected(graph::DAG)
return length(seenNodes) == length(graph.nodes)
end
"""
is_valid(graph::DAG)
Validate the entire graph using asserts. Intended for testing with `@assert is_valid(graph)`.
"""
function is_valid(graph::DAG)
for node in graph.nodes
@assert is_valid(graph, node)
@ -50,3 +59,19 @@ function is_valid(graph::DAG)
return true
end
"""
is_scheduled(graph::DAG)
Validate that the entire graph has been scheduled, i.e., every [`ComputeTaskNode`](@ref) has its `.device` set.
"""
function is_scheduled(graph::DAG)
for node in graph.nodes
if (node isa DataTaskNode)
continue
end
@assert !ismissing(node.device)
end
return true
end

92
src/models/abc/compute.jl Normal file
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@ -0,0 +1,92 @@
using AccurateArithmetic
using StaticArrays
"""
compute(::ComputeTaskABC_P, data::ABCParticleValue)
Return the particle and value as is.
0 FLOP.
"""
function compute(::ComputeTaskABC_P, data::ABCParticleValue{P})::ABCParticleValue{P} where {P <: ABCParticle}
return data
end
"""
compute(::ComputeTaskABC_U, data::ABCParticleValue)
Compute an outer edge. Return the particle value with the same particle and the value multiplied by an ABC_outer_edge factor.
1 FLOP.
"""
function compute(::ComputeTaskABC_U, data::ABCParticleValue{P})::ABCParticleValue{P} where {P <: ABCParticle}
return ABCParticleValue{P}(data.p, data.v * ABC_outer_edge(data.p))
end
"""
compute(::ComputeTaskABC_V, data1::ABCParticleValue, data2::ABCParticleValue)
Compute a vertex. Preserve momentum and particle types (AB->C etc.) to create resulting particle, multiply values together and times a vertex factor.
6 FLOP.
"""
function compute(
::ComputeTaskABC_V,
data1::ABCParticleValue{P1},
data2::ABCParticleValue{P2},
)::ABCParticleValue where {P1 <: ABCParticle, P2 <: ABCParticle}
p3 = ABC_conserve_momentum(data1.p, data2.p)
dataOut = ABCParticleValue{typeof(p3)}(p3, data1.v * ABC_vertex() * data2.v)
return dataOut
end
"""
compute(::ComputeTaskABC_S2, data1::ABCParticleValue, data2::ABCParticleValue)
Compute a final inner edge (2 input particles, no output particle).
For valid inputs, both input particles should have the same momenta at this point.
12 FLOP.
"""
function compute(
::ComputeTaskABC_S2,
data1::ParticleValue{P},
data2::ParticleValue{P},
)::Float64 where {P <: ABCParticle}
#=
@assert isapprox(abs(data1.p.momentum.E), abs(data2.p.momentum.E), rtol = 0.001, atol = sqrt(eps())) "E: $(data1.p.momentum.E) vs. $(data2.p.momentum.E)"
@assert isapprox(data1.p.momentum.px, -data2.p.momentum.px, rtol = 0.001, atol = sqrt(eps())) "px: $(data1.p.momentum.px) vs. $(data2.p.momentum.px)"
@assert isapprox(data1.p.momentum.py, -data2.p.momentum.py, rtol = 0.001, atol = sqrt(eps())) "py: $(data1.p.momentum.py) vs. $(data2.p.momentum.py)"
@assert isapprox(data1.p.momentum.pz, -data2.p.momentum.pz, rtol = 0.001, atol = sqrt(eps())) "pz: $(data1.p.momentum.pz) vs. $(data2.p.momentum.pz)"
=#
inner = ABC_inner_edge(data1.p)
return data1.v * inner * data2.v
end
"""
compute(::ComputeTaskABC_S1, data::ABCParticleValue)
Compute inner edge (1 input particle, 1 output particle).
11 FLOP.
"""
function compute(::ComputeTaskABC_S1, data::ABCParticleValue{P})::ABCParticleValue{P} where {P <: ABCParticle}
return ABCParticleValue{P}(data.p, data.v * ABC_inner_edge(data.p))
end
"""
compute(::ComputeTaskABC_Sum, data...)
compute(::ComputeTaskABC_Sum, data::AbstractArray)
Compute a sum over the vector. Use an algorithm that accounts for accumulated errors in long sums with potentially large differences in magnitude of the summands.
Linearly many FLOP with growing data.
"""
function compute(::ComputeTaskABC_Sum, data...)::Float64
return sum(data)
end
function compute(::ComputeTaskABC_Sum, data::AbstractArray)::Float64
return sum(data)
end

86
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@ -0,0 +1,86 @@
using QEDbase
using Random
using Roots
using ForwardDiff
ComputeTaskABC_Sum() = ComputeTaskABC_Sum(0)
function _svector_from_type_in(processDescription::ABCProcessDescription, type, particles)
if haskey(in_particles(processDescription), type)
return SVector{in_particles(processDescription)[type], type}(filter(x -> typeof(x) <: type, particles))
end
return SVector{0, type}()
end
function _svector_from_type_out(processDescription::ABCProcessDescription, type, particles)
if haskey(out_particles(processDescription), type)
return SVector{out_particles(processDescription)[type], type}(filter(x -> typeof(x) <: type, particles))
end
return SVector{0, type}()
end
"""
gen_process_input(processDescription::ABCProcessDescription)
Return a ProcessInput of randomly generated [`ABCParticle`](@ref)s from a [`ABCProcessDescription`](@ref). The process description can be created manually or parsed from a string using [`parse_process`](@ref).
Note: This uses RAMBO to create a valid process with conservation of momentum and energy.
"""
function gen_process_input(processDescription::ABCProcessDescription)
inParticleTypes = keys(processDescription.inParticles)
outParticleTypes = keys(processDescription.outParticles)
massSum = 0
inputMasses = Vector{Float64}()
for (particle, n) in processDescription.inParticles
for _ in 1:n
massSum += mass(particle)
push!(inputMasses, mass(particle))
end
end
outputMasses = Vector{Float64}()
for (particle, n) in processDescription.outParticles
for _ in 1:n
massSum += mass(particle)
push!(outputMasses, mass(particle))
end
end
# add some extra random mass to allow for some momentum
massSum += rand(rng[threadid()]) * (length(inputMasses) + length(outputMasses))
inputParticles = Vector{ABCParticle}()
initialMomenta = generate_initial_moms(massSum, inputMasses)
index = 1
for (particle, n) in processDescription.inParticles
for _ in 1:n
mom = initialMomenta[index]
push!(inputParticles, particle(mom))
index += 1
end
end
outputParticles = Vector{ABCParticle}()
final_momenta = generate_physical_massive_moms(rng[threadid()], massSum, outputMasses)
index = 1
for (particle, n) in processDescription.outParticles
for _ in 1:n
mom = final_momenta[index]
push!(outputParticles, particle(SFourMomentum(-mom.E, mom.px, mom.py, mom.pz)))
index += 1
end
end
inA = _svector_from_type_in(processDescription, ParticleA, inputParticles)
inB = _svector_from_type_in(processDescription, ParticleB, inputParticles)
inC = _svector_from_type_in(processDescription, ParticleC, inputParticles)
outA = _svector_from_type_out(processDescription, ParticleA, outputParticles)
outB = _svector_from_type_out(processDescription, ParticleB, outputParticles)
outC = _svector_from_type_out(processDescription, ParticleC, outputParticles)
processInput = ABCProcessInput(processDescription, inA, inB, inC, outA, outB, outC)
return return processInput
end

188
src/models/abc/parse.jl
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@ -1,11 +1,17 @@
using Printf
# functions for importing DAGs from a file
regex_a = r"^[A-C]\d+$" # Regex for the initial particles
regex_c = r"^[A-C]\(([^']*),([^']*)\)$" # Regex for the combinations of 2 particles
regex_m = r"^M\(([^']*),([^']*),([^']*)\)$" # Regex for the combinations of 3 particles
regex_plus = r"^\+$" # Regex for the sum
const PARTICLE_VALUE_SIZE::Int = 48
const FLOAT_SIZE::Int = 8
"""
parse_nodes(input::AbstractString)
Parse the given string into a vector of strings containing each node.
"""
function parse_nodes(input::AbstractString)
regex = r"'([^']*)'"
matches = eachmatch(regex, input)
@ -13,6 +19,11 @@ function parse_nodes(input::AbstractString)
return output
end
"""
parse_edges(input::AbstractString)
Parse the given string into a vector of strings containing each edge. Currently unused since the entire graph can be read from just the node names.
"""
function parse_edges(input::AbstractString)
regex = r"\('([^']*)', '([^']*)'\)"
matches = eachmatch(regex, input)
@ -20,8 +31,14 @@ function parse_edges(input::AbstractString)
return output
end
# reads an abc-model process from the given file
function parse_abc(filename::String, verbose::Bool = false)
"""
parse_dag(filename::String, model::ABCModel; verbose::Bool = false)
Read an abc-model process from the given file. If `verbose` is set to true, print some progress information to stdout.
Returns a valid [`DAG`](@ref).
"""
function parse_dag(filename::AbstractString, model::ABCModel, verbose::Bool = false)
file = open(filename, "r")
if (verbose)
@ -46,9 +63,9 @@ function parse_abc(filename::String, verbose::Bool = false)
end
sizehint!(graph.nodes, estimate_no_nodes)
sum_node = insert_node!(graph, make_node(ComputeTaskSum()), false, false)
global_data_out = insert_node!(graph, make_node(DataTask(10)), false, false)
insert_edge!(graph, sum_node, global_data_out, false, false)
sum_node = insert_node!(graph, make_node(ComputeTaskABC_Sum(0)), track = false, invalidate_cache = false)
global_data_out = insert_node!(graph, make_node(DataTask(FLOAT_SIZE)), track = false, invalidate_cache = false)
insert_edge!(graph, sum_node, global_data_out, track = false, invalidate_cache = false)
# remember the data out nodes for connection
dataOutNodes = Dict()
@ -63,25 +80,29 @@ function parse_abc(filename::String, verbose::Bool = false)
noNodes += 1
if (noNodes % 100 == 0)
if (verbose)
@printf "\rReading Nodes... %.2f%%" (
100.0 * noNodes / nodesToRead
)
percent = string(round(100.0 * noNodes / nodesToRead, digits = 2), "%")
print("\rReading Nodes... $percent")
end
end
if occursin(regex_a, node)
# add nodes and edges for the state reading to u(P(Particle))
data_in = insert_node!(graph, make_node(DataTask(4)), false, false) # read particle data node
compute_P =
insert_node!(graph, make_node(ComputeTaskP()), false, false) # compute P node
data_Pu = insert_node!(graph, make_node(DataTask(6)), false, false) # transfer data from P to u
compute_u =
insert_node!(graph, make_node(ComputeTaskU()), false, false) # compute U node
data_out = insert_node!(graph, make_node(DataTask(3)), false, false) # transfer data out from u
data_in = insert_node!(
graph,
make_node(DataTask(PARTICLE_VALUE_SIZE), string(node)),
track = false,
invalidate_cache = false,
) # read particle data node
compute_P = insert_node!(graph, make_node(ComputeTaskABC_P()), track = false, invalidate_cache = false) # compute P node
data_Pu =
insert_node!(graph, make_node(DataTask(PARTICLE_VALUE_SIZE)), track = false, invalidate_cache = false) # transfer data from P to u (one ABCParticleValue object)
compute_u = insert_node!(graph, make_node(ComputeTaskABC_U()), track = false, invalidate_cache = false) # compute U node
data_out =
insert_node!(graph, make_node(DataTask(PARTICLE_VALUE_SIZE)), track = false, invalidate_cache = false) # transfer data out from u (one ABCParticleValue object)
insert_edge!(graph, data_in, compute_P, false, false)
insert_edge!(graph, compute_P, data_Pu, false, false)
insert_edge!(graph, data_Pu, compute_u, false, false)
insert_edge!(graph, compute_u, data_out, false, false)
insert_edge!(graph, data_in, compute_P, track = false, invalidate_cache = false)
insert_edge!(graph, compute_P, data_Pu, track = false, invalidate_cache = false)
insert_edge!(graph, data_Pu, compute_u, track = false, invalidate_cache = false)
insert_edge!(graph, compute_u, data_out, track = false, invalidate_cache = false)
# remember the data_out node for future edges
dataOutNodes[node] = data_out
@ -91,50 +112,48 @@ function parse_abc(filename::String, verbose::Bool = false)
in1 = capt.captures[1]
in2 = capt.captures[2]
compute_v =
insert_node!(graph, make_node(ComputeTaskV()), false, false)
data_out = insert_node!(graph, make_node(DataTask(5)), false, false)
compute_v = insert_node!(graph, make_node(ComputeTaskABC_V()), track = false, invalidate_cache = false)
data_out =
insert_node!(graph, make_node(DataTask(PARTICLE_VALUE_SIZE)), track = false, invalidate_cache = false)
if (occursin(regex_c, in1))
# put an S node after this input
compute_S = insert_node!(
compute_S = insert_node!(graph, make_node(ComputeTaskABC_S1()), track = false, invalidate_cache = false)
data_S_v = insert_node!(
graph,
make_node(ComputeTaskS1()),
false,
false,
make_node(DataTask(PARTICLE_VALUE_SIZE)),
track = false,
invalidate_cache = false,
)
data_S_v =
insert_node!(graph, make_node(DataTask(5)), false, false)
insert_edge!(graph, dataOutNodes[in1], compute_S, false, false)
insert_edge!(graph, compute_S, data_S_v, false, false)
insert_edge!(graph, dataOutNodes[in1], compute_S, track = false, invalidate_cache = false)
insert_edge!(graph, compute_S, data_S_v, track = false, invalidate_cache = false)
insert_edge!(graph, data_S_v, compute_v, false, false)
insert_edge!(graph, data_S_v, compute_v, track = false, invalidate_cache = false)
else
insert_edge!(graph, dataOutNodes[in1], compute_v, false, false)
insert_edge!(graph, dataOutNodes[in1], compute_v, track = false, invalidate_cache = false)
end
if (occursin(regex_c, in2))
# i think the current generator only puts the combined particles in the first space, so this case might never be entered
# put an S node after this input
compute_S = insert_node!(
compute_S = insert_node!(graph, make_node(ComputeTaskABC_S1()), track = false, invalidate_cache = false)
data_S_v = insert_node!(
graph,
make_node(ComputeTaskS1()),
false,
false,
make_node(DataTask(PARTICLE_VALUE_SIZE)),
track = false,
invalidate_cache = false,
)
data_S_v =
insert_node!(graph, make_node(DataTask(5)), false, false)
insert_edge!(graph, dataOutNodes[in2], compute_S, false, false)
insert_edge!(graph, compute_S, data_S_v, false, false)
insert_edge!(graph, dataOutNodes[in2], compute_S, track = false, invalidate_cache = false)
insert_edge!(graph, compute_S, data_S_v, track = false, invalidate_cache = false)
insert_edge!(graph, data_S_v, compute_v, false, false)
insert_edge!(graph, data_S_v, compute_v, track = false, invalidate_cache = false)
else
insert_edge!(graph, dataOutNodes[in2], compute_v, false, false)
insert_edge!(graph, dataOutNodes[in2], compute_v, track = false, invalidate_cache = false)
end
insert_edge!(graph, compute_v, data_out, false, false)
insert_edge!(graph, compute_v, data_out, track = false, invalidate_cache = false)
dataOutNodes[node] = data_out
elseif occursin(regex_m, node)
@ -145,40 +164,85 @@ function parse_abc(filename::String, verbose::Bool = false)
in3 = capt.captures[3]
# in2 + in3 with a v
compute_v =
insert_node!(graph, make_node(ComputeTaskV()), false, false)
data_v = insert_node!(graph, make_node(DataTask(5)), false, false)
compute_v = insert_node!(graph, make_node(ComputeTaskABC_V()), track = false, invalidate_cache = false)
data_v =
insert_node!(graph, make_node(DataTask(PARTICLE_VALUE_SIZE)), track = false, invalidate_cache = false)
insert_edge!(graph, dataOutNodes[in2], compute_v, false, false)
insert_edge!(graph, dataOutNodes[in3], compute_v, false, false)
insert_edge!(graph, compute_v, data_v, false, false)
insert_edge!(graph, dataOutNodes[in2], compute_v, track = false, invalidate_cache = false)
insert_edge!(graph, dataOutNodes[in3], compute_v, track = false, invalidate_cache = false)
insert_edge!(graph, compute_v, data_v, track = false, invalidate_cache = false)
# combine with the v of the combined other input
compute_S2 =
insert_node!(graph, make_node(ComputeTaskS2()), false, false)
data_out =
insert_node!(graph, make_node(DataTask(10)), false, false)
compute_S2 = insert_node!(graph, make_node(ComputeTaskABC_S2()), track = false, invalidate_cache = false)
data_out = insert_node!(graph, make_node(DataTask(FLOAT_SIZE)), track = false, invalidate_cache = false) # output of a S2 task is only a float
insert_edge!(graph, data_v, compute_S2, false, false)
insert_edge!(graph, dataOutNodes[in1], compute_S2, false, false)
insert_edge!(graph, compute_S2, data_out, false, false)
insert_edge!(graph, data_v, compute_S2, track = false, invalidate_cache = false)
insert_edge!(graph, dataOutNodes[in1], compute_S2, track = false, invalidate_cache = false)
insert_edge!(graph, compute_S2, data_out, track = false, invalidate_cache = false)
insert_edge!(graph, data_out, sum_node, false, false)
insert_edge!(graph, data_out, sum_node, track = false, invalidate_cache = false)
add_child!(task(sum_node))
elseif occursin(regex_plus, node)
if (verbose)
println("\rReading Nodes Complete ")
println("Added ", length(graph.nodes), " nodes")
end
else
@assert false (
"Unknown node '$node' while reading from file $filename"
)
@assert false ("Unknown node '$node' while reading from file $filename")
end
end
#put all nodes into dirty nodes set
graph.dirtyNodes = copy(graph.nodes)
if (verbose)
println("Generating the graph's properties")
end
graph.properties = GraphProperties(graph)
if (verbose)
println("Done")
end
# don't actually need to read the edges
return graph
end
"""
parse_process(string::AbstractString, model::ABCModel)
Parse a string representation of a process, such as "AB->ABBB" into the corresponding [`ABCProcessDescription`](@ref).
"""
function parse_process(str::AbstractString, model::ABCModel)
inParticles = Dict{Type, Int}()
outParticles = Dict{Type, Int}()
if !(contains(str, "->"))
throw("Did not find -> while parsing process \"$str\"")
end
(inStr, outStr) = split(str, "->")
if (isempty(inStr) || isempty(outStr))
throw("Process (\"$str\") input or output part is empty!")
end
for t in types(model)
inCount = count(x -> x == String(t)[1], inStr)
outCount = count(x -> x == String(t)[1], outStr)
if inCount != 0
inParticles[t] = inCount
end
if outCount != 0
outParticles[t] = outCount
end
end
if length(inStr) != sum(values(inParticles))
throw("Encountered unknown characters in the input part of process \"$str\"")
elseif length(outStr) != sum(values(outParticles))
throw("Encountered unknown characters in the output part of process \"$str\"")
end
return ABCProcessDescription(inParticles, outParticles)
end

232
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@ -0,0 +1,232 @@
using StaticArrays
import QEDbase.mass
"""
ABCModel <: AbstractPhysicsModel
Singleton definition for identification of the ABC-Model.
"""
struct ABCModel <: AbstractPhysicsModel end
"""
ABCParticle
Base type for all particles in the [`ABCModel`](@ref).
"""
abstract type ABCParticle <: AbstractParticle end
"""
ParticleA <: ABCParticle
An 'A' particle in the ABC Model.
"""
struct ParticleA <: ABCParticle
momentum::SFourMomentum
end
"""
ParticleB <: ABCParticle
A 'B' particle in the ABC Model.
"""
struct ParticleB <: ABCParticle
momentum::SFourMomentum
end
"""
ParticleC <: ABCParticle
A 'C' particle in the ABC Model.
"""
struct ParticleC <: ABCParticle
momentum::SFourMomentum
end
"""
ABCProcessDescription <: AbstractProcessDescription
A description of a process in the ABC-Model. Contains the input and output particles.
See also: [`in_particles`](@ref), [`out_particles`](@ref), [`parse_process`](@ref)
"""
struct ABCProcessDescription <: AbstractProcessDescription
inParticles::Dict{Type, Int}
outParticles::Dict{Type, Int}
end
"""
ABCProcessInput <: AbstractProcessInput
Input for a ABC Process. Contains the [`ABCProcessDescription`](@ref) of the process it is an input for, and the values of the in and out particles.
See also: [`gen_process_input`](@ref)
"""
struct ABCProcessInput{N1, N2, N3, N4, N5, N6} <: AbstractProcessInput
process::ABCProcessDescription
inA::SVector{N1, ParticleA}
inB::SVector{N2, ParticleB}
inC::SVector{N3, ParticleC}
outA::SVector{N4, ParticleA}
outB::SVector{N5, ParticleB}
outC::SVector{N6, ParticleC}
end
ABCParticleValue{ParticleType <: ABCParticle} = ParticleValue{ParticleType, ComplexF64}
"""
mass(t::Type{T}) where {T <: ABCParticle}
Return the mass (at rest) of the given particle type.
"""
mass(::ParticleA) = 1.0
mass(::ParticleB) = 1.0
mass(::ParticleC) = 0.0
mass(::Type{ParticleA}) = 1.0
mass(::Type{ParticleB}) = 1.0
mass(::Type{ParticleC}) = 0.0
"""
interaction_result(t1::Type{T1}, t2::Type{T2}) where {T1 <: ABCParticle, T2 <: ABCParticle}
For 2 given (non-equal) particle types, return the third of ABC.
"""
function interaction_result(t1::Type{T1}, t2::Type{T2}) where {T1 <: ABCParticle, T2 <: ABCParticle}
@assert t1 != t2
if t1 != ParticleA && t2 != ParticleA
return ParticleA
elseif t1 != ParticleB && t2 != ParticleB
return ParticleB
else
return ParticleC
end
end
"""
types(::ABCModel)
Return a Vector of the possible types of particle in the [`ABCModel`](@ref).
"""
function types(::ABCModel)
return [ParticleA, ParticleB, ParticleC]
end
"""
square(p::ABCParticle)
Return the square of the particle's momentum as a `Float` value.
Takes 7 effective FLOP.
"""
function square(p::ABCParticle)
return getMass2(p.momentum)
end
"""
ABC_inner_edge(p::ABCParticle)
Return the factor of the inner edge with the given (virtual) particle.
Takes 10 effective FLOP. (3 here + 7 in square(p))
"""
function ABC_inner_edge(p::ABCParticle)
return 1.0 / (square(p) - mass(p)^2)
end
"""
ABC_outer_edge(p::ABCParticle)
Return the factor of the outer edge with the given (real) particle.
Takes 0 effective FLOP.
"""
function ABC_outer_edge(p::ABCParticle)
return 1.0
end
"""
ABC_vertex()
Return the factor of a vertex.
Takes 0 effective FLOP since it's constant.
"""
function ABC_vertex()
i = 1.0
lambda = 1.0 / 137.0
return i * lambda
end
"""
ABC_conserve_momentum(p1::ABCParticle, p2::ABCParticle)
Calculate and return a new particle from two given interacting ones at a vertex.
Takes 4 effective FLOP.
"""
function ABC_conserve_momentum(p1::ABCParticle, p2::ABCParticle)
t3 = interaction_result(typeof(p1), typeof(p2))
p3 = t3(p1.momentum + p2.momentum)
return p3
end
function copy(process::ABCProcessDescription)
return ABCProcessDescription(copy(process.inParticles), copy(process.outParticles))
end
model(::ABCProcessDescription) = ABCModel()
model(::ABCProcessInput) = ABCModel()
function type_index_from_name(::ABCModel, name::String)
if startswith(name, "A")
return (ParticleA, parse(Int, name[2:end]))
elseif startswith(name, "B")
return (ParticleB, parse(Int, name[2:end]))
elseif startswith(name, "C")
return (ParticleC, parse(Int, name[2:end]))
else
throw("Invalid name for a particle in the ABC model")
end
end
function String(::Type{ParticleA})
return "A"
end
function String(::Type{ParticleB})
return "B"
end
function String(::Type{ParticleC})
return "C"
end
function in_particles(process::ABCProcessDescription)
return process.inParticles
end
function out_particles(process::ABCProcessDescription)
return process.outParticles
end
function get_particle(input::ABCProcessInput, t::Type{Particle}, n::Int)::Particle where {Particle}
if (t <: ParticleA)
if (n > length(input.inA))
return input.outA[n - length(input.inA)]
else
return input.inA[n]
end
elseif (t <: ParticleB)
if (n > length(input.inB))
return input.outB[n - length(input.inB)]
else
return input.inB[n]
end
elseif (t <: ParticleC)
if (n > length(input.inC))
return input.outC[n - length(input.inC)]
else
return input.inC[n]
end
end
@assert false "Invalid type given"
end

69
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@ -0,0 +1,69 @@
"""
show(io::IO, process::ABCProcessDescription)
Pretty print an [`ABCProcessDescription`](@ref) (no newlines).
```jldoctest
julia> using MetagraphOptimization
julia> print(parse_process("AB->ABBB", ABCModel()))
ABC Process: 'AB->ABBB'
```
"""
function show(io::IO, process::ABCProcessDescription)
# types() gives the types in order (ABC) instead of random like keys() would
print(io, "ABC Process: \'")
for type in types(ABCModel())
for _ in 1:get(process.inParticles, type, 0)
print(io, String(type))
end
end
print(io, "->")
for type in types(ABCModel())
for _ in 1:get(process.outParticles, type, 0)
print(io, String(type))
end
end
print(io, "'")
return nothing
end
"""
show(io::IO, processInput::ABCProcessInput)
Pretty print an [`ABCProcessInput`](@ref) (with newlines).
"""
function show(io::IO, processInput::ABCProcessInput)
println(io, "Input for $(processInput.process):")
println(io, "Incoming particles:")
if !isempty(processInput.inA)
println(io, " $(processInput.inA)")
end
if !isempty(processInput.inB)
println(io, " $(processInput.inB)")
end
if !isempty(processInput.inC)
println(io, " $(processInput.inC)")
end
println(io, "Outgoing particles:")
if !isempty(processInput.outA)
println(io, " $(processInput.outA)")
end
if !isempty(processInput.outB)
println(io, " $(processInput.outB)")
end
if !isempty(processInput.outC)
println(io, " $(processInput.outC)")
end
end
"""
show(io::IO, particle::T) where {T <: ABCParticle}
Pretty print an [`ABCParticle`](@ref) (no newlines).
"""
function show(io::IO, particle::T) where {T <: ABCParticle}
print(io, "$(String(typeof(particle))): $(particle.momentum)")
return nothing
end

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# define compute_efforts tasks computation
# put some "random" numbers here for now
compute_effort(t::ComputeTaskS1) = 10
compute_effort(t::ComputeTaskS2) = 10
compute_effort(t::ComputeTaskU) = 6
compute_effort(t::ComputeTaskV) = 20
compute_effort(t::ComputeTaskP) = 15
compute_effort(t::ComputeTaskSum) = 1
"""
compute_effort(t::ComputeTaskABC_S1)
function show(io::IO, t::DataTask)
return print(io, "Data", t.data)
Return the compute effort of an S1 task.
"""
compute_effort(t::ComputeTaskABC_S1)::Float64 = 11.0
"""
compute_effort(t::ComputeTaskABC_S2)
Return the compute effort of an S2 task.
"""
compute_effort(t::ComputeTaskABC_S2)::Float64 = 12.0
"""
compute_effort(t::ComputeTaskABC_U)
Return the compute effort of a U task.
"""
compute_effort(t::ComputeTaskABC_U)::Float64 = 1.0
"""
compute_effort(t::ComputeTaskABC_V)
Return the compute effort of a V task.
"""
compute_effort(t::ComputeTaskABC_V)::Float64 = 6.0
"""
compute_effort(t::ComputeTaskABC_P)
Return the compute effort of a P task.
"""
compute_effort(t::ComputeTaskABC_P)::Float64 = 0.0
"""
compute_effort(t::ComputeTaskABC_Sum)
Return the compute effort of a Sum task.
Note: This is a constant compute effort, even though sum scales with the number of its inputs. Since there is only ever a single sum node in a graph generated from the ABC-Model,
this doesn't matter.
"""
compute_effort(t::ComputeTaskABC_Sum)::Float64 = 1.0
"""
children(::ComputeTaskABC_S1)
Return the number of children of a ComputeTaskABC_S1 (always 1).
"""
children(::ComputeTaskABC_S1) = 1
"""
children(::ComputeTaskABC_S2)
Return the number of children of a ComputeTaskABC_S2 (always 2).
"""
children(::ComputeTaskABC_S2) = 2
"""
children(::ComputeTaskABC_P)
Return the number of children of a ComputeTaskABC_P (always 1).
"""
children(::ComputeTaskABC_P) = 1
"""
children(::ComputeTaskABC_U)
Return the number of children of a ComputeTaskABC_U (always 1).
"""
children(::ComputeTaskABC_U) = 1
"""
children(::ComputeTaskABC_V)
Return the number of children of a ComputeTaskABC_V (always 2).
"""
children(::ComputeTaskABC_V) = 2
"""
children(::ComputeTaskABC_Sum)
Return the number of children of a ComputeTaskABC_Sum.
"""
children(t::ComputeTaskABC_Sum) = t.children_number
function add_child!(t::ComputeTaskABC_Sum)
t.children_number += 1
return nothing
end
show(io::IO, t::ComputeTaskS1) = print("ComputeS1")
show(io::IO, t::ComputeTaskS2) = print("ComputeS2")
show(io::IO, t::ComputeTaskP) = print("ComputeP")
show(io::IO, t::ComputeTaskU) = print("ComputeU")
show(io::IO, t::ComputeTaskV) = print("ComputeV")
show(io::IO, t::ComputeTaskSum) = print("ComputeSum")
copy(t::DataTask) = DataTask(t.data)

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struct DataTask <: AbstractDataTask
data::UInt64
"""
ComputeTaskABC_S1 <: AbstractComputeTask
S task with a single child.
"""
struct ComputeTaskABC_S1 <: AbstractComputeTask end
"""
ComputeTaskABC_S2 <: AbstractComputeTask
S task with two children.
"""
struct ComputeTaskABC_S2 <: AbstractComputeTask end
"""
ComputeTaskABC_P <: AbstractComputeTask
P task with no children.
"""
struct ComputeTaskABC_P <: AbstractComputeTask end
"""
ComputeTaskABC_V <: AbstractComputeTask
v task with two children.
"""
struct ComputeTaskABC_V <: AbstractComputeTask end
"""
ComputeTaskABC_U <: AbstractComputeTask
u task with a single child.
"""
struct ComputeTaskABC_U <: AbstractComputeTask end
"""
ComputeTaskABC_Sum <: AbstractComputeTask
Task that sums all its inputs, n children.
"""
mutable struct ComputeTaskABC_Sum <: AbstractComputeTask
children_number::Int
end
# S task with 1 child
struct ComputeTaskS1 <: AbstractComputeTask end
"""
ABC_TASKS
# S task with 2 children
struct ComputeTaskS2 <: AbstractComputeTask end
# P task with 0 children
struct ComputeTaskP <: AbstractComputeTask end
# v task with 2 children
struct ComputeTaskV <: AbstractComputeTask end
# u task with 1 child
struct ComputeTaskU <: AbstractComputeTask end
# task that sums all its inputs, n children
struct ComputeTaskSum <: AbstractComputeTask end
ABC_TASKS = [
DataTask,
ComputeTaskS1,
ComputeTaskS2,
ComputeTaskP,
ComputeTaskV,
ComputeTaskU,
ComputeTaskSum,
]
Constant vector of all tasks of the ABC-Model.
"""
ABC_TASKS =
[ComputeTaskABC_S1, ComputeTaskABC_S2, ComputeTaskABC_P, ComputeTaskABC_V, ComputeTaskABC_U, ComputeTaskABC_Sum]

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import QEDbase.mass
import QEDbase.AbstractParticle
"""
AbstractPhysicsModel
Base type for a model, e.g. ABC-Model or QED. This is used to dispatch many functions.
"""
abstract type AbstractPhysicsModel end
"""
ParticleValue{ParticleType <: AbstractParticle}
A struct describing a particle during a calculation of a Feynman Diagram, together with the value that's being calculated. `AbstractParticle` is the type from the QEDbase package.
`sizeof(ParticleValue())` = 48 Byte
"""
struct ParticleValue{ParticleType <: AbstractParticle, ValueType}
p::ParticleType
v::ValueType
end
"""
AbstractProcessDescription
Base type for process descriptions. An object of this type of a corresponding [`AbstractPhysicsModel`](@ref) should uniquely identify a process in that model.
See also: [`parse_process`](@ref)
"""
abstract type AbstractProcessDescription end
"""
AbstractProcessInput
Base type for process inputs. An object of this type contains the input values (e.g. momenta) of the particles in a process.
See also: [`gen_process_input`](@ref)
"""
abstract type AbstractProcessInput end
"""
interaction_result(t1::Type{T1}, t2::Type{T2}) where {T1 <: AbstractParticle, T2 <: AbstractParticle}
Interface function that must be implemented for every subtype of [`AbstractParticle`](@ref), returning the result particle type when the two given particles interact.
"""
function interaction_result end
"""
types(::AbstractPhysicsModel)
Interface function that must be implemented for every subtype of [`AbstractPhysicsModel`](@ref), returning a `Vector` of the available particle types in the model.
"""
function types end
"""
in_particles(::AbstractProcessDescription)
Interface function that must be implemented for every subtype of [`AbstractProcessDescription`](@ref).
Returns a `<: Dict{Type{AbstractParticle}, Int}` object, representing the number of incoming particles for the process per particle type.
in_particles(::AbstractProcessInput)
Interface function that must be implemented for every subtype of [`AbstractProcessInput`](@ref).
Returns a `<: Vector{AbstractParticle}` object with the values of all incoming particles for the corresponding `ProcessDescription`.
"""
function in_particles end
"""
out_particles(::AbstractProcessDescription)
Interface function that must be implemented for every subtype of [`AbstractProcessDescription`](@ref).
Returns a `<: Dict{Type{AbstractParticle}, Int}` object, representing the number of outgoing particles for the process per particle type.
out_particles(::AbstractProcessInput)
Interface function that must be implemented for every subtype of [`AbstractProcessInput`](@ref).
Returns a `<: Vector{AbstractParticle}` object with the values of all outgoing particles for the corresponding `ProcessDescription`.
"""
function out_particles end
"""
get_particle(::AbstractProcessInput, t::Type, n::Int)
Interface function that must be implemented for every subtype of [`AbstractProcessInput`](@ref).
Returns the `n`th particle of type `t`.
"""
function get_particle end
"""
parse_process(::AbstractString, ::AbstractPhysicsModel)
Interface function that must be implemented for every subtype of [`AbstractPhysicsModel`](@ref).
Returns a `ProcessDescription` object.
"""
function parse_process end
"""
gen_process_input(::AbstractProcessDescription)
Interface function that must be implemented for every specific [`AbstractProcessDescription`](@ref).
Returns a randomly generated and valid corresponding `ProcessInput`.
"""
function gen_process_input end
"""
model(::AbstractProcessDescription)
model(::AbstarctProcessInput)
Return the model of this process description or input.
"""
function model end
"""
type_from_name(model::AbstractModel, name::String)
For a name of a particle in the given [`AbstractModel`](@ref), return the particle's [`Type`] and index as a tuple. The input string can be expetced to be of the form \"<name><index>\".
"""
function type_index_from_name end

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"""
show(io::IO, particleValue::ParticleValue)
Pretty print a [`ParticleValue`](@ref), no newlines.
"""
function show(io::IO, particleValue::ParticleValue)
print(io, "($(particleValue.p), value: $(particleValue.v))")
return nothing
end

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using StaticArrays
"""
compute(::ComputeTaskQED_P, data::QEDParticleValue)
Return the particle as is and initialize the Value.
"""
function compute(::ComputeTaskQED_P, data::QEDParticleValue{P}) where {P <: QEDParticle}
# TODO do we actually need this for anything?
return ParticleValue{P, DiracMatrix}(data.p, one(DiracMatrix))
end
"""
compute(::ComputeTaskQED_U, data::QEDParticleValue)
Compute an outer edge. Return the particle value with the same particle and the value multiplied by an outer_edge factor.
"""
function compute(::ComputeTaskQED_U, data::PV) where {P <: QEDParticle, PV <: QEDParticleValue{P}}
part::P = data.p
state = base_state(particle(part), direction(part), momentum(part), spin_or_pol(part))
return ParticleValue{P, typeof(state)}(
data.p,
state, # will return a SLorentzVector{ComplexF64}, BiSpinor or AdjointBiSpinor
)
end
"""
compute(::ComputeTaskQED_V, data1::QEDParticleValue, data2::QEDParticleValue)
Compute a vertex. Preserve momentum and particle types (e + gamma->p etc.) to create resulting particle, multiply values together and times a vertex factor.
"""
function compute(
::ComputeTaskQED_V,
data1::PV1,
data2::PV2,
) where {P1 <: QEDParticle, P2 <: QEDParticle, PV1 <: QEDParticleValue{P1}, PV2 <: QEDParticleValue{P2}}
p3 = QED_conserve_momentum(data1.p, data2.p)
P3 = interaction_result(P1, P2)
state = QED_vertex()
if (typeof(data1.v) <: AdjointBiSpinor)
state = data1.v * state
else
state = state * data1.v
end
if (typeof(data2.v) <: AdjointBiSpinor)
state = data2.v * state
else
state = state * data2.v
end
dataOut = ParticleValue{P3, typeof(state)}(P3(momentum(p3)), state)
return dataOut
end
"""
compute(::ComputeTaskQED_S2, data1::QEDParticleValue, data2::QEDParticleValue)
Compute a final inner edge (2 input particles, no output particle).
For valid inputs, both input particles should have the same momenta at this point.
12 FLOP.
"""
function compute(
::ComputeTaskQED_S2,
data1::ParticleValue{P1},
data2::ParticleValue{P2},
) where {P1 <: Union{AntiFermionStateful, FermionStateful}, P2 <: Union{AntiFermionStateful, FermionStateful}}
#@assert isapprox(data1.p.momentum, data2.p.momentum, rtol = sqrt(eps()), atol = sqrt(eps())) "$(data1.p.momentum) vs. $(data2.p.momentum)"
inner = QED_inner_edge(propagation_result(P1)(momentum(data1.p)))
# inner edge is just a "scalar", data1 and data2 are bispinor/adjointbispinnor, need to keep correct order
if typeof(data1.v) <: BiSpinor
return data2.v * inner * data1.v
else
return data1.v * inner * data2.v
end
end
function compute(
::ComputeTaskQED_S2,
data1::ParticleValue{P1},
data2::ParticleValue{P2},
) where {P1 <: PhotonStateful, P2 <: PhotonStateful}
# TODO: assert that data1 and data2 are opposites
inner = QED_inner_edge(data1.p)
# inner edge is just a scalar, data1 and data2 are photon states that are just Complex numbers here
return data1.v * inner * data2.v
end
"""
compute(::ComputeTaskQED_S1, data::QEDParticleValue)
Compute inner edge (1 input particle, 1 output particle).
"""
function compute(::ComputeTaskQED_S1, data::QEDParticleValue{P}) where {P <: QEDParticle}
newP = propagation_result(P)
new_p = newP(momentum(data.p))
# inner edge is just a scalar, can multiply from either side
if typeof(data.v) <: BiSpinor
return ParticleValue(new_p, QED_inner_edge(new_p) * data.v)
else
return ParticleValue(new_p, data.v * QED_inner_edge(new_p))
end
end
"""
compute(::ComputeTaskQED_Sum, data...)
compute(::ComputeTaskQED_Sum, data::AbstractArray)
Compute a sum over the vector. Use an algorithm that accounts for accumulated errors in long sums with potentially large differences in magnitude of the summands.
Linearly many FLOP with growing data.
"""
function compute(::ComputeTaskQED_Sum, data...)::ComplexF64
# TODO: want to use sum_kbn here but it doesn't seem to support ComplexF64, do it element-wise?
return sum(data)
end
function compute(::ComputeTaskQED_Sum, data::AbstractArray)::ComplexF64
# TODO: want to use sum_kbn here but it doesn't seem to support ComplexF64, do it element-wise?
return sum(data)
end

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ComputeTaskQED_Sum() = ComputeTaskQED_Sum(0)
function _svector_from_type(processDescription::QEDProcessDescription, type, particles)
if haskey(in_particles(processDescription), type)
return SVector{in_particles(processDescription)[type], type}(filter(x -> typeof(x) <: type, particles))
end
if haskey(out_particles(processDescription), type)
return SVector{out_particles(processDescription)[type], type}(filter(x -> typeof(x) <: type, particles))
end
return SVector{0, type}()
end
"""
gen_process_input(processDescription::QEDProcessDescription)
Return a ProcessInput of randomly generated [`QEDParticle`](@ref)s from a [`QEDProcessDescription`](@ref). The process description can be created manually or parsed from a string using [`parse_process`](@ref).
Note: This uses RAMBO to create a valid process with conservation of momentum and energy.
"""
function gen_process_input(processDescription::QEDProcessDescription)
massSum = 0
inputMasses = Vector{Float64}()
for (particle, n) in processDescription.inParticles
for _ in 1:n
massSum += mass(particle)
push!(inputMasses, mass(particle))
end
end
outputMasses = Vector{Float64}()
for (particle, n) in processDescription.outParticles
for _ in 1:n
massSum += mass(particle)
push!(outputMasses, mass(particle))
end
end
# add some extra random mass to allow for some momentum
massSum += rand(rng[threadid()]) * (length(inputMasses) + length(outputMasses))
particles = Vector{QEDParticle}()
initialMomenta = generate_initial_moms(massSum, inputMasses)
index = 1
for (particle, n) in processDescription.inParticles
for _ in 1:n
mom = initialMomenta[index]
push!(particles, particle(mom))
index += 1
end
end
final_momenta = generate_physical_massive_moms(rng[threadid()], massSum, outputMasses)
index = 1
for (particle, n) in processDescription.outParticles
for _ in 1:n
push!(particles, particle(final_momenta[index]))
index += 1
end
end
inFerms = _svector_from_type(processDescription, FermionStateful{Incoming, SpinUp}, particles)
outFerms = _svector_from_type(processDescription, FermionStateful{Outgoing, SpinUp}, particles)
inAntiferms = _svector_from_type(processDescription, AntiFermionStateful{Incoming, SpinUp}, particles)
outAntiferms = _svector_from_type(processDescription, AntiFermionStateful{Outgoing, SpinUp}, particles)
inPhotons = _svector_from_type(processDescription, PhotonStateful{Incoming, PolX}, particles)
outPhotons = _svector_from_type(processDescription, PhotonStateful{Outgoing, PolX}, particles)
processInput =
QEDProcessInput(processDescription, inFerms, outFerms, inAntiferms, outAntiferms, inPhotons, outPhotons)
return processInput
end
"""
gen_graph(process_description::QEDProcessDescription)
For a given [`QEDProcessDescription`](@ref), return the [`DAG`](@ref) that computes it.
"""
function gen_graph(process_description::QEDProcessDescription)
initial_diagram = FeynmanDiagram(process_description)
diagrams = gen_diagrams(initial_diagram)
graph = DAG()
COMPLEX_SIZE = sizeof(ComplexF64)
PARTICLE_VALUE_SIZE = 96.0
# TODO: Not all diagram outputs should always be summed at the end, if they differ by fermion exchange they need to be diffed
# Should not matter for n-Photon Compton processes though
sum_node = insert_node!(graph, make_node(ComputeTaskQED_Sum(0)), track = false, invalidate_cache = false)
global_data_out = insert_node!(graph, make_node(DataTask(COMPLEX_SIZE)), track = false, invalidate_cache = false)
insert_edge!(graph, sum_node, global_data_out, track = false, invalidate_cache = false)
# remember the data out nodes for connection
dataOutNodes = Dict()
for particle in initial_diagram.particles
# generate data in and U tasks
data_in = insert_node!(
graph,
make_node(DataTask(PARTICLE_VALUE_SIZE), String(particle)),
track = false,
invalidate_cache = false,
) # read particle data node
compute_u = insert_node!(graph, make_node(ComputeTaskQED_U()), track = false, invalidate_cache = false) # compute U node
data_out =
insert_node!(graph, make_node(DataTask(PARTICLE_VALUE_SIZE)), track = false, invalidate_cache = false) # transfer data out from u (one ABCParticleValue object)
insert_edge!(graph, data_in, compute_u, track = false, invalidate_cache = false)
insert_edge!(graph, compute_u, data_out, track = false, invalidate_cache = false)
# remember the data_out node for future edges
dataOutNodes[String(particle)] = data_out
end
#dataOutBackup = copy(dataOutNodes)
for diagram in diagrams
# the intermediate (virtual) particles change across
#dataOutNodes = copy(dataOutBackup)
tie = diagram.tie[]
# handle the vertices
for vertices in diagram.vertices
for vertex in vertices
data_in1 = dataOutNodes[String(vertex.in1)]
data_in2 = dataOutNodes[String(vertex.in2)]
compute_V = insert_node!(graph, make_node(ComputeTaskQED_V()), track = false, invalidate_cache = false) # compute vertex
insert_edge!(graph, data_in1, compute_V, track = false, invalidate_cache = false)
insert_edge!(graph, data_in2, compute_V, track = false, invalidate_cache = false)
data_V_out = insert_node!(
graph,
make_node(DataTask(PARTICLE_VALUE_SIZE)),
track = false,
invalidate_cache = false,
)
insert_edge!(graph, compute_V, data_V_out, track = false, invalidate_cache = false)
if (vertex.out == tie.in1 || vertex.out == tie.in2)
# out particle is part of the tie -> there will be an S2 task with it later, don't make S1 task
dataOutNodes[String(vertex.out)] = data_V_out
continue
end
# otherwise, add S1 task
compute_S1 =
insert_node!(graph, make_node(ComputeTaskQED_S1()), track = false, invalidate_cache = false) # compute propagator
insert_edge!(graph, data_V_out, compute_S1, track = false, invalidate_cache = false)
data_S1_out = insert_node!(
graph,
make_node(DataTask(PARTICLE_VALUE_SIZE)),
track = false,
invalidate_cache = false,
)
insert_edge!(graph, compute_S1, data_S1_out, track = false, invalidate_cache = false)
# overrides potentially different nodes from previous diagrams, which is intentional
dataOutNodes[String(vertex.out)] = data_S1_out
end
end
# handle the tie
data_in1 = dataOutNodes[String(tie.in1)]
data_in2 = dataOutNodes[String(tie.in2)]
compute_S2 = insert_node!(graph, make_node(ComputeTaskQED_S2()), track = false, invalidate_cache = false)
data_S2 = insert_node!(graph, make_node(DataTask(PARTICLE_VALUE_SIZE)), track = false, invalidate_cache = false)
insert_edge!(graph, data_in1, compute_S2, track = false, invalidate_cache = false)
insert_edge!(graph, data_in2, compute_S2, track = false, invalidate_cache = false)
insert_edge!(graph, compute_S2, data_S2, track = false, invalidate_cache = false)
insert_edge!(graph, data_S2, sum_node, track = false, invalidate_cache = false)
add_child!(task(sum_node))
end
return graph
end

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import Base.copy
import Base.hash
import Base.==
import Base.show
"""
FeynmanParticle
Representation of a particle for use in [`FeynmanDiagram`](@ref)s. Consist of the [`QEDParticle`](@ref) type and an id.
"""
struct FeynmanParticle
particle::Type{<:QEDParticle}
id::Int
end
"""
FeynmanVertex
Representation of a vertex in a [`FeynmanDiagram`](@ref). Stores two input [`FeynmanParticle`](@ref)s and one output.
"""
struct FeynmanVertex
in1::FeynmanParticle
in2::FeynmanParticle
out::FeynmanParticle
end
"""
FeynmanTie
Representation of a "tie" in a [`FeynmanDiagram`](@ref). A tie ties two virtual particles in a diagram together and thus represent an inner line of the diagram. Not all inner lines are [`FeynmanTie`](@ref)s, in fact, a connected diagram only ever has exactly one tie.
"""
struct FeynmanTie
in1::FeynmanParticle
in2::FeynmanParticle
end
"""
FeynmanDiagram
Representation of a feynman diagram. It consists of its initial input/output particles, and a vector of sets of [`FeynmanVertex`](@ref)s. The vertices are to be applied level by level.
A [`FeynmanVertex`](@ref) will always be at the lowest level possible, i.e. the lowest level at which all input particles for it exist.
The [`FeynmanTie`](@ref) represents the final inner edge of the diagram.
"""
struct FeynmanDiagram
vertices::Vector{Set{FeynmanVertex}}
tie::Ref{Union{FeynmanTie, Missing}}
particles::Vector{FeynmanParticle}
type_ids::Dict{Type, Int64} # lut for number of used ids for a particle type
end
"""
FeynmanDiagram(pd::QEDProcessDescription)
Create an initial [`FeynmanDiagram`](@ref) with only its initial particles set and no vertices or ties.
Use [`gen_diagrams`](@ref) to generate all possible diagrams from this one.
"""
function FeynmanDiagram(pd::QEDProcessDescription)
parts = Vector{FeynmanParticle}()
for (type, n) in pd.inParticles
for i in 1:n
push!(parts, FeynmanParticle(type, i))
end
end
for (type, n) in pd.outParticles
for i in 1:n
push!(parts, FeynmanParticle(type, i))
end
end
ids = Dict{Type, Int64}()
for t in types(QEDModel())
if (isincoming(t))
ids[t] = get(pd.inParticles, t, 0)
else
ids[t] = get(pd.outParticles, t, 0)
end
end
return FeynmanDiagram([], missing, parts, ids)
end
function particle_after_tie(p::FeynmanParticle, t::FeynmanTie)
if p == t.in1 || p == t.in2
return FeynmanParticle(FermionStateful{Incoming, SpinUp}, -1) # placeholder particle and id for tied particles
end
return p
end
function vertex_after_tie(v::FeynmanVertex, t::FeynmanTie)
return FeynmanVertex(particle_after_tie(v.in1, t), particle_after_tie(v.in2, t), particle_after_tie(v.out, t))
end
function vertex_after_tie(v::FeynmanVertex, t::Missing)
return v
end
function vertex_set_after_tie(vs::Set{FeynmanVertex}, t::FeynmanTie)
return Set{FeynmanVertex}(vertex_after_tie(v, t) for v in vs)
end
function vertex_set_after_tie(vs::Set{FeynmanVertex}, t::Missing)
return vs
end
function vertex_set_after_tie(vs::Set{FeynmanVertex}, t1::Union{FeynmanTie, Missing}, t2::Union{FeynmanTie, Missing})
return Set{FeynmanVertex}(vertex_after_tie(vertex_after_tie(v, t1), t2) for v in vs)
end
"""
String(p::FeynmanParticle)
Return a string representation of the [`FeynmanParticle`](@ref) in a format that is readable by [`type_index_from_name`](@ref).
"""
function String(p::FeynmanParticle)
return "$(String(p.particle))$(String(direction(p.particle)))$(p.id)"
end
function hash(v::FeynmanVertex)
return hash(v.in1) * hash(v.in2)
end
function hash(t::FeynmanTie)
return hash(t.in1) * hash(t.in2)
end
function hash(d::FeynmanDiagram)
return hash((d.vertices, d.particles))
end
function ==(v1::FeynmanVertex, v2::FeynmanVertex)
return (v1.in1 == v2.in1 && v1.in2 == v2.in1) || (v1.in2 == v2.in1 && v1.in1 == v2.in2)
end
function ==(t1::FeynmanTie, t2::FeynmanTie)
return (t1.in1 == t2.in1 && t1.in2 == t2.in1) || (t1.in2 == t2.in1 && t1.in1 == t2.in2)
end
function ==(d1::FeynmanDiagram, d2::FeynmanDiagram)
if (!ismissing(d1.tie[]) && ismissing(d2.tie[])) || (ismissing(d1.tie[]) && !ismissing(d2.tie[]))
return false
end
if d1.particles != d2.particles
return false
end
if length(d1.vertices) != length(d2.vertices)
return false
end
# TODO can i prove that this works?
for (v1, v2) in zip(d1.vertices, d2.vertices)
if vertex_set_after_tie(v1, d1.tie[], d2.tie[]) != vertex_set_after_tie(v2, d1.tie[], d2.tie[])
return false
end
end
return true
#=return isequal.(
vertex_set_after_tie(d1.vertices, d1.tie, d2.tie),
vertex_set_after_tie(d2.vertices, d1.tie, d2.tie),
)=#
end
copy(fd::FeynmanDiagram) =
FeynmanDiagram(deepcopy(fd.vertices), copy(fd.tie[]), deepcopy(fd.particles), copy(fd.type_ids))
"""
id_for_type(d::FeynmanDiagram, t::Type{<:QEDParticle})
Return the highest id of any particle of the given type in the diagram + 1.
"""
function id_for_type(d::FeynmanDiagram, t::Type{<:QEDParticle})
return d.type_ids[t] + 1
end
"""
can_apply_vertex(particles::Vector{FeynmanParticle}, vertex::FeynmanVertex)
Return true if the given [`FeynmanVertex`](@ref) can be applied to the given particles, i.e. both input particles of the vertex are in the vector and the output particle is not.
"""
function can_apply_vertex(particles::Vector{FeynmanParticle}, vertex::FeynmanVertex)
return vertex.in1 in particles && vertex.in2 in particles && !(vertex.out in particles)
end
"""
apply_vertex!(particles::Vector{FeynmanParticle}, vertex::FeynmanVertex)
Apply a [`FeynmanVertex`](@ref) to the given vector of [`FeynmanParticle`](@ref)s.
"""
function apply_vertex!(particles::Vector{FeynmanParticle}, vertex::FeynmanVertex)
#@assert can_apply_vertex(particles, vertex)
length_before = length(particles)
filter!(x -> x != vertex.in1 && x != vertex.in2, particles)
push!(particles, vertex.out)
#@assert length(particles) == length_before - 1
return nothing
end
"""
can_apply_tie(particles::Vector{FeynmanParticle}, tie::FeynmanTie)
Return true if the given [`FeynmanTie`](@ref) can be applied to the given particles, i.e. both input particles of the tie are in the vector.
"""
function can_apply_tie(particles::Vector{FeynmanParticle}, tie::FeynmanTie)
return tie.in1 in particles && tie.in2 in particles
end
"""
apply_tie!(particles::Vector{FeynmanParticle}, tie::FeynmanTie)
Apply a [`FeynmanTie`](@ref) to the given vector of [`FeynmanParticle`](@ref)s.
"""
function apply_tie!(particles::Vector{FeynmanParticle}, tie::FeynmanTie)
@assert length(particles) == 2
@assert can_apply_tie(particles, tie)
@assert can_tie(tie.in1.particle, tie.in2.particle)
empty!(particles)
@assert length(particles) == 0
return nothing
end
function apply_tie!(::Vector{FeynmanParticle}, ::Missing)
return nothing
end
"""
get_particles(fd::FeynmanDiagram, level::Int)
Return a vector of the particles after applying the vertices and tie of the diagram up to the given level. If no level is given, apply all. The tie comes last and is its own "level".
"""
function get_particles(fd::FeynmanDiagram, level::Int = -1)
if level == -1
level = length(fd.vertices) + 1
end
working_particles = copy(fd.particles)
for l in 1:length(fd.vertices)
if l > level
break
end
for v in fd.vertices[l]
apply_vertex!(working_particles, v)
end
end
if (level > length(fd.vertices))
apply_tie!(working_particles, fd.tie[])
end
return working_particles
end
"""
add_vertex!(fd::FeynmanDiagram, vertex::FeynmanVertex)
Add the given vertex to the diagram, at the earliest level possible.
"""
function add_vertex!(fd::FeynmanDiagram, vertex::FeynmanVertex)
for i in eachindex(fd.vertices)
if (can_apply_vertex(get_particles(fd, i - 1), vertex))
push!(fd.vertices[i], vertex)
fd.type_ids[vertex.out.particle] += 1
return nothing
end
end
if !can_apply_vertex(get_particles(fd), vertex)
#@assert false "Can't add vertex $vertex to diagram"
end
push!(fd.vertices, Set{FeynmanVertex}())
push!(fd.vertices[end], vertex)
fd.type_ids[vertex.out.particle] += 1
return nothing
end
"""
add_vertex(fd::FeynmanDiagram, vertex::FeynmanVertex)
Add the given vertex to the diagram, at the earliest level possible. Return the new diagram without muting the given one.
"""
function add_vertex(fd::FeynmanDiagram, vertex::FeynmanVertex)
newfd = copy(fd)
add_vertex!(newfd, vertex)
return newfd
end
"""
add_tie!(fd::FeynmanDiagram, tie::FeynmanTie)
Add the given tie to the diagram, always at the last level.
"""
function add_tie!(fd::FeynmanDiagram, tie::FeynmanTie)
if !can_apply_tie(get_particles(fd), tie)
@assert false "Can't add tie $tie to diagram"
end
fd.tie[] = tie
#=
@assert length(fd.vertices) >= 2
#if the last vertex is involved in the tie and alone, lower it one level down
if (length(fd.vertices[end]) != 1)
return nothing
end
vert = fd.vertices[end][1]
if (vert != vertex_after_tie(vert, tie))
return nothing
end
pop!(fd.vertices)
push!(fd.vertices[end], vert)
=#
return nothing
end
"""
add_tie(fd::FeynmanDiagram, tie::FeynmanTie)
Add the given tie to the diagram, at the earliest level possible. Return the new diagram without muting the given one.
"""
function add_tie(fd::FeynmanDiagram, tie::FeynmanTie)
newfd = copy(fd)
add_tie!(newfd, tie)
return newfd
end
"""
isvalid(fd::FeynmanDiagram)
Return whether the given diagram is valid. A diagram is valid iff the following are true:
- After applying all vertices and the tie, there are no more particles left
- The diagram is connected
"""
function isvalid(fd::FeynmanDiagram)
if ismissing(fd.tie[])
# diagram is connected iff there is one tie
return false
end
if get_particles(fd) != []
return false
end
return true
end
"""
possible_vertices(fd::FeynmanDiagram)
Return a vector of all possible vertices that can be applied to the diagram at its current state.
"""
function possible_vertices(fd::FeynmanDiagram)
possibilities = Vector{FeynmanVertex}()
fully_generated_particles = get_particles(fd)
min_level = max(0, length(fd.vertices) - 1)
for l in min_level:length(fd.vertices)
particles = get_particles(fd, l)
for i in 1:length(particles)
for j in (i + 1):length(particles)
p1 = particles[i]
p2 = particles[j]
if (caninteract(p1.particle, p2.particle))
interaction_res = propagation_result(interaction_result(p1.particle, p2.particle))
v = FeynmanVertex(p1, p2, FeynmanParticle(interaction_res, id_for_type(fd, interaction_res)))
#@assert !(v.out in particles) "$v is in $fd"
if !can_apply_vertex(fully_generated_particles, v)
continue
end
push!(possibilities, v)
end
end
end
if (!isempty(possibilities))
return possibilities
end
end
return possibilities
end
"""
can_tie(p1::Type, p2::Type)
For two given [`QEDParitcle`](@ref) types, return whether they can be tied together.
They can be tied iff one is the [`propagation_result`](@ref) of the other, or if both are photons, in which case their direction does not matter.
"""
function can_tie(p1::Type, p2::Type)
if p1 == propagation_result(p2)
return true
end
if (p1 <: PhotonStateful && p2 <: PhotonStateful)
return true
end
return false
end
"""
possible_tie(fd::FeynmanDiagram)
Return a possible tie or `missing` for the diagram at its current state.
"""
function possible_tie(fd::FeynmanDiagram)
particles = get_particles(fd)
if (length(particles) != 2)
return missing
end
if (particles[1] in fd.particles || particles[2] in fd.particles)
return missing
end
tie = FeynmanTie(particles[1], particles[2])
if (can_apply_tie(particles, tie))
return tie
end
return missing
end
function remove_duplicates(compare_set::Set{FeynmanDiagram})
result = Set()
while !isempty(compare_set)
x = pop!(compare_set)
# we know there will only be one duplicate if any, so search for that and delete it
for y in compare_set
if x == y
delete!(compare_set, y)
break
end
end
push!(result, x)
end
return result
end
"""
gen_diagrams(fd::FeynmanDiagram)
From a given feynman diagram in its initial state, e.g. when created through the [`FeynmanDiagram(pd::ProcessDescription)`](@ref) constructor, generate and return all possible [`FeynmanDiagram`](@ref)s that describe that process.
"""
function gen_diagrams(fd::FeynmanDiagram)
working = Set{FeynmanDiagram}()
results = Set{FeynmanDiagram}()
push!(working, fd)
# we know there will be particle_number - 2 vertices, followed by 1 tie
n_particles = length(fd.particles)
n_vertices = n_particles - 2
# doing this in iterations should reduce the intermediate number of diagrams by hash collisions
for _ in 1:n_vertices
next_iter_set = Set{FeynmanDiagram}()
while !isempty(working)
d = pop!(working)
possibilities = possible_vertices(d)
for v in possibilities
push!(next_iter_set, add_vertex(d, v))
end
end
working = next_iter_set
end
# add the tie
for d in working
tie = possible_tie(d)
if ismissing(tie)
continue
end
add_tie!(d, tie)
if isvalid(d)
push!(results, d)
end
end
return remove_duplicates(results)
end

44
src/models/qed/parse.jl Normal file
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@ -0,0 +1,44 @@
"""
parse_process(string::AbstractString, model::QEDModel)
Parse a string representation of a process, such as "ke->ke" into the corresponding [`QEDProcessDescription`](@ref).
"""
function parse_process(str::AbstractString, model::QEDModel)
inParticles = Dict{Type, Int}()
outParticles = Dict{Type, Int}()
if !(contains(str, "->"))
throw("Did not find -> while parsing process \"$str\"")
end
(inStr, outStr) = split(str, "->")
if (isempty(inStr) || isempty(outStr))
throw("Process (\"$str\") input or output part is empty!")
end
for t in types(model)
if (isincoming(t))
inCount = count(x -> x == String(t)[1], inStr)
if inCount != 0
inParticles[t] = inCount
end
end
if (isoutgoing(t))
outCount = count(x -> x == String(t)[1], outStr)
if outCount != 0
outParticles[t] = outCount
end
end
end
if length(inStr) != sum(values(inParticles))
throw("Encountered unknown characters in the input part of process \"$str\"")
elseif length(outStr) != sum(values(outParticles))
throw("Encountered unknown characters in the output part of process \"$str\"")
end
return QEDProcessDescription(inParticles, outParticles)
end

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