25 Commits

Author SHA1 Message Date
43e1866988 Add docs for optmization 2023-11-22 02:06:40 +01:00
4d1dc27f4f Improve operation/optimization performance 2023-11-21 21:33:54 +01:00
968f6856de Improve interface, add random walk and reduction implementations, add tests 2023-11-21 20:22:53 +01:00
7d31f61e5f More fun with type stability and comment out some asserts that take 95% of the time 2023-11-21 15:38:35 +01:00
705bfb30fe Fun with type stability 2023-11-21 01:48:59 +01:00
9d947a49ce Add description of necessities of cost_type 2023-11-20 19:08:40 +01:00
1e0e699e6d Add Optimization interface, add greedy optimizer, add some functionality to CDCost 2023-11-20 19:07:05 +01:00
c73053f991 Add iterator for PossibleOperations data structure 2023-11-20 16:56:42 +01:00
992450374c Fix operations and estimator tests 2023-11-20 14:37:35 +01:00
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
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
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
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
62791ab422 Fix docs
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2023-09-17 12:40:11 +02:00
4c452dce98 Add execution test 2023-09-17 10:32:43 +02:00
27c4b8ba34 Use real ABC-Model compute effort and data transfer numbers 2023-09-07 18:46:41 +02:00
e59d24ebe5 Add code gen documentation 2023-09-07 18:23:36 +02:00
d1666de432 Add accurate arithmetic for summation, fix order of input particles 2023-09-07 16:49:44 +02:00
0f78053ccf Fix topoligical ordering on the graph 2023-09-05 12:14:41 +02:00
7a1a97dac8 Add basic execution function 2023-09-01 16:22:16 +02:00
f1edce258a Start adding code generation 2023-08-31 18:24:48 +02:00
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
e09ab7c77b Add tests 2023-08-29 13:09:33 +02:00
7387fa86b1 Add GraphProperties and property caching 2023-08-29 13:08:02 +02:00
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
114 changed files with 6515 additions and 856 deletions

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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

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@ -1,34 +1,114 @@
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
prepare:
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'
# needed for the file hashing, should be removed when ${{ hashFiles('**/Project.toml') }} is supported in gitea
- name: Setup go environment
uses: actions/setup-go@v3
with:
go-version: '1.20'
- name: Hash files
uses: https://gitea.com/actions/go-hashfiles@v0.0.1
id: get-hash
with:
patterns: |-
**/Project.toml
- name: Restore Cache
uses: actions/cache/restore@v3
id: cache-restore
with:
path: |
.julia/artifacts
.julia/packages
.julia/registries
key: julia-${{ steps.get-hash.outputs.hash }}
- name: Check cache hit
if: steps.cache-restore.outputs.cache-hit == 'true'
run: exit 0
- name: Install dependencies
run: julia --project=./ -e 'import Pkg; Pkg.instantiate()'
run: |
julia --project=./ -e 'import Pkg; Pkg.instantiate(); Pkg.precompile()'
julia --project=examples/ -e 'import Pkg; Pkg.develop(Pkg.PackageSpec(path=pwd())); Pkg.instantiate(); Pkg.precompile()'
julia --project=docs/ -e 'import Pkg; Pkg.develop(Pkg.PackageSpec(path=pwd())); Pkg.instantiate(); Pkg.precompile()'
- name: Cache Julia packages
uses: actions/cache/save@v3
with:
path: |
.julia/artifacts
.julia/packages
.julia/registries
key: julia-${{ steps.get-hash.outputs.hash }}
test:
needs: prepare
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'
# needed for the file hashing, should be removed when ${{ hashFiles('**/Project.toml') }} is supported in gitea
- name: Setup go environment
uses: actions/setup-go@v3
with:
go-version: '1.20'
- name: Hash files
uses: https://gitea.com/actions/go-hashfiles@v0.0.1
id: get-hash
with:
patterns: |-
**/Project.toml
- name: Restore cached Julia packages
uses: actions/cache/restore@v3
with:
path: |
.julia/artifacts
.julia/packages
.julia/registries
key: julia-${{ steps.get-hash.outputs.hash }}
- name: Install dependencies
run: |
julia --project=./ -e 'import Pkg; Pkg.instantiate(); Pkg.precompile()'
julia --project=examples/ -e 'import Pkg; Pkg.develop(Pkg.PackageSpec(path=pwd())); Pkg.instantiate(); Pkg.precompile()'
julia --project=docs/ -e 'import Pkg; Pkg.develop(Pkg.PackageSpec(path=pwd())); Pkg.instantiate(); Pkg.precompile()'
- 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 == ""
@ -43,4 +123,63 @@ jobs:
run: julia --project=./ -t 4 -e 'import 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/ -t 4 -e 'include("examples/import_bench.jl")' -O3
docs:
needs: prepare
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'
# needed for the file hashing, should be removed when ${{ hashFiles('**/Project.toml') }} is supported in gitea
- name: Setup go environment
uses: actions/setup-go@v3
with:
go-version: '1.20'
- name: Hash files
uses: https://gitea.com/actions/go-hashfiles@v0.0.1
id: get-hash
with:
patterns: |-
**/Project.toml
- name: Restore cached Julia packages
uses: actions/cache/restore@v3
with:
path: |
.julia/artifacts
.julia/packages
.julia/registries
key: julia-${{ steps.get-hash.outputs.hash }}
- name: Install dependencies
run: |
julia --project=./ -e 'import Pkg; Pkg.instantiate(); Pkg.precompile()'
julia --project=examples/ -e 'import Pkg; Pkg.develop(Pkg.PackageSpec(path=pwd())); Pkg.instantiate(); Pkg.precompile()'
julia --project=docs/ -e 'import Pkg; Pkg.develop(Pkg.PackageSpec(path=pwd())); Pkg.instantiate(); Pkg.precompile()'
- name: Build docs
run: 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 }}

2
.gitignore vendored
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@ -26,3 +26,5 @@ Manifest.toml
# vscode workspace directory
.vscode
.julia
**/.ipynb_checkpoints/

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@ -4,10 +4,16 @@ 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"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Roots = "f2b01f46-fcfa-551c-844a-d8ac1e96c665"
UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
[extras]

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@ -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
@ -53,7 +53,7 @@ For graphs AB->AB^n:
- Number of ComputeTaskS2 should always be (n+1)!
- Number of ComputeTaskU 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

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",
],
)

3
docs/src/contribution.md Normal file
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@ -0,0 +1,3 @@
# Contribution
This is currently in development for a diploma thesis and is therefore private and impossible to contribute to.

75
docs/src/flowchart.drawio Normal file
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@ -0,0 +1,75 @@
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26
docs/src/index.md Normal file
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@ -0,0 +1,26 @@
# 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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@ -0,0 +1,8 @@
# Code Generation
## Main
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["code_gen/main.jl"]
Order = [:function]
```

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@ -0,0 +1,59 @@
# 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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@ -0,0 +1,22 @@
# 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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@ -0,0 +1,21 @@
# 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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@ -0,0 +1,50 @@
# 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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@ -0,0 +1,72 @@
# 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
*To be added*

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@ -0,0 +1,43 @@
# 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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@ -0,0 +1,41 @@
# 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 +1,15 @@
# Scheduler
## Interface
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["scheduler/interface.jl"]
Order = [:type, :function]
```
## Greedy
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["scheduler/greedy.jl"]
Order = [:type, :function]
```

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@ -0,0 +1,43 @@
# 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]
```
## Print
```@autodocs
Modules = [MetagraphOptimization]
Pages = ["task/print.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]
```

24
docs/src/lib/public.md Normal file
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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"]
```

7
docs/src/manual.md Normal file
View File

@ -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.

View File

@ -1,7 +1,3 @@
[deps]
BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
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"

33
examples/ab5.jl Normal file
View File

@ -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))

33
examples/ab7.jl Normal file
View File

@ -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->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))

View File

@ -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: ")

View File

@ -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()

View File

@ -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)

View File

@ -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

Binary file not shown.

View File

@ -0,0 +1,636 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"using MetagraphOptimization"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1 NUMA nodes\n",
"CUDA is non-functional\n"
]
}
],
"source": [
"# Get machine and set dictionary caching strategy\n",
"machine = get_machine_info()\n",
"MetagraphOptimization.set_cache_strategy(machine.devices[1], MetagraphOptimization.Dictionary())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Graph:\n",
" Nodes: Total: 7854, ComputeTaskP: 8, ComputeTaskS2: 720, \n",
" ComputeTaskU: 8, ComputeTaskSum: 1, ComputeTaskS1: 1230, \n",
" ComputeTaskV: 1956, DataTask: 3931\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": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"compute__ae7097a4_7bfc_11ee_2cec_190d7ced64f1 (generic function with 1 method)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"compute_AB_AB5 = get_compute_function(graph, process, machine)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.140021 seconds (791.41 k allocations: 30.317 MiB, 9.74% gc time)\n",
"Graph:\n",
" Nodes: Total: 4998, ComputeTaskP: 8, ComputeTaskS2: 720, \n",
" ComputeTaskU: 8, ComputeTaskSum: 1, ComputeTaskS1: 516, \n",
" ComputeTaskV: 1242, DataTask: 2503\n",
" Edges: 7671\n",
" Total Compute Effort: 21777.0\n",
" Total Data Transfer: 219648.0\n",
" Total Compute Intensity: 0.09914499562937062\n"
]
}
],
"source": [
"@time optimize_to_fixpoint!(ReductionOptimizer(), graph)\n",
"print(graph)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 3.626740 seconds (1.52 M allocations: 114.358 MiB, 0.84% gc time)\n"
]
},
{
"data": {
"text/plain": [
"compute__bad8f2ac_7bfc_11ee_176b_b72dc8919aad (generic function with 1 method)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@time compute_AB_AB5_reduced = get_compute_function(graph, process, machine)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 2.130952 seconds (4.31 M allocations: 276.129 MiB, 4.50% gc time, 99.02% compilation time)\n"
]
},
{
"data": {
"text/plain": [
"1000-element Vector{ABCProcessInput}:\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [4.694213004647641, 0.0, 0.0, 4.58646222408983]\n",
" B: [4.694213004647641, 0.0, 0.0, -4.58646222408983]\n",
" 6 Outgoing Particles:\n",
" A: [-1.1989656045893697, -0.40235742161696864, 0.06512533692021122, 0.5209469423550988]\n",
" B: [-1.2555060342925868, 0.3685683194051901, 0.4785890883121294, -0.4597882997907804]\n",
" B: [-2.189083660521547, 0.31663070338411387, 0.1742479621961443, -1.9134967776579581]\n",
" B: [-1.0637129314000269, -0.2948512505337184, 0.0500740340487307, -0.2050378784528044]\n",
" B: [-1.6149410305664367, 1.0344652685816964, -0.406159957064284, 0.6106965118475143]\n",
" B: [-2.0662167479253144, -1.0224556192203134, -0.3618764644129321, 1.4466795016989296]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [5.621657834589244, 0.0, 0.0, 5.532001157736559]\n",
" B: [5.621657834589244, 0.0, 0.0, -5.532001157736559]\n",
" 6 Outgoing Particles:\n",
" A: [-2.058801595505931, 0.7220299456693885, 0.22719930902793095, 1.6327024349806234]\n",
" B: [-1.1826215869997767, 0.04638669502532437, -0.553508153090363, -0.30011800516629]\n",
" B: [-2.3776830758041227, -0.8637209881441633, -0.22710813067439403, 1.9636152272240621]\n",
" B: [-1.9086249240920268, 0.02598092498567318, -1.087715954825374, -1.2079106316365085]\n",
" B: [-2.6526208210236426, 0.3117066248738638, 1.6178469805428013, -1.8225826038033035]\n",
" B: [-1.0629636657529868, -0.24238320241008685, 0.023285949019398133, -0.2657064215985837]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [6.176284774018432, 0.0, 0.0, 6.094792335245879]\n",
" B: [6.176284774018432, 0.0, 0.0, -6.094792335245879]\n",
" 6 Outgoing Particles:\n",
" A: [-3.2943110238771185, 1.9799744259594443, 2.3805040294128346, 0.5151572192390796]\n",
" B: [-1.0255775134941767, 0.18009906891836583, -0.12779691496180498, 0.05514988745120904]\n",
" B: [-1.7854209452644407, -0.56381615584479, -0.9572322565407875, 0.9764966468120639]\n",
" B: [-3.3312939695760786, -0.5949754252793171, -2.9420979921841868, -1.0428725518649993]\n",
" B: [-1.6551651824618003, -0.8748451354288965, 0.9749427327758187, -0.1539624566503731]\n",
" B: [-1.260800913363249, -0.12643677832480643, 0.6716804014981268, -0.34996874498697933]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [4.747497785190141, 0.0, 0.0, 4.640984294348053]\n",
" B: [4.747497785190141, 0.0, 0.0, -4.640984294348053]\n",
" 6 Outgoing Particles:\n",
" A: [-1.3704329562088802, 0.8292801285050307, 0.2251475790952209, 0.3737506167990253]\n",
" B: [-1.352958681672649, 0.11120507604905326, 0.6088733084867489, -0.6688825902852584]\n",
" B: [-1.4224569379606473, -0.25277059018918374, -0.4925475402927904, -0.84669220478242]\n",
" B: [-2.4534584066229996, -0.23638988525842838, -1.4120549440785204, 1.7232756047945383]\n",
" B: [-1.4378719974624208, 0.5461758322111039, 0.8131489669135029, -0.3285674953530594]\n",
" B: [-1.457816590452685, -0.9975005613175758, 0.257432629875838, -0.25288393117282576]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [6.148648417619223, 0.0, 0.0, 6.066784763240853]\n",
" B: [6.148648417619223, 0.0, 0.0, -6.066784763240853]\n",
" 6 Outgoing Particles:\n",
" A: [-1.5381168736188293, 0.5769721565317305, 1.0069443436143835, 0.13773066601554382]\n",
" B: [-1.3178580311796126, 0.27781510267038506, -0.8083323925420551, 0.07853217328003184]\n",
" B: [-1.5330954954905804, 0.4994081736550063, -1.0290017953406905, 0.20525247761163526]\n",
" B: [-3.083592979398096, -2.1497728433794587, -1.2247634566690573, -1.5449844205264607]\n",
" B: [-3.1391572693216845, 0.49043306139044257, 2.931865230552653, 0.13397777318202247]\n",
" B: [-1.6854761862296446, 0.30514434913189475, -0.876711929615233, 0.989491330437227]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [7.422637433466136, 0.0, 0.0, 7.35496746890785]\n",
" B: [7.422637433466136, 0.0, 0.0, -7.35496746890785]\n",
" 6 Outgoing Particles:\n",
" A: [-3.3788591199517355, 2.3069724486616927, -0.5016400230094518, 2.2006645271171985]\n",
" B: [-2.193241133599192, -1.652465184572841, -0.691853387986234, -0.7752447184070871]\n",
" B: [-2.295315825041209, 0.334376552772819, 0.5374003175214306, 1.966689593293318]\n",
" B: [-2.3721558149969235, -2.0813404180022568, 0.4923496733367945, 0.22964554029865022]\n",
" B: [-1.5367714331999278, 0.9008878309070798, 0.1482895506792473, -0.7266895920420517]\n",
" B: [-3.068931540143284, 0.1915687702335065, 0.015453869458212284, -2.8950653502600274]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [6.716486802754837, 0.0, 0.0, 6.64162592830851]\n",
" B: [6.716486802754837, 0.0, 0.0, -6.64162592830851]\n",
" 6 Outgoing Particles:\n",
" A: [-1.3263331205917814, -0.5023870926274977, 0.418137178911541, 0.5761319775467438]\n",
" B: [-2.1603199304697136, -1.202627416523187, 1.024176720111292, -1.0824654936733602]\n",
" B: [-1.1665818595303201, 0.5747508534091106, 0.05041215840441908, 0.16743149576984034]\n",
" B: [-1.829760754209137, 0.5127529745920416, -0.17835468593467171, -1.4329334983509001]\n",
" B: [-2.891550940379351, -2.652621236308268, 0.3953841214715819, 0.41029113320086874]\n",
" B: [-4.05842700032937, 3.2701319174577996, -1.7097554929641623, 1.3615443855068068]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [7.700331598721008, 0.0, 0.0, 7.635123229539995]\n",
" B: [7.700331598721008, 0.0, 0.0, -7.635123229539995]\n",
" 6 Outgoing Particles:\n",
" A: [-2.382743739041896, -1.410381415274026, 1.0613871843128353, 1.2496996576655786]\n",
" B: [-3.021630369232257, 0.25595209564405125, -2.8389223073732714, 0.07251720968504605]\n",
" B: [-2.7262381500229256, 1.0736489469437192, 2.293577756890956, 0.13839603484966886]\n",
" B: [-2.222260574660266, 1.5432031708495264, -0.7055857379280247, 1.0291330339668954]\n",
" B: [-1.650055097318715, -1.062833285640475, -0.34598865120359784, 0.6880109623839291]\n",
" B: [-3.397735267165956, -0.3995895125227963, 0.5355317553011019, -3.1777568985511193]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [4.9341647451125334, 0.0, 0.0, 4.8317679716550375]\n",
" B: [4.9341647451125334, 0.0, 0.0, -4.8317679716550375]\n",
" 6 Outgoing Particles:\n",
" A: [-1.834221818900379, 0.1070495973399568, 1.2695354794210922, 0.860923766155068]\n",
" B: [-1.5116322118250454, 0.39753882899610743, -0.756426277560466, -0.7448584495617266]\n",
" B: [-1.6588475476725886, 0.06712527283179799, 0.6875031760830096, -1.1289857249063835]\n",
" B: [-1.5718164783029667, 0.4294130824657117, -0.6215317131811225, -0.9486357444151968]\n",
" B: [-1.7838526603309615, -0.5732435925039472, -0.9425541080554634, 0.9824020820472578]\n",
" B: [-1.5079587731931232, -0.4278831891296266, 0.36347344329295106, 0.979154070680981]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [7.099667747066588, 0.0, 0.0, 7.028889109862067]\n",
" B: [7.099667747066588, 0.0, 0.0, -7.028889109862067]\n",
" 6 Outgoing Particles:\n",
" A: [-3.851129225519823, 2.5555470019017212, -2.502060728335724, 1.019837214678957]\n",
" B: [-2.3860288930086897, 0.6059782347076652, 0.6711053982516709, 1.9686395814801452]\n",
" B: [-1.9543999030878276, -1.5857282951514855, 0.5255033921941499, -0.17026726032362857]\n",
" B: [-1.5523812781985644, -1.154244859738803, 0.03484928145183679, -0.2763909626783212]\n",
" B: [-3.2795110937910716, -1.0290377989842119, 1.3607888704851536, -2.616204860580336]\n",
" B: [-1.175885100527199, 0.6074857172651138, -0.09018621404708665, 0.07438628742318319]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [6.3653048194550985, 0.0, 0.0, 6.286263233796236]\n",
" B: [6.3653048194550985, 0.0, 0.0, -6.286263233796236]\n",
" 6 Outgoing Particles:\n",
" A: [-3.274142279992413, -2.62046758782023, -1.339558866223036, 1.028950598785383]\n",
" B: [-1.8502190446152251, -1.1967169760014287, 0.8476370040459147, 0.5221977611776395]\n",
" B: [-1.3090919645484567, 0.8304076910302604, -0.132118345313184, 0.08178985973111547]\n",
" B: [-1.7699077332157842, 0.8156249668276708, -0.2891156025546255, 1.1763254081859622]\n",
" B: [-1.6671330761442815, 1.2573648831500233, 0.2190135291489001, -0.3878135096217862]\n",
" B: [-2.8601155403940384, 0.913787022813704, 0.6941422808960306, -2.421450118258315]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [5.2620105860572215, 0.0, 0.0, 5.166116085395126]\n",
" B: [5.2620105860572215, 0.0, 0.0, -5.166116085395126]\n",
" 6 Outgoing Particles:\n",
" A: [-1.9479176369516882, 0.8861257045164052, 1.1018829783040076, 0.8916379636750793]\n",
" B: [-1.2433791528628988, 0.41365857789168176, 0.544699730060495, -0.27960776595565956]\n",
" B: [-1.074755543453127, 0.3002469943380598, 0.01041159782849033, 0.25464253219924826]\n",
" B: [-1.7453891507499704, 1.1576089006622574, 0.03134512003430503, -0.8398466551182168]\n",
" B: [-1.5208938996272057, 0.008686514238768405, -1.1440782944999142, -0.06424682441800389]\n",
" B: [-2.991685788469555, -2.7663266916471727, -0.544261131727384, 0.03742074961755215]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [4.439668869119513, 0.0, 0.0, 4.325582003318043]\n",
" B: [4.439668869119513, 0.0, 0.0, -4.325582003318043]\n",
" 6 Outgoing Particles:\n",
" A: [-1.1969832203303146, 0.48265768801558717, -0.02482335564392214, 0.4463117598342591]\n",
" B: [-1.7251727113760817, -1.0744400415092346, 0.6322269398265393, 0.6496834443295479]\n",
" B: [-1.419669052608684, -0.4173084301546306, -0.44626125418717505, -0.8013518491074973]\n",
" B: [-1.331289111993432, -0.7645577006899625, -0.3423664341778722, 0.2656453402118452]\n",
" B: [-1.5156451020746182, 0.6491857388484042, 0.8955487542892042, -0.2715333876518423]\n",
" B: [-1.6905785398558963, 1.1244627454898357, -0.7143246501067739, -0.2887553076163127]\n",
"\n",
" ⋮\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [5.750717080737416, 0.0, 0.0, 5.663104002460582]\n",
" B: [5.750717080737416, 0.0, 0.0, -5.663104002460582]\n",
" 6 Outgoing Particles:\n",
" A: [-1.0362067302993534, 0.23737037129807034, 0.1316212944823847, 0.007451817649030921]\n",
" B: [-3.597917991072113, -1.5787159301449987, 0.28387609057144564, 3.0613860010767477]\n",
" B: [-1.0798303035395174, -0.06880694215947386, -0.2669312876106363, -0.3000779512850572]\n",
" B: [-1.3394551212059678, -0.7053379424304421, 0.44160810884651497, -0.3187799976376953]\n",
" B: [-3.270241523195321, 1.927780354010675, 0.003047457202140131, -2.4450221348130854]\n",
" B: [-1.1777824921625586, 0.1877100894261692, -0.5932216634918489, -0.004957734989940532]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [6.84577391627276, 0.0, 0.0, 6.772342320993563]\n",
" B: [6.84577391627276, 0.0, 0.0, -6.772342320993563]\n",
" 6 Outgoing Particles:\n",
" A: [-1.0594956991232163, -0.09579189209396338, 0.21296650876679918, 0.2607687021353065]\n",
" B: [-1.8300488673592041, 0.8497425690197566, -0.8227483588311224, 0.9747315329664396]\n",
" B: [-2.860723394379955, 0.6743651794772785, 0.1320397309862766, 2.5906631300310776]\n",
" B: [-2.557528905485892, -1.3508678766931497, 1.2829278224554168, -1.4388211440218013]\n",
" B: [-3.790115184858299, 0.47588521284738383, -1.0334447791446917, -3.474262262286086]\n",
" B: [-1.5936357813389537, -0.553333192557306, 0.2282590757673212, 1.086920041175065]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [6.25909007687458, 0.0, 0.0, 6.178689876537731]\n",
" B: [6.25909007687458, 0.0, 0.0, -6.178689876537731]\n",
" 6 Outgoing Particles:\n",
" A: [-2.15208406752572, -0.27987613820502405, 0.20983197963180572, -1.873260718983155]\n",
" B: [-3.1436326945514232, -2.0821664144960677, -1.9679549582157083, 0.8210741885063981]\n",
" B: [-2.206056617746511, 1.7689323832663284, -0.4273996865759156, -0.7449117612507478]\n",
" B: [-1.8709609004510535, 0.5332842722412897, 1.48760475220818, -0.055988188078690854]\n",
" B: [-1.0916331546903268, 0.018218872767661307, 0.4300802089857822, 0.07976234031782706]\n",
" B: [-2.0538127187841235, 0.04160702442581186, 0.2678377039658561, 1.7733241394883685]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [6.8752382625158255, 0.0, 0.0, 6.802124753807565]\n",
" B: [6.8752382625158255, 0.0, 0.0, -6.802124753807565]\n",
" 6 Outgoing Particles:\n",
" A: [-3.815955448364548, 1.7284392485789066, 3.22998101457395, -0.37581430702794955]\n",
" B: [-3.705003390432734, 0.8773209536576554, -3.1633610279519866, -1.3966048382509024]\n",
" B: [-1.4798429985544235, -0.876885056483666, -0.05155962504198175, 0.6467994303891397]\n",
" B: [-1.196598159149068, -0.6492448407423084, 0.0066213036625077295, -0.10141227532326653]\n",
" B: [-1.307725757451199, -0.47623875265044, -0.08939192779758245, -0.6894580410872709]\n",
" B: [-2.2453507710796776, -0.6033915523601473, 0.06771026255509205, 1.91649003130025]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [6.591382068439754, 0.0, 0.0, 6.515083849970707]\n",
" B: [6.591382068439754, 0.0, 0.0, -6.515083849970707]\n",
" 6 Outgoing Particles:\n",
" A: [-2.166341377746586, 0.738656605699622, 1.1097711420427974, -1.3841348908550482]\n",
" B: [-1.9136122405957643, -1.3687809690739081, -0.8052302154690981, 0.37410528752561706]\n",
" B: [-1.020282522629639, 0.01566959851558055, -0.04103060943002397, -0.1976040959992001]\n",
" B: [-3.3680104240574718, -0.44221430614525714, -3.1855463435158966, -0.015336796039828009]\n",
" B: [-1.1380460439601876, 0.33787512483866744, -0.3053034033656307, 0.2962752606648943]\n",
" B: [-3.576471527889859, 0.7187939461652956, 3.227339429737853, 0.9266952347035636]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [7.366791305680796, 0.0, 0.0, 7.298603574756898]\n",
" B: [7.366791305680796, 0.0, 0.0, -7.298603574756898]\n",
" 6 Outgoing Particles:\n",
" A: [-1.1161936134323496, 0.1815174250263101, -0.30155987378038246, 0.34928677273057857]\n",
" B: [-1.1768168637671912, -0.488638136596838, -0.0387546058981897, 0.38030091090042567]\n",
" B: [-3.8756829146246745, -0.22123631639903027, -3.6727532274395425, -0.694878606198396]\n",
" B: [-1.4161987387916468, -0.42653096897021076, -0.26480462532703347, -0.8680833546784509]\n",
" B: [-3.4638938410201177, 2.8217659294852746, 1.2824429941168167, 1.179634497585545]\n",
" B: [-3.6847966397256138, -1.8668779325455054, 2.995429338328331, -0.346260220339702]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [4.762032860651893, 0.0, 0.0, 4.655851905497903]\n",
" B: [4.762032860651893, 0.0, 0.0, -4.655851905497903]\n",
" 6 Outgoing Particles:\n",
" A: [-2.656166654414924, 2.017338594394486, -1.384735065574992, 0.2609120345236529]\n",
" B: [-1.031990140619295, -0.035004877965791346, -0.20112979442869375, 0.15272561883031827]\n",
" B: [-1.7319386082994335, -1.0359644740176492, 0.8025718625008718, -0.5312883934487891]\n",
" B: [-1.7450617894727098, -0.49163856285061436, 1.1666756465784553, 0.6651316473275205]\n",
" B: [-1.0945973465763637, -0.42438631366397905, -0.017047995524507212, 0.1332252744613839]\n",
" B: [-1.2643111819210613, -0.030344365896452122, -0.3663346535511349, -0.6807061816940867]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [6.12211537837656, 0.0, 0.0, 6.039892110473065]\n",
" B: [6.12211537837656, 0.0, 0.0, -6.039892110473065]\n",
" 6 Outgoing Particles:\n",
" A: [-2.09449973649211, -1.247911941781509, -0.776547530016726, 1.1075282684200622]\n",
" B: [-2.857971140758051, 1.4507115887866229, 2.2078617054725442, 0.43449006556414854]\n",
" B: [-2.068918524386865, -0.43350532192333185, 1.7407499017717505, -0.24957318745593]\n",
" B: [-1.0503370840395667, 0.28162676024293815, -0.11219953076948735, 0.10632790470480236]\n",
" B: [-1.6648953051752136, 0.3171875953909028, -1.2925202016854087, 0.025689195388605857]\n",
" B: [-2.5076089659013125, -0.36810868071562286, -1.7673443447726724, -1.4244622466216894]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [7.431058837653249, 0.0, 0.0, 7.363466265874004]\n",
" B: [7.431058837653249, 0.0, 0.0, -7.363466265874004]\n",
" 6 Outgoing Particles:\n",
" A: [-1.4340725727125623, 0.9525417282027518, 0.38239995291064965, -0.05476016666222433]\n",
" B: [-3.5734117962040854, 2.3267511116139916, 2.49915109639257, -0.33127771922267657]\n",
" B: [-2.3529075757582945, 1.185265706342765, -1.375530715171772, 1.1132091075119688]\n",
" B: [-2.710381815585542, -2.1195780947035594, -1.2974231675570782, -0.4126153305389483]\n",
" B: [-2.374272199256637, -1.2400410368129877, 1.6839473809113144, -0.5136028830766439]\n",
" B: [-2.4170717157893766, -1.104939414642962, -1.8925445474856835, 0.1990469919885247]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [4.370360958267613, 0.0, 0.0, 4.254415930013168]\n",
" B: [4.370360958267613, 0.0, 0.0, -4.254415930013168]\n",
" 6 Outgoing Particles:\n",
" A: [-1.0037967551530176, -0.04979456910726583, -0.007092097585518878, 0.07126098999442977]\n",
" B: [-2.2427356029926337, 0.4432886498747459, -1.2315068062419472, -1.522087101319342]\n",
" B: [-1.576810353663218, -0.08400160217698217, 1.025238316808337, 0.6543401378482231]\n",
" B: [-1.1878570602356244, 0.3852696171578499, -0.47734716319323317, 0.18630996601909597]\n",
" B: [-1.6436772930583505, -1.0018521094453126, 0.4216069097815019, 0.7212593210074284]\n",
" B: [-1.0858448514323804, 0.3070900136969648, 0.26910084043086047, -0.11108331354983517]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [5.940760429560125, 0.0, 0.0, 5.855991332082674]\n",
" B: [5.940760429560125, 0.0, 0.0, -5.855991332082674]\n",
" 6 Outgoing Particles:\n",
" A: [-2.5515863925730233, 0.0574036477190863, 1.9321385747234918, 1.3319678930281418]\n",
" B: [-3.2707523737124977, -2.710802011299676, -1.41016923110446, -0.6006632045712658]\n",
" B: [-1.6965910302662786, 0.9846458960035911, 0.9504416414719069, -0.07452697242920955]\n",
" B: [-1.0283520810617242, 0.1620200166783027, 0.15874691422324994, -0.07782630689000514]\n",
" B: [-1.277724475991329, 0.26836143674120055, -0.33222621981983513, -0.6709602929248032]\n",
" B: [-2.0565145055153993, 1.2383710141574962, -1.298931679494354, 0.09200888378714224]\n",
"\n",
" Input for ABC Process: 'AB->ABBBBB':\n",
" 2 Incoming particles:\n",
" A: [6.732994664701373, 0.0, 0.0, 6.65831939417877]\n",
" B: [6.732994664701373, 0.0, 0.0, -6.65831939417877]\n",
" 6 Outgoing Particles:\n",
" A: [-1.602557260532173, -0.06659157948757613, 0.9308846463293637, -0.8349904850080558]\n",
" B: [-1.3205375883536927, 0.7078592481114431, -0.05631226213188625, -0.48947291677035515]\n",
" B: [-1.7625153098951976, 0.12706601232750347, 0.34097061443470383, 1.405010137407617]\n",
" B: [-2.7792473938949334, 1.6510422215054068, 1.7155538904747691, -1.0272051928194055]\n",
" B: [-2.722083339444658, -0.5204063912580275, -2.061236049180356, -1.3748530264647703]\n",
" B: [-3.279048437282091, -1.89896951119875, -0.8698608399265956, 2.3215114836549695]\n"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@time inputs = [gen_process_input(process) for _ in 1:1000]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Internal error: stack overflow in type inference of materialize(Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1}, Nothing, typeof(MetagraphOptimization.compute__bad8f2ac_7bfc_11ee_176b_b72dc8919aad), Tuple{Array{MetagraphOptimization.ABCProcessInput, 1}}}).\n",
"This might be caused by recursion over very long tuples or argument lists.\n"
]
},
{
"ename": "LoadError",
"evalue": "StackOverflowError:",
"output_type": "error",
"traceback": [
"StackOverflowError:",
"",
"Stacktrace:",
" [1] argtypes_to_type",
" @ ./compiler/typeutils.jl:71 [inlined]",
" [2] abstract_call_known(interp::Core.Compiler.NativeInterpreter, f::Any, arginfo::Core.Compiler.ArgInfo, si::Core.Compiler.StmtInfo, sv::Core.Compiler.InferenceState, max_methods::Int64)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:1948",
" [3] abstract_call(interp::Core.Compiler.NativeInterpreter, arginfo::Core.Compiler.ArgInfo, si::Core.Compiler.StmtInfo, sv::Core.Compiler.InferenceState, max_methods::Int64)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2020",
" [4] abstract_apply(interp::Core.Compiler.NativeInterpreter, argtypes::Vector{Any}, si::Core.Compiler.StmtInfo, sv::Core.Compiler.InferenceState, max_methods::Int64)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:1566",
" [5] abstract_call_known(interp::Core.Compiler.NativeInterpreter, f::Any, arginfo::Core.Compiler.ArgInfo, si::Core.Compiler.StmtInfo, sv::Core.Compiler.InferenceState, max_methods::Int64)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:1855",
" [6] abstract_call(interp::Core.Compiler.NativeInterpreter, arginfo::Core.Compiler.ArgInfo, si::Core.Compiler.StmtInfo, sv::Core.Compiler.InferenceState, max_methods::Nothing)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2020",
" [7] abstract_call(interp::Core.Compiler.NativeInterpreter, arginfo::Core.Compiler.ArgInfo, si::Core.Compiler.StmtInfo, sv::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:1999",
" [8] abstract_eval_statement_expr(interp::Core.Compiler.NativeInterpreter, e::Expr, vtypes::Vector{Core.Compiler.VarState}, sv::Core.Compiler.InferenceState, mi::Nothing)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2183",
" [9] abstract_eval_statement(interp::Core.Compiler.NativeInterpreter, e::Any, vtypes::Vector{Core.Compiler.VarState}, sv::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2396",
" [10] abstract_eval_basic_statement(interp::Core.Compiler.NativeInterpreter, stmt::Any, pc_vartable::Vector{Core.Compiler.VarState}, frame::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2682",
" [11] typeinf_local(interp::Core.Compiler.NativeInterpreter, frame::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2867",
" [12] typeinf_nocycle(interp::Core.Compiler.NativeInterpreter, frame::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2955",
" [13] _typeinf(interp::Core.Compiler.NativeInterpreter, frame::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/typeinfer.jl:246",
" [14] typeinf(interp::Core.Compiler.NativeInterpreter, frame::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/typeinfer.jl:216",
" [15] typeinf_edge(interp::Core.Compiler.NativeInterpreter, method::Method, atype::Any, sparams::Core.SimpleVector, caller::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/typeinfer.jl:932",
" [16] abstract_call_method(interp::Core.Compiler.NativeInterpreter, method::Method, sig::Any, sparams::Core.SimpleVector, hardlimit::Bool, si::Core.Compiler.StmtInfo, sv::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:611",
" [17] abstract_call_gf_by_type(interp::Core.Compiler.NativeInterpreter, f::Any, arginfo::Core.Compiler.ArgInfo, si::Core.Compiler.StmtInfo, atype::Any, sv::Core.Compiler.InferenceState, max_methods::Int64)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:152",
" [18] abstract_call_known(interp::Core.Compiler.NativeInterpreter, f::Any, arginfo::Core.Compiler.ArgInfo, si::Core.Compiler.StmtInfo, sv::Core.Compiler.InferenceState, max_methods::Int64)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:1949",
"--- the last 16 lines are repeated 413 more times ---",
" [6627] abstract_call(interp::Core.Compiler.NativeInterpreter, arginfo::Core.Compiler.ArgInfo, si::Core.Compiler.StmtInfo, sv::Core.Compiler.InferenceState, max_methods::Int64)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2020",
" [6628] abstract_apply(interp::Core.Compiler.NativeInterpreter, argtypes::Vector{Any}, si::Core.Compiler.StmtInfo, sv::Core.Compiler.InferenceState, max_methods::Int64)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:1566",
" [6629] abstract_call_known(interp::Core.Compiler.NativeInterpreter, f::Any, arginfo::Core.Compiler.ArgInfo, si::Core.Compiler.StmtInfo, sv::Core.Compiler.InferenceState, max_methods::Int64)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:1855",
" [6630] abstract_call(interp::Core.Compiler.NativeInterpreter, arginfo::Core.Compiler.ArgInfo, si::Core.Compiler.StmtInfo, sv::Core.Compiler.InferenceState, max_methods::Nothing)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2020",
" [6631] abstract_call(interp::Core.Compiler.NativeInterpreter, arginfo::Core.Compiler.ArgInfo, si::Core.Compiler.StmtInfo, sv::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:1999",
" [6632] abstract_eval_statement_expr(interp::Core.Compiler.NativeInterpreter, e::Expr, vtypes::Vector{Core.Compiler.VarState}, sv::Core.Compiler.InferenceState, mi::Nothing)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2183",
" [6633] abstract_eval_statement(interp::Core.Compiler.NativeInterpreter, e::Any, vtypes::Vector{Core.Compiler.VarState}, sv::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2396",
" [6634] abstract_eval_basic_statement(interp::Core.Compiler.NativeInterpreter, stmt::Any, pc_vartable::Vector{Core.Compiler.VarState}, frame::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2658",
" [6635] typeinf_local(interp::Core.Compiler.NativeInterpreter, frame::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2867",
" [6636] typeinf_nocycle(interp::Core.Compiler.NativeInterpreter, frame::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2955",
" [6637] _typeinf(interp::Core.Compiler.NativeInterpreter, frame::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/typeinfer.jl:246",
" [6638] typeinf(interp::Core.Compiler.NativeInterpreter, frame::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/typeinfer.jl:216",
" [6639] typeinf_edge(interp::Core.Compiler.NativeInterpreter, method::Method, atype::Any, sparams::Core.SimpleVector, caller::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/typeinfer.jl:932",
" [6640] abstract_call_method(interp::Core.Compiler.NativeInterpreter, method::Method, sig::Any, sparams::Core.SimpleVector, hardlimit::Bool, si::Core.Compiler.StmtInfo, sv::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:611",
" [6641] abstract_call_gf_by_type(interp::Core.Compiler.NativeInterpreter, f::Any, arginfo::Core.Compiler.ArgInfo, si::Core.Compiler.StmtInfo, atype::Any, sv::Core.Compiler.InferenceState, max_methods::Int64)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:152",
" [6642] abstract_call_known(interp::Core.Compiler.NativeInterpreter, f::Any, arginfo::Core.Compiler.ArgInfo, si::Core.Compiler.StmtInfo, sv::Core.Compiler.InferenceState, max_methods::Int64)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:1949",
" [6643] abstract_call(interp::Core.Compiler.NativeInterpreter, arginfo::Core.Compiler.ArgInfo, si::Core.Compiler.StmtInfo, sv::Core.Compiler.InferenceState, max_methods::Nothing)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2020",
" [6644] abstract_call(interp::Core.Compiler.NativeInterpreter, arginfo::Core.Compiler.ArgInfo, si::Core.Compiler.StmtInfo, sv::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:1999",
" [6645] abstract_eval_statement_expr(interp::Core.Compiler.NativeInterpreter, e::Expr, vtypes::Vector{Core.Compiler.VarState}, sv::Core.Compiler.InferenceState, mi::Nothing)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2183",
" [6646] abstract_eval_statement(interp::Core.Compiler.NativeInterpreter, e::Any, vtypes::Vector{Core.Compiler.VarState}, sv::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2396",
" [6647] abstract_eval_basic_statement(interp::Core.Compiler.NativeInterpreter, stmt::Any, pc_vartable::Vector{Core.Compiler.VarState}, frame::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2682",
" [6648] typeinf_local(interp::Core.Compiler.NativeInterpreter, frame::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2867",
" [6649] typeinf_nocycle(interp::Core.Compiler.NativeInterpreter, frame::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/abstractinterpretation.jl:2955",
" [6650] _typeinf(interp::Core.Compiler.NativeInterpreter, frame::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/typeinfer.jl:246",
" [6651] typeinf(interp::Core.Compiler.NativeInterpreter, frame::Core.Compiler.InferenceState)",
" @ Core.Compiler ./compiler/typeinfer.jl:216",
" [6652] typeinf",
" @ ./compiler/typeinfer.jl:12 [inlined]",
" [6653] typeinf_type(interp::Core.Compiler.NativeInterpreter, method::Method, atype::Any, sparams::Core.SimpleVector)",
" @ Core.Compiler ./compiler/typeinfer.jl:1079",
" [6654] return_type(interp::Core.Compiler.NativeInterpreter, t::DataType)",
" @ Core.Compiler ./compiler/typeinfer.jl:1140",
" [6655] return_type(f::Any, t::DataType)",
" @ Core.Compiler ./compiler/typeinfer.jl:1112",
" [6656] combine_eltypes(f::Function, args::Tuple{Vector{ABCProcessInput}})",
" @ Base.Broadcast ./broadcast.jl:730",
" [6657] copy(bc::Base.Broadcast.Broadcasted{Style}) where Style",
" @ Base.Broadcast ./broadcast.jl:895",
" [6658] materialize(bc::Base.Broadcast.Broadcasted)",
" @ Base.Broadcast ./broadcast.jl:873",
" [6659] var\"##core#302\"()",
" @ Main ~/.julia/packages/BenchmarkTools/0owsb/src/execution.jl:489",
" [6660] var\"##sample#303\"(::Tuple{}, __params::BenchmarkTools.Parameters)",
" @ Main ~/.julia/packages/BenchmarkTools/0owsb/src/execution.jl:495",
" [6661] _run(b::BenchmarkTools.Benchmark, p::BenchmarkTools.Parameters; verbose::Bool, pad::String, kwargs::Base.Pairs{Symbol, Integer, NTuple{4, Symbol}, NamedTuple{(:samples, :evals, :gctrial, :gcsample), Tuple{Int64, Int64, Bool, Bool}}})",
" @ BenchmarkTools ~/.julia/packages/BenchmarkTools/0owsb/src/execution.jl:99",
" [6662] #invokelatest#2",
" @ ./essentials.jl:821 [inlined]",
" [6663] invokelatest",
" @ ./essentials.jl:816 [inlined]",
" [6664] #run_result#45",
" @ ~/.julia/packages/BenchmarkTools/0owsb/src/execution.jl:34 [inlined]",
" [6665] run_result",
" @ ~/.julia/packages/BenchmarkTools/0owsb/src/execution.jl:34 [inlined]",
" [6666] run(b::BenchmarkTools.Benchmark, p::BenchmarkTools.Parameters; progressid::Nothing, nleaves::Float64, ndone::Float64, kwargs::Base.Pairs{Symbol, Integer, NTuple{5, Symbol}, NamedTuple{(:verbose, :samples, :evals, :gctrial, :gcsample), Tuple{Bool, Int64, Int64, Bool, Bool}}})",
" @ BenchmarkTools ~/.julia/packages/BenchmarkTools/0owsb/src/execution.jl:117",
" [6667] run (repeats 2 times)",
" @ ~/.julia/packages/BenchmarkTools/0owsb/src/execution.jl:117 [inlined]",
" [6668] #warmup#54",
" @ ~/.julia/packages/BenchmarkTools/0owsb/src/execution.jl:169 [inlined]",
" [6669] warmup(item::BenchmarkTools.Benchmark)",
" @ BenchmarkTools ~/.julia/packages/BenchmarkTools/0owsb/src/execution.jl:168"
]
}
],
"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": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.9.3",
"language": "julia",
"name": "julia-1.9"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.9.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

View File

@ -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, ComputeTaskP"
]
},
{
"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, ComputeTaskU: 6, \n",
" ComputeTaskV: 64, ComputeTaskSum: 1, ComputeTaskS2: 24, \n",
" ComputeTaskS1: 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 1.9.3",
"language": "julia",
"name": "julia-1.9"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.9.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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": 5,
"metadata": {},
"outputs": [],
"source": [
"@ProfileView.profview comp_func = get_compute_function(graph, process)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.9.3",
"language": "julia",
"name": "julia-1.9"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.9.3"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}

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

View File

@ -1,35 +1,83 @@
"""
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
# 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 ABCProcessDescription, ABCProcessInput, ABCModel
export ComputeTaskP
export ComputeTaskS1
export ComputeTaskS2
export ComputeTaskV
export ComputeTaskU
export ComputeTaskSum
# code generation related
export execute
export parse_dag, parse_process
export gen_process_input
export get_compute_function
# 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
@ -38,6 +86,8 @@ export bytes_to_human_readable
import Base.length
import Base.show
import Base.==
import Base.+
import Base.-
import Base.in
import Base.copy
import Base.isempty
@ -46,9 +96,11 @@ 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")
@ -72,6 +124,7 @@ 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 +132,48 @@ 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/compute.jl")
include("task/print.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("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/main.jl")
end # module MetagraphOptimization

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"""
gen_code(graph::DAG)
Generate the code for a given graph. The return value is a named tuple of:
- `code::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
See also: [`execute`](@ref)
"""
function gen_code(graph::DAG, machine::Machine)
sched = schedule_dag(GreedyScheduler(), graph, machine)
codeAcc = Vector{Expr}()
sizehint!(codeAcc, length(graph.nodes))
for node in sched
# TODO: this is kind of ugly, should init nodes be scheduled differently from the rest?
if (node isa DataTaskNode && length(node.children) == 0)
push!(codeAcc, get_init_expression(node, entry_device(machine)))
continue
end
push!(codeAcc, get_expression(node))
end
# 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))
return (code = Expr(:block, codeAcc...), inputSymbols = inputSyms, outputSymbol = outSym)
end
function gen_cache_init_code(machine::Machine)
initializeCaches = Vector{Expr}()
for device in machine.devices
push!(initializeCaches, gen_cache_init_code(device))
end
return Expr(:block, initializeCaches...)
end
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{Expr}()
for (name, symbols) in inputSymbols
type = type_from_name(name)
index = parse(Int, name[2:end])
p = nothing
if (index > in_particles(processDescription)[type])
index -= in_particles(processDescription)[type]
@assert index <= out_particles(processDescription)[type] "Too few particles of type $type in input particles for this process"
p = "filter(x -> typeof(x) <: $type, out_particles($(processInputSymbol)))[$(index)]"
else
p = "filter(x -> typeof(x) <: $type, in_particles($(processInputSymbol)))[$(index)]"
end
for symbol in symbols
# TODO: how to get the "default" cpu device?
device = entry_device(machine)
evalExpr = eval(gen_access_expr(device, symbol))
push!(assignInputs, Meta.parse("$(evalExpr)::ParticleValue{$type} = ParticleValue($p, 1.0)"))
end
end
return Expr(:block, assignInputs...)
end
"""
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)
(code, inputSymbols, outputSymbol) = gen_code(graph, machine)
initCaches = gen_cache_init_code(machine)
assignInputs = gen_input_assignment_code(inputSymbols, process, machine, :input)
functionId = to_var_name(UUIDs.uuid1(rng[1]))
resSym = eval(gen_access_expr(entry_device(machine), outputSymbol))
expr = Meta.parse(
"function compute_$(functionId)(input::AbstractProcessInput) $initCaches; $assignInputs; $code; return $resSym; end",
)
func = eval(expr)
return func
end
"""
execute(graph::DAG, process::AbstractProcessDescription, machine::Machine, input::AbstractProcessInput)
Execute the code of the given `graph` on the given input particles.
This is essentially shorthand for
```julia
compute_graph = get_compute_function(graph, process)
result = compute_graph(particles)
```
See also: [`parse_dag`](@ref), [`parse_process`](@ref), [`gen_process_input`](@ref)
"""
function execute(graph::DAG, process::AbstractProcessDescription, machine::Machine, input::AbstractProcessInput)
(code, inputSymbols, outputSymbol) = gen_code(graph, machine)
initCaches = gen_cache_init_code(machine)
assignInputs = gen_input_assignment_code(inputSymbols, process, machine, :input)
functionId = to_var_name(UUIDs.uuid1(rng[1]))
resSym = eval(gen_access_expr(entry_device(machine), outputSymbol))
expr = Meta.parse(
"function compute_$(functionId)(input::AbstractProcessInput) $initCaches; $assignInputs; $code; return $resSym; end",
)
func = eval(expr)
result = 0
try
result = @eval $func($input)
catch e
println("Error while evaluating: $e")
# if we find a uuid in the exception we can color it in so it's easier to spot
uuidRegex = r"[0-9a-f]{8}_[0-9a-f]{4}_[0-9a-f]{4}_[0-9a-f]{4}_[0-9a-f]{12}"
m = match(uuidRegex, string(e))
functionStr = string(expr)
if (isa(m, RegexMatch))
functionStr = replace(functionStr, m.match => "\033[31m$(m.match)\033[0m")
end
println("Function:\n$functionStr")
@assert false
end
return result
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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@ -1,6 +1,11 @@
"""
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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@ -1,6 +1,11 @@
"""
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

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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

View File

@ -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

View File

@ -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)

View File

@ -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 +106,46 @@ 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)
#TODO: filter is very slow
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 +170,110 @@ 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)
# TODO: filter is very slow
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 +284,13 @@ 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 +301,11 @@ 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 +319,11 @@ 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)

View File

@ -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
@ -48,12 +59,8 @@ function show(io::IO, graph::DAG)
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

View File

@ -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

View File

@ -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

View File

@ -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

159
src/models/abc/compute.jl Normal file
View File

@ -0,0 +1,159 @@
using AccurateArithmetic
"""
compute(::ComputeTaskP, data::ParticleValue)
Return the particle and value as is.
0 FLOP.
"""
function compute(::ComputeTaskP, data::ParticleValue{P})::ParticleValue{P} where {P <: ABCParticle}
return data
end
"""
compute(::ComputeTaskU, data::ParticleValue)
Compute an outer edge. Return the particle value with the same particle and the value multiplied by an outer_edge factor.
1 FLOP.
"""
function compute(::ComputeTaskU, data::ParticleValue{P})::ParticleValue{P} where {P <: ABCParticle}
return ParticleValue(data.p, data.v * outer_edge(data.p))
end
"""
compute(::ComputeTaskV, data1::ParticleValue, data2::ParticleValue)
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(
::ComputeTaskV,
data1::ParticleValue{P1},
data2::ParticleValue{P2},
)::ParticleValue where {P1 <: ABCParticle, P2 <: ABCParticle}
p3 = preserve_momentum(data1.p, data2.p)
dataOut = ParticleValue(p3, data1.v * vertex() * data2.v)
return dataOut
end
"""
compute(::ComputeTaskS2, data1::ParticleValue, data2::ParticleValue)
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(::ComputeTaskS2, 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. $(data2.p.momentum.pz)"
=#
inner = inner_edge(data1.p)
return data1.v * inner * data2.v
end
"""
compute(::ComputeTaskS1, data::ParticleValue)
Compute inner edge (1 input particle, 1 output particle).
11 FLOP.
"""
function compute(::ComputeTaskS1, data::ParticleValue{P})::ParticleValue{P} where {P <: ABCParticle}
return ParticleValue(data.p, data.v * inner_edge(data.p))
end
"""
compute(::ComputeTaskSum, data::Vector{Float64})
Compute a sum over the vector. Use an algorithm that accounts for accumulated errors in long sums with potentially large differences in magnitude of the summands.
Linearly many FLOP with growing data.
"""
function compute(::ComputeTaskSum, data::Vector{Float64})::Float64
return sum_kbn(data)
end
"""
get_expression(::ComputeTaskP, device::AbstractDevice, inExprs::Vector{Expr}, outExpr::Expr)
Generate and return code evaluating [`ComputeTaskP`](@ref) on `inSyms`, providing the output on `outSym`.
"""
function get_expression(::ComputeTaskP, device::AbstractDevice, inExprs::Vector, outExpr)
in = [eval(inExprs[1])]
out = eval(outExpr)
return Meta.parse("$out = compute(ComputeTaskP(), $(in[1]))")
end
"""
get_expression(::ComputeTaskU, device::AbstractDevice, inExprs::Vector{Expr}, outExpr::Expr)
Generate code evaluating [`ComputeTaskU`](@ref) on `inSyms`, providing the output on `outSym`.
`inSyms` should be of type [`ParticleValue`](@ref), `outSym` will be of type [`ParticleValue`](@ref).
"""
function get_expression(::ComputeTaskU, device::AbstractDevice, inExprs::Vector, outExpr)
in = [eval(inExprs[1])]
out = eval(outExpr)
return Meta.parse("$out = compute(ComputeTaskU(), $(in[1]))")
end
"""
get_expression(::ComputeTaskV, device::AbstractDevice, inExprs::Vector{Expr}, outExpr::Expr)
Generate code evaluating [`ComputeTaskV`](@ref) on `inSyms`, providing the output on `outSym`.
`inSym[1]` and `inSym[2]` should be of type [`ParticleValue`](@ref), `outSym` will be of type [`ParticleValue`](@ref).
"""
function get_expression(::ComputeTaskV, device::AbstractDevice, inExprs::Vector, outExpr)
in = [eval(inExprs[1]), eval(inExprs[2])]
out = eval(outExpr)
return Meta.parse("$out = compute(ComputeTaskV(), $(in[1]), $(in[2]))")
end
"""
get_expression(::ComputeTaskS2, device::AbstractDevice, inExprs::Vector{Expr}, outExpr::Expr)
Generate code evaluating [`ComputeTaskS2`](@ref) on `inSyms`, providing the output on `outSym`.
`inSyms[1]` and `inSyms[2]` should be of type [`ParticleValue`](@ref), `outSym` will be of type `Float64`.
"""
function get_expression(::ComputeTaskS2, device::AbstractDevice, inExprs::Vector, outExpr)
in = [eval(inExprs[1]), eval(inExprs[2])]
out = eval(outExpr)
return Meta.parse("$out = compute(ComputeTaskS2(), $(in[1]), $(in[2]))")
end
"""
get_expression(::ComputeTaskS1, device::AbstractDevice, inExprs::Vector{Expr}, outExpr::Expr)
Generate code evaluating [`ComputeTaskS1`](@ref) on `inSyms`, providing the output on `outSym`.
`inSyms` should be of type [`ParticleValue`](@ref), `outSym` will be of type [`ParticleValue`](@ref).
"""
function get_expression(::ComputeTaskS1, device::AbstractDevice, inExprs::Vector, outExpr)
in = [eval(inExprs[1])]
out = eval(outExpr)
return Meta.parse("$out = compute(ComputeTaskS1(), $(in[1]))")
end
"""
get_expression(::ComputeTaskSum, device::AbstractDevice, inExprs::Vector{Expr}, outExpr::Expr)
Generate code evaluating [`ComputeTaskSum`](@ref) on `inSyms`, providing the output on `outSym`.
`inSyms` should be of type [`Float64`], `outSym` will be of type [`Float64`].
"""
function get_expression(::ComputeTaskSum, device::AbstractDevice, inExprs::Vector, outExpr)
in = eval.(inExprs)
out = eval(outExpr)
return Meta.parse("$out = compute(ComputeTaskSum(), [$(unroll_symbol_vector(in))])")
end

198
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@ -0,0 +1,198 @@
using QEDbase
using Random
using Roots
using ForwardDiff
ComputeTaskSum() = ComputeTaskSum(0)
"""
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
processInput = ABCProcessInput(processDescription, inputParticles, outputParticles)
return return processInput
end
####################
# CODE FROM HERE BORROWED FROM SOURCE: https://codebase.helmholtz.cloud/qedsandbox/QEDphasespaces.jl/
# use qedphasespaces directly once released
#
# quick and dirty implementation of the RAMBO algorithm
#
# reference:
# * https://cds.cern.ch/record/164736/files/198601282.pdf
# * https://www.sciencedirect.com/science/article/pii/0010465586901190
####################
function generate_initial_moms(ss, masses)
E1 = (ss^2 + masses[1]^2 - masses[2]^2) / (2 * ss)
E2 = (ss^2 + masses[2]^2 - masses[1]^2) / (2 * ss)
rho1 = sqrt(E1^2 - masses[1]^2)
rho2 = sqrt(E2^2 - masses[2]^2)
return [SFourMomentum(E1, 0, 0, rho1), SFourMomentum(E2, 0, 0, -rho2)]
end
Random.rand(rng::AbstractRNG, ::Random.SamplerType{SFourMomentum}) = SFourMomentum(rand(rng, 4))
Random.rand(rng::AbstractRNG, ::Random.SamplerType{NTuple{N, Float64}}) where {N} = Tuple(rand(rng, N))
function _transform_uni_to_mom(u1, u2, u3, u4)
cth = 2 * u1 - 1
sth = sqrt(1 - cth^2)
phi = 2 * pi * u2
q0 = -log(u3 * u4)
qx = q0 * sth * cos(phi)
qy = q0 * sth * sin(phi)
qz = q0 * cth
return SFourMomentum(q0, qx, qy, qz)
end
function _transform_uni_to_mom!(uni_mom, dest)
u1, u2, u3, u4 = Tuple(uni_mom)
cth = 2 * u1 - 1
sth = sqrt(1 - cth^2)
phi = 2 * pi * u2
q0 = -log(u3 * u4)
qx = q0 * sth * cos(phi)
qy = q0 * sth * sin(phi)
qz = q0 * cth
return dest = SFourMomentum(q0, qx, qy, qz)
end
_transform_uni_to_mom(u1234::Tuple) = _transform_uni_to_mom(u1234...)
_transform_uni_to_mom(u1234::SFourMomentum) = _transform_uni_to_mom(Tuple(u1234))
function generate_massless_moms(rng, n::Int)
a = Vector{SFourMomentum}(undef, n)
rand!(rng, a)
return map(_transform_uni_to_mom, a)
end
function generate_physical_massless_moms(rng, ss, n)
r_moms = generate_massless_moms(rng, n)
Q = sum(r_moms)
M = sqrt(Q * Q)
fac = -1 / M
Qx = getX(Q)
Qy = getY(Q)
Qz = getZ(Q)
bx = fac * Qx
by = fac * Qy
bz = fac * Qz
gamma = getT(Q) / M
a = 1 / (1 + gamma)
x = ss / M
i = 1
while i <= n
mom = r_moms[i]
mom0 = getT(mom)
mom1 = getX(mom)
mom2 = getY(mom)
mom3 = getZ(mom)
bq = bx * mom1 + by * mom2 + bz * mom3
p0 = x * (gamma * mom0 + bq)
px = x * (mom1 + bx * mom0 + a * bq * bx)
py = x * (mom2 + by * mom0 + a * bq * by)
pz = x * (mom3 + bz * mom0 + a * bq * bz)
r_moms[i] = SFourMomentum(p0, px, py, pz)
i += 1
end
return r_moms
end
function _to_be_solved(xi, masses, p0s, ss)
sum = 0.0
for (i, E) in enumerate(p0s)
sum += sqrt(masses[i]^2 + xi^2 * E^2)
end
return sum - ss
end
function _build_massive_momenta(xi, masses, massless_moms)
vec = SFourMomentum[]
i = 1
while i <= length(massless_moms)
massless_mom = massless_moms[i]
k0 = sqrt(getT(massless_mom)^2 * xi^2 + masses[i]^2)
kx = xi * getX(massless_mom)
ky = xi * getY(massless_mom)
kz = xi * getZ(massless_mom)
push!(vec, SFourMomentum(k0, kx, ky, kz))
i += 1
end
return vec
end
first_derivative(func) = x -> ForwardDiff.derivative(func, float(x))
function generate_physical_massive_moms(rng, ss, masses; x0 = 0.1)
n = length(masses)
massless_moms = generate_physical_massless_moms(rng, ss, n)
energies = getT.(massless_moms)
f = x -> _to_be_solved(x, masses, energies, ss)
xi = find_zero((f, first_derivative(f)), x0, Roots.Newton())
return _build_massive_momenta(xi, masses, massless_moms)
end

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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(ComputeTaskSum(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(ComputeTaskP()), 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 ParticleValue object)
compute_u = insert_node!(graph, make_node(ComputeTaskU()), 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 ParticleValue 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(ComputeTaskV()), 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(ComputeTaskS1()), 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(ComputeTaskS1()), 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(ComputeTaskV()), 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(ComputeTaskS2()), 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

210
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@ -0,0 +1,210 @@
using QEDbase
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 <: AbstractProcessInput
process::ABCProcessDescription
inParticles::Vector{ABCParticle}
outParticles::Vector{ABCParticle}
end
"""
PARTICLE_MASSES
A constant dictionary containing the masses of the different [`ABCParticle`](@ref)s.
"""
const PARTICLE_MASSES = Dict{Type, Float64}(ParticleA => 1.0, ParticleB => 1.0, ParticleC => 0.0)
"""
mass(t::Type{T}) where {T <: ABCParticle}
Return the mass (at rest) of the given particle type.
"""
mass(t::Type{T}) where {T <: ABCParticle} = PARTICLE_MASSES[t]
"""
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
"""
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 inner_edge(p::ABCParticle)
return 1.0 / (square(p) - mass(typeof(p)) * mass(typeof(p)))
end
"""
outer_edge(p::ABCParticle)
Return the factor of the outer edge with the given (real) particle.
Takes 0 effective FLOP.
"""
function outer_edge(p::ABCParticle)
return 1.0
end
"""
vertex()
Return the factor of a vertex.
Takes 0 effective FLOP since it's constant.
"""
function vertex()
i = 1.0
lambda = 1.0 / 137.0
return i * lambda
end
"""
preserve_momentum(p1::ABCParticle, p2::ABCParticle)
Calculate and return a new particle from two given interacting ones at a vertex.
Takes 4 effective FLOP.
"""
function preserve_momentum(p1::ABCParticle, p2::ABCParticle)
t3 = interaction_result(typeof(p1), typeof(p2))
p3 = t3(p1.momentum + p2.momentum)
return p3
end
"""
type_from_name(name::String)
For a name of a particle, return the particle's [`Type`].
"""
function type_from_name(name::String)
if startswith(name, "A")
return ParticleA
elseif startswith(name, "B")
return ParticleB
elseif startswith(name, "C")
return ParticleC
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 in_particles(input::ABCProcessInput)
return input.inParticles
end
function out_particles(process::ABCProcessDescription)
return process.outParticles
end
function out_particles(input::ABCProcessInput)
return input.outParticles
end

58
src/models/abc/print.jl Normal file
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@ -0,0 +1,58 @@
"""
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, " $(length(processInput.inParticles)) Incoming particles:")
for particle in processInput.inParticles
println(io, " $particle")
end
println(io, " $(length(processInput.outParticles)) Outgoing Particles:")
for particle in processInput.outParticles
println(io, " $particle")
end
return nothing
end
"""
show(io::IO, particle::T) where {T <: ABCParticle}
Pretty print an [`ABCParticle`](@ref) (no newlines).
"""
function show(io::IO, particle::T) where {T <: ABCParticle}
print(io, "$(String(typeof(particle))): $(particle.momentum)")
return nothing
end

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@ -1,21 +1,166 @@
# 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::ComputeTaskS1)
Return the compute effort of an S1 task.
"""
compute_effort(t::ComputeTaskS1)::Float64 = 11.0
"""
compute_effort(t::ComputeTaskS2)
Return the compute effort of an S2 task.
"""
compute_effort(t::ComputeTaskS2)::Float64 = 12.0
"""
compute_effort(t::ComputeTaskU)
Return the compute effort of a U task.
"""
compute_effort(t::ComputeTaskU)::Float64 = 1.0
"""
compute_effort(t::ComputeTaskV)
Return the compute effort of a V task.
"""
compute_effort(t::ComputeTaskV)::Float64 = 6.0
"""
compute_effort(t::ComputeTaskP)
Return the compute effort of a P task.
"""
compute_effort(t::ComputeTaskP)::Float64 = 0.0
"""
compute_effort(t::ComputeTaskSum)
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::ComputeTaskSum)::Float64 = 1.0
"""
show(io::IO, t::DataTask)
Print the data task to io.
"""
function show(io::IO, t::DataTask)
return print(io, "Data", t.data)
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")
"""
show(io::IO, t::ComputeTaskS1)
Print the S1 task to io.
"""
show(io::IO, t::ComputeTaskS1) = print(io, "ComputeS1")
"""
show(io::IO, t::ComputeTaskS2)
Print the S2 task to io.
"""
show(io::IO, t::ComputeTaskS2) = print(io, "ComputeS2")
"""
show(io::IO, t::ComputeTaskP)
Print the P task to io.
"""
show(io::IO, t::ComputeTaskP) = print(io, "ComputeP")
"""
show(io::IO, t::ComputeTaskU)
Print the U task to io.
"""
show(io::IO, t::ComputeTaskU) = print(io, "ComputeU")
"""
show(io::IO, t::ComputeTaskV)
Print the V task to io.
"""
show(io::IO, t::ComputeTaskV) = print(io, "ComputeV")
"""
show(io::IO, t::ComputeTaskSum)
Print the sum task to io.
"""
show(io::IO, t::ComputeTaskSum) = print(io, "ComputeSum")
"""
copy(t::DataTask)
Copy the data task and return it.
"""
copy(t::DataTask) = DataTask(t.data)
"""
children(::DataTask)
Return the number of children of a data task (always 1).
"""
children(::DataTask) = 1
"""
children(::ComputeTaskS1)
Return the number of children of a ComputeTaskS1 (always 1).
"""
children(::ComputeTaskS1) = 1
"""
children(::ComputeTaskS2)
Return the number of children of a ComputeTaskS2 (always 2).
"""
children(::ComputeTaskS2) = 2
"""
children(::ComputeTaskP)
Return the number of children of a ComputeTaskP (always 1).
"""
children(::ComputeTaskP) = 1
"""
children(::ComputeTaskU)
Return the number of children of a ComputeTaskU (always 1).
"""
children(::ComputeTaskU) = 1
"""
children(::ComputeTaskV)
Return the number of children of a ComputeTaskV (always 2).
"""
children(::ComputeTaskV) = 2
"""
children(::ComputeTaskSum)
Return the number of children of a ComputeTaskSum.
"""
children(t::ComputeTaskSum) = t.children_number
"""
children(t::FusedComputeTask)
Return the number of children of a FusedComputeTask.
"""
function children(t::FusedComputeTask)
return length(union(Set(t.t1_inputs), Set(t.t2_inputs)))
end
function add_child!(t::ComputeTaskSum)
t.children_number += 1
return nothing
end

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@ -1,31 +1,59 @@
"""
DataTask <: AbstractDataTask
Task representing a specific data transfer in the ABC Model.
"""
struct DataTask <: AbstractDataTask
data::UInt64
data::Float64
end
# S task with 1 child
"""
ComputeTaskS1 <: AbstractComputeTask
S task with a single child.
"""
struct ComputeTaskS1 <: AbstractComputeTask end
# S task with 2 children
"""
ComputeTaskS2 <: AbstractComputeTask
S task with two children.
"""
struct ComputeTaskS2 <: AbstractComputeTask end
# P task with 0 children
"""
ComputeTaskP <: AbstractComputeTask
P task with no children.
"""
struct ComputeTaskP <: AbstractComputeTask end
# v task with 2 children
"""
ComputeTaskV <: AbstractComputeTask
v task with two children.
"""
struct ComputeTaskV <: AbstractComputeTask end
# u task with 1 child
"""
ComputeTaskU <: AbstractComputeTask
u task with a single child.
"""
struct ComputeTaskU <: AbstractComputeTask end
# task that sums all its inputs, n children
struct ComputeTaskSum <: AbstractComputeTask end
"""
ComputeTaskSum <: AbstractComputeTask
ABC_TASKS = [
DataTask,
ComputeTaskS1,
ComputeTaskS2,
ComputeTaskP,
ComputeTaskV,
ComputeTaskU,
ComputeTaskSum,
]
Task that sums all its inputs, n children.
"""
mutable struct ComputeTaskSum <: AbstractComputeTask
children_number::Int
end
"""
ABC_TASKS
Constant vector of all tasks of the ABC-Model.
"""
ABC_TASKS = [DataTask, ComputeTaskS1, ComputeTaskS2, ComputeTaskP, ComputeTaskV, ComputeTaskU, ComputeTaskSum]

109
src/models/interface.jl Normal file
View File

@ -0,0 +1,109 @@
"""
AbstractPhysicsModel
Base type for a model, e.g. ABC-Model or QED. This is used to dispatch many functions.
"""
abstract type AbstractPhysicsModel end
"""
AbstractParticle
Base type for particles belonging to a certain [`AbstractPhysicsModel`](@ref).
"""
abstract type AbstractParticle end
"""
ParticleValue{ParticleType <: AbstractParticle}
A struct describing a particle during a calculation of a Feynman Diagram, together with the value that's being calculated.
`sizeof(ParticleValue())` = 48 Byte
"""
struct ParticleValue{ParticleType <: AbstractParticle}
p::ParticleType
v::Float64
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
"""
mass(t::Type{T}) where {T <: AbstractParticle}
Interface function that must be implemented for every subtype of [`AbstractParticle`](@ref), returning the particles mass at rest.
"""
function mass 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
"""
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

10
src/models/print.jl Normal file
View File

@ -0,0 +1,10 @@
"""
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

View File

@ -1,15 +1,35 @@
"""
==(e1::Edge, e2::Edge)
Equality comparison between two edges.
"""
function ==(e1::Edge, e2::Edge)
return e1.edge[1] == e2.edge[1] && e1.edge[2] == e2.edge[2]
end
"""
==(n1::Node, n2::Node)
Fallback equality comparison between two nodes. For equal node types, the more specific versions of this function will be called.
"""
function ==(n1::Node, n2::Node)
return false
end
function ==(n1::ComputeTaskNode, n2::ComputeTaskNode)
"""
==(n1::ComputeTaskNode, n2::ComputeTaskNode)
Equality comparison between two [`ComputeTaskNode`](@ref)s.
"""
function ==(n1::ComputeTaskNode{TaskType}, n2::ComputeTaskNode{TaskType}) where {TaskType <: AbstractComputeTask}
return n1.id == n2.id
end
function ==(n1::DataTaskNode, n2::DataTaskNode)
"""
==(n1::DataTaskNode, n2::DataTaskNode)
Equality comparison between two [`DataTaskNode`](@ref)s.
"""
function ==(n1::DataTaskNode{TaskType}, n2::DataTaskNode{TaskType}) where {TaskType <: AbstractDataTask}
return n1.id == n2.id
end

View File

@ -1,23 +1,71 @@
DataTaskNode(t::AbstractDataTask, name = "") =
DataTaskNode(t, Vector{Node}(), Vector{Node}(), UUIDs.uuid1(rng[threadid()]), missing, missing, missing, name)
ComputeTaskNode(t::AbstractComputeTask) = ComputeTaskNode(
t, # task
Vector{Node}(), # parents
Vector{Node}(), # children
UUIDs.uuid1(rng[threadid()]), # id
missing, # node reduction
missing, # node split
Vector{NodeFusion}(), # node fusions
missing, # device
)
copy(m::Missing) = missing
copy(n::ComputeTaskNode) = ComputeTaskNode(copy(task(n)))
copy(n::DataTaskNode) = DataTaskNode(copy(task(n)), n.name)
"""
make_node(t::AbstractTask)
Fallback implementation of `make_node` for an [`AbstractTask`](@ref), throwing an error.
"""
function make_node(t::AbstractTask)
return error("Cannot make a node from this task type")
end
function make_node(t::AbstractDataTask)
return DataTaskNode(t)
"""
make_node(t::AbstractDataTask)
Construct and return a new [`DataTaskNode`](@ref) with the given task.
"""
function make_node(t::AbstractDataTask, name::String = "")
return DataTaskNode(t, name)
end
"""
make_node(t::AbstractComputeTask)
Construct and return a new [`ComputeTaskNode`](@ref) with the given task.
"""
function make_node(t::AbstractComputeTask)
return ComputeTaskNode(t)
end
"""
make_edge(n1::Node, n2::Node)
Fallback implementation of `make_edge` throwing an error. If you got this error it likely means you tried to construct an edge between two nodes of the same type.
"""
function make_edge(n1::Node, n2::Node)
return error("Can only create edges from compute to data node or reverse")
end
"""
make_edge(n1::ComputeTaskNode, n2::DataTaskNode)
Construct and return a new [`Edge`](@ref) pointing from `n1` (child) to `n2` (parent).
"""
function make_edge(n1::ComputeTaskNode, n2::DataTaskNode)
return Edge((n1, n2))
end
"""
make_edge(n1::DataTaskNode, n2::ComputeTaskNode)
Construct and return a new [`Edge`](@ref) pointing from `n1` (child) to `n2` (parent).
"""
function make_edge(n1::DataTaskNode, n2::ComputeTaskNode)
return Edge((n1, n2))
end

View File

@ -1,7 +1,27 @@
"""
show(io::IO, n::Node)
Print a short string representation of the node to io.
"""
function show(io::IO, n::Node)
return print(io, "Node(", n.task, ")")
return print(io, "Node(", task(n), ")")
end
"""
show(io::IO, e::Edge)
Print a short string representation of the edge to io.
"""
function show(io::IO, e::Edge)
return print(io, "Edge(", e.edge[1], ", ", e.edge[2], ")")
end
"""
to_var_name(id::UUID)
Return the uuid as a string usable as a variable name in code generation.
"""
function to_var_name(id::UUID)
str = "_" * replace(string(id), "-" => "_")
return str
end

View File

@ -1,52 +1,123 @@
is_entry_node(node::Node) = length(node.children) == 0
is_exit_node(node::Node) = length(node.parents) == 0
"""
is_entry_node(node::Node)
# children = prerequisite nodes, nodes that need to execute before the task, edges point into this task
function children(node::Node)
return copy(node.children)
Return whether this node is an entry node in its graph, i.e., it has no children.
"""
is_entry_node(node::Node) = length(children(node)) == 0
"""
is_exit_node(node::Node)
Return whether this node is an exit node of its graph, i.e., it has no parents.
"""
is_exit_node(node::Node)::Bool = length(parents(node)) == 0
"""
task(node::Node)
Return the node's task.
"""
function task(node::DataTaskNode{TaskType})::TaskType where {TaskType <: Union{AbstractDataTask, AbstractComputeTask}}
return node.task
end
function task(
node::ComputeTaskNode{TaskType},
)::TaskType where {TaskType <: Union{AbstractDataTask, AbstractComputeTask}}
return node.task
end
# parents = subsequent nodes, nodes that need this node to execute, edges point from this task
function parents(node::Node)
return copy(node.parents)
"""
children(node::Node)
Return a copy of the node's children so it can safely be muted without changing the node in the graph.
A node's children are its prerequisite nodes, nodes that need to execute before the task of this node.
"""
function children(node::DataTaskNode)::Vector{ComputeTaskNode}
return node.children
end
function children(node::ComputeTaskNode)::Vector{DataTaskNode}
return node.children
end
# siblings = all children of any parents, no duplicates, includes the node itself
function siblings(node::Node)
"""
parents(node::Node)
Return a copy of the node's parents so it can safely be muted without changing the node in the graph.
A node's parents are its subsequent nodes, nodes that need this node to execute.
"""
function parents(node::DataTaskNode)::Vector{ComputeTaskNode}
return node.parents
end
function parents(node::ComputeTaskNode)::Vector{DataTaskNode}
return node.parents
end
"""
siblings(node::Node)
Return a vector of all siblings of this node.
A node's siblings are all children of any of its parents. The result contains no duplicates and includes the node itself.
"""
function siblings(node::Node)::Set{Node}
result = Set{Node}()
push!(result, node)
for parent in node.parents
union!(result, parent.children)
for parent in parents(node)
union!(result, children(parent))
end
return result
end
# partners = all parents of any children, no duplicates, includes the node itself
function partners(node::Node)
"""
partners(node::Node)
Return a vector of all partners of this node.
A node's partners are all parents of any of its children. The result contains no duplicates and includes the node itself.
Note: This is very slow when there are multiple children with many parents.
This is less of a problem in [`siblings(node::Node)`](@ref) because (depending on the model) there are no nodes with a large number of children, or only a single one.
"""
function partners(node::Node)::Set{Node}
result = Set{Node}()
push!(result, node)
for child in node.children
union!(result, child.parents)
for child in children(node)
union!(result, parents(child))
end
return result
end
# alternative version to partners(Node), avoiding allocation of a new set
# works on the given set and returns nothing
"""
partners(node::Node, set::Set{Node})
Alternative version to [`partners(node::Node)`](@ref), avoiding allocation of a new set. Works on the given set and returns `nothing`.
"""
function partners(node::Node, set::Set{Node})
push!(set, node)
for child in node.children
union!(set, child.parents)
for child in children(node)
union!(set, parents(child))
end
return nothing
end
function is_parent(potential_parent, node)
return potential_parent in node.parents
"""
is_parent(potential_parent::Node, node::Node)
Return whether the `potential_parent` is a parent of `node`.
"""
function is_parent(potential_parent::Node, node::Node)::Bool
return potential_parent in parents(node)
end
function is_child(potential_child, node)
return potential_child in node.children
"""
is_child(potential_child::Node, node::Node)
Return whether the `potential_child` is a child of `node`.
"""
function is_child(potential_child::Node, node::Node)::Bool
return potential_child in children(node)
end

View File

@ -5,14 +5,36 @@ using Base.Threads
# TODO: reliably find out how many threads we're running with (nthreads() returns 1 when precompiling :/)
rng = [Random.MersenneTwister(0) for _ in 1:32]
"""
Node
The abstract base type of every node.
See [`DataTaskNode`](@ref), [`ComputeTaskNode`](@ref) and [`make_node`](@ref).
"""
abstract type Node end
# declare this type here because it's needed
# the specific operations are declared in graph.jl
abstract type Operation end
mutable struct DataTaskNode <: Node
task::AbstractDataTask
"""
DataTaskNode <: Node
Any node that transfers data and does no computation.
# Fields
`.task`: The node's data task type. Usually [`DataTask`](@ref).\\
`.parents`: A vector of the node's parents (i.e. nodes that depend on this one).\\
`.children`: A vector of the node's children (i.e. nodes that this one depends on).\\
`.id`: The node's id. Improves the speed of comparisons and is used as a unique identifier.\\
`.nodeReduction`: Either this node's [`NodeReduction`](@ref) or `missing`, if none. There can only be at most one.\\
`.nodeSplit`: Either this node's [`NodeSplit`](@ref) or `missing`, if none. There can only be at most one.\\
`.nodeFusion`: Either this node's [`NodeFusion`](@ref) or `missing`, if none. There can only be at most one for DataTaskNodes.\\
`.name`: The name of this node for entry nodes into the graph ([`is_entry_node`](@ref)) to reliably assign the inputs to the correct nodes when executing.\\
"""
mutable struct DataTaskNode{TaskType <: AbstractDataTask} <: Node
task::TaskType
# use vectors as sets have way too much memory overhead
parents::Vector{Node}
@ -31,11 +53,28 @@ mutable struct DataTaskNode <: Node
# the node fusion involving this node, if it exists
nodeFusion::Union{Operation, Missing}
# for input nodes we need a name for the node to distinguish between them
name::String
end
# same as DataTaskNode
mutable struct ComputeTaskNode <: Node
task::AbstractComputeTask
"""
ComputeTaskNode <: Node
Any node that computes a result from inputs using an [`AbstractComputeTask`](@ref).
# Fields
`.task`: The node's compute task type. A concrete subtype of [`AbstractComputeTask`](@ref).\\
`.parents`: A vector of the node's parents (i.e. nodes that depend on this one).\\
`.children`: A vector of the node's children (i.e. nodes that this one depends on).\\
`.id`: The node's id. Improves the speed of comparisons and is used as a unique identifier.\\
`.nodeReduction`: Either this node's [`NodeReduction`](@ref) or `missing`, if none. There can only be at most one.\\
`.nodeSplit`: Either this node's [`NodeSplit`](@ref) or `missing`, if none. There can only be at most one.\\
`.nodeFusions`: A vector of this node's [`NodeFusion`](@ref)s. For a `ComputeTaskNode` there can be any number of these, unlike the [`DataTaskNode`](@ref)s.\\
`.device`: The Device this node has been scheduled on by a [`Scheduler`](@ref).
"""
mutable struct ComputeTaskNode{TaskType <: AbstractComputeTask} <: Node
task::TaskType
parents::Vector{Node}
children::Vector{Node}
id::Base.UUID
@ -44,52 +83,22 @@ mutable struct ComputeTaskNode <: Node
nodeSplit::Union{Operation, Missing}
# for ComputeTasks there can be multiple fusions, unlike the DataTasks
nodeFusions::Vector{Operation}
nodeFusions::Vector{<:Operation}
# the device this node is assigned to execute on
device::Union{AbstractDevice, Missing}
end
DataTaskNode(t::AbstractDataTask) = DataTaskNode(
t,
Vector{Node}(),
Vector{Node}(),
UUIDs.uuid1(rng[threadid()]),
missing,
missing,
missing,
)
ComputeTaskNode(t::AbstractComputeTask) = ComputeTaskNode(
t,
Vector{Node}(),
Vector{Node}(),
UUIDs.uuid1(rng[threadid()]),
missing,
missing,
Vector{NodeFusion}(),
)
"""
Edge
Type of an edge in the graph. Edges can only exist between a [`DataTaskNode`](@ref) and a [`ComputeTaskNode`](@ref) or vice versa, not between two of the same type of node.
An edge always points from child to parent: `child = e.edge[1]` and `parent = e.edge[2]`.
The child is the prerequisite node of the parent.
"""
struct Edge
# edge points from child to parent
edge::Union{
Tuple{DataTaskNode, ComputeTaskNode},
Tuple{ComputeTaskNode, DataTaskNode},
}
edge::Union{Tuple{DataTaskNode, ComputeTaskNode}, Tuple{ComputeTaskNode, DataTaskNode}}
end
copy(m::Missing) = missing
copy(n::ComputeTaskNode) = ComputeTaskNode(
copy(n.task),
copy(n.parents),
copy(n.children),
UUIDs.uuid1(rng[threadid()]),
copy(n.nodeReduction),
copy(n.nodeSplit),
copy(n.nodeFusions),
)
copy(n::DataTaskNode) = DataTaskNode(
copy(n.task),
copy(n.parents),
copy(n.children),
UUIDs.uuid1(rng[threadid()]),
copy(n.nodeReduction),
copy(n.nodeSplit),
copy(n.nodeFusion),
)

View File

@ -1,3 +1,12 @@
"""
is_valid_node(graph::DAG, node::Node)
Verify that a given node is valid in the graph. Call like `@test is_valid_node(g, n)`. Uses `@assert` to fail if something is invalid but also provide an error message.
This function is very performance intensive and should only be used when testing or debugging.
See also this function's specific versions for the concrete Node types [`is_valid(graph::DAG, node::ComputeTaskNode)`](@ref) and [`is_valid(graph::DAG, node::DataTaskNode)`](@ref).
"""
function is_valid_node(graph::DAG, node::Node)
@assert node in graph "Node is not part of the given graph!"
@ -13,31 +22,55 @@ function is_valid_node(graph::DAG, node::Node)
@assert node in child.parents "Node is not a parent of its child!"
end
if !ismissing(node.nodeReduction)
#=if !ismissing(node.nodeReduction)
@assert is_valid(graph, node.nodeReduction)
end
if !ismissing(node.nodeSplit)
@assert is_valid(graph, node.nodeSplit)
end=#
if !(typeof(task(node)) <: FusedComputeTask)
# the remaining checks are only necessary for fused compute tasks
return true
end
# every child must be in some input of the task
for child in node.children
str = Symbol(to_var_name(child.id))
@assert (str in task(node).t1_inputs) || (str in task(node).t2_inputs) "$str was not in any of the tasks' inputs\nt1_inputs: $(task(node).t1_inputs)\nt2_inputs: $(task(node).t2_inputs)"
end
return true
end
# call with @assert
"""
is_valid(graph::DAG, node::ComputeTaskNode)
Verify that the given compute node is valid in the graph. Call with `@assert` or `@test` when testing or debugging.
This also calls [`is_valid_node(graph::DAG, node::Node)`](@ref).
"""
function is_valid(graph::DAG, node::ComputeTaskNode)
@assert is_valid_node(graph, node)
for nf in node.nodeFusions
#=for nf in node.nodeFusions
@assert is_valid(graph, nf)
end
end=#
return true
end
# call with @assert
"""
is_valid(graph::DAG, node::DataTaskNode)
Verify that the given compute node is valid in the graph. Call with `@assert` or `@test` when testing or debugging.
This also calls [`is_valid_node(graph::DAG, node::Node)`](@ref).
"""
function is_valid(graph::DAG, node::DataTaskNode)
@assert is_valid_node(graph, node)
if !ismissing(node.nodeFusion)
#=if !ismissing(node.nodeFusion)
@assert is_valid(graph, node.nodeFusion)
end
end=#
return true
end

View File

@ -1,6 +1,8 @@
# functions that apply graph operations
"""
apply_all!(graph::DAG)
# applies all unapplied operations in the DAG
Apply all unapplied operations in the DAG. Is automatically called in all functions that require the latest state of the [`DAG`](@ref).
"""
function apply_all!(graph::DAG)
while !isempty(graph.operationsToApply)
# get next operation to apply from front of the deque
@ -15,86 +17,158 @@ function apply_all!(graph::DAG)
return nothing
end
"""
apply_operation!(graph::DAG, operation::Operation)
Fallback implementation of apply_operation! for unimplemented operation types, throwing an error.
"""
function apply_operation!(graph::DAG, operation::Operation)
return error("Unknown operation type!")
end
"""
apply_operation!(graph::DAG, operation::NodeFusion)
Apply the given [`NodeFusion`](@ref) to the graph. Generic wrapper around [`node_fusion!`](@ref).
Return an [`AppliedNodeFusion`](@ref) object generated from the graph's [`Diff`](@ref).
"""
function apply_operation!(graph::DAG, operation::NodeFusion)
diff = node_fusion!(
graph,
operation.input[1],
operation.input[2],
operation.input[3],
)
diff = node_fusion!(graph, operation.input[1], operation.input[2], operation.input[3])
graph.properties += GraphProperties(diff)
return AppliedNodeFusion(operation, diff)
end
"""
apply_operation!(graph::DAG, operation::NodeReduction)
Apply the given [`NodeReduction`](@ref) to the graph. Generic wrapper around [`node_reduction!`](@ref).
Return an [`AppliedNodeReduction`](@ref) object generated from the graph's [`Diff`](@ref).
"""
function apply_operation!(graph::DAG, operation::NodeReduction)
diff = node_reduction!(graph, operation.input)
graph.properties += GraphProperties(diff)
return AppliedNodeReduction(operation, diff)
end
"""
apply_operation!(graph::DAG, operation::NodeSplit)
Apply the given [`NodeSplit`](@ref) to the graph. Generic wrapper around [`node_split!`](@ref).
Return an [`AppliedNodeSplit`](@ref) object generated from the graph's [`Diff`](@ref).
"""
function apply_operation!(graph::DAG, operation::NodeSplit)
diff = node_split!(graph, operation.input)
graph.properties += GraphProperties(diff)
return AppliedNodeSplit(operation, diff)
end
"""
revert_operation!(graph::DAG, operation::AppliedOperation)
Fallback implementation of operation reversion for unimplemented operation types, throwing an error.
"""
function revert_operation!(graph::DAG, operation::AppliedOperation)
return error("Unknown operation type!")
end
"""
revert_operation!(graph::DAG, operation::AppliedNodeFusion)
Revert the applied node fusion on the graph. Return the original [`NodeFusion`](@ref) operation.
"""
function revert_operation!(graph::DAG, operation::AppliedNodeFusion)
revert_diff!(graph, operation.diff)
return operation.operation
end
"""
revert_operation!(graph::DAG, operation::AppliedNodeReduction)
Revert the applied node fusion on the graph. Return the original [`NodeReduction`](@ref) operation.
"""
function revert_operation!(graph::DAG, operation::AppliedNodeReduction)
revert_diff!(graph, operation.diff)
return operation.operation
end
"""
revert_operation!(graph::DAG, operation::AppliedNodeSplit)
Revert the applied node fusion on the graph. Return the original [`NodeSplit`](@ref) operation.
"""
function revert_operation!(graph::DAG, operation::AppliedNodeSplit)
revert_diff!(graph, operation.diff)
return operation.operation
end
"""
revert_diff!(graph::DAG, diff::Diff)
Revert the given diff on the graph. Used to revert the individual [`AppliedOperation`](@ref)s with [`revert_operation!`](@ref).
"""
function revert_diff!(graph::DAG, diff::Diff)
# add removed nodes, remove added nodes, same for edges
# note the order
for edge in diff.addedEdges
remove_edge!(graph, edge.edge[1], edge.edge[2], false)
remove_edge!(graph, edge.edge[1], edge.edge[2], track = false)
end
for node in diff.addedNodes
remove_node!(graph, node, false)
remove_node!(graph, node, track = false)
end
for node in diff.removedNodes
insert_node!(graph, node, false)
insert_node!(graph, node, track = false)
end
for edge in diff.removedEdges
insert_edge!(graph, edge.edge[1], edge.edge[2], false)
insert_edge!(graph, edge.edge[1], edge.edge[2], track = false)
end
for (node, t) in diff.updatedChildren
# node must be fused compute task at this point
@assert typeof(task(node)) <: FusedComputeTask
node.task = t
end
graph.properties -= GraphProperties(diff)
return nothing
end
# Fuse nodes n1 -> n2 -> n3 together into one node, return the applied difference to the graph
function node_fusion!(
graph::DAG,
n1::ComputeTaskNode,
n2::DataTaskNode,
n3::ComputeTaskNode,
)
# @assert is_valid_node_fusion_input(graph, n1, n2, n3)
"""
node_fusion!(graph::DAG, n1::ComputeTaskNode, n2::DataTaskNode, n3::ComputeTaskNode)
Fuse nodes n1 -> n2 -> n3 together into one node, return the applied difference to the graph.
For details see [`NodeFusion`](@ref).
"""
function node_fusion!(graph::DAG, n1::ComputeTaskNode, n2::DataTaskNode, n3::ComputeTaskNode)
@assert is_valid_node_fusion_input(graph, n1, n2, n3)
# clear snapshot
get_snapshot_diff(graph)
# save children and parents
n1_children = children(n1)
n3_parents = parents(n3)
n3_children = children(n3)
n1Children = copy(children(n1))
n3Parents = copy(parents(n3))
n1Task = copy(task(n1))
n3Task = copy(task(n3))
# assemble the input node vectors of n1 and n3 to save into the FusedComputeTask
n1Inputs = Vector{Symbol}()
for child in n1Children
push!(n1Inputs, Symbol(to_var_name(child.id)))
end
# remove the edges and nodes that will be replaced by the fused node
remove_edge!(graph, n1, n2)
@ -103,106 +177,138 @@ function node_fusion!(
remove_node!(graph, n2)
# get n3's children now so it automatically excludes n2
n3_children = children(n3)
n3Children = copy(children(n3))
n3Inputs = Vector{Symbol}()
for child in n3Children
push!(n3Inputs, Symbol(to_var_name(child.id)))
end
remove_node!(graph, n3)
# create new node with the fused compute task
new_node =
ComputeTaskNode(FusedComputeTask{typeof(n1.task), typeof(n3.task)}())
insert_node!(graph, new_node)
newNode = ComputeTaskNode(FusedComputeTask(n1Task, n3Task, n1Inputs, Symbol(to_var_name(n2.id)), n3Inputs))
insert_node!(graph, newNode)
# use a set for combined children of n1 and n3 to not get duplicates
n1and3_children = Set{Node}()
# remove edges from n1 children to n1
for child in n1_children
for child in n1Children
remove_edge!(graph, child, n1)
push!(n1and3_children, child)
insert_edge!(graph, child, newNode)
end
# remove edges from n3 children to n3
for child in n3_children
for child in n3Children
remove_edge!(graph, child, n3)
push!(n1and3_children, child)
if !(child in n1Children)
insert_edge!(graph, child, newNode)
end
end
for child in n1and3_children
insert_edge!(graph, child, new_node)
end
# "repoint" parents of n3 from new node
for parent in n3_parents
for parent in n3Parents
remove_edge!(graph, n3, parent)
insert_edge!(graph, new_node, parent)
insert_edge!(graph, newNode, parent)
# important! update the parent node's child names in case they are fused compute tasks
# needed for compute generation so the fused compute task can correctly match inputs to its component tasks
update_child!(graph, parent, Symbol(to_var_name(n3.id)), Symbol(to_var_name(newNode.id)))
end
return get_snapshot_diff(graph)
end
"""
node_reduction!(graph::DAG, nodes::Vector{Node})
Reduce the given nodes together into one node, return the applied difference to the graph.
For details see [`NodeReduction`](@ref).
"""
function node_reduction!(graph::DAG, nodes::Vector{Node})
# @assert is_valid_node_reduction_input(graph, nodes)
@assert is_valid_node_reduction_input(graph, nodes)
# clear snapshot
get_snapshot_diff(graph)
n1 = nodes[1]
n1_children = children(n1)
n1Children = copy(children(n1))
n1_parents = Set(n1.parents)
new_parents = Set{Node}()
n1Parents = Set(n1.parents)
# set of the new parents of n1
newParents = Set{Node}()
# names of the previous children that n1 now replaces per parent
newParentsChildNames = Dict{Node, Symbol}()
# remove all of the nodes' parents and children and the nodes themselves (except for first node)
for i in 2:length(nodes)
n = nodes[i]
for child in n1_children
for child in n1Children
remove_edge!(graph, child, n)
end
for parent in parents(n)
for parent in copy(parents(n))
remove_edge!(graph, n, parent)
# collect all parents
push!(new_parents, parent)
push!(newParents, parent)
newParentsChildNames[parent] = Symbol(to_var_name(n.id))
end
remove_node!(graph, n)
end
setdiff!(new_parents, n1_parents)
for parent in new_parents
for parent in newParents
# now add parents of all input nodes to n1 without duplicates
insert_edge!(graph, n1, parent)
if !(parent in n1Parents)
# don't double insert edges
insert_edge!(graph, n1, parent)
end
# this has to be done for all parents, even the ones of n1 because they can be duplicate
prevChild = newParentsChildNames[parent]
update_child!(graph, parent, prevChild, Symbol(to_var_name(n1.id)))
end
return get_snapshot_diff(graph)
end
function node_split!(graph::DAG, n1::Node)
# @assert is_valid_node_split_input(graph, n1)
"""
node_split!(graph::DAG, n1::Node)
Split the given node into one node per parent, return the applied difference to the graph.
For details see [`NodeSplit`](@ref).
"""
function node_split!(
graph::DAG,
n1::Union{DataTaskNode{TaskType}, ComputeTaskNode{TaskType}},
) where {TaskType <: AbstractTask}
@assert is_valid_node_split_input(graph, n1)
# clear snapshot
get_snapshot_diff(graph)
n1_parents = parents(n1)
n1_children = children(n1)
n1Parents = copy(parents(n1))
n1Children = copy(children(n1))
for parent in n1_parents
for parent in n1Parents
remove_edge!(graph, n1, parent)
end
for child in n1_children
for child in n1Children
remove_edge!(graph, child, n1)
end
remove_node!(graph, n1)
for parent in n1_parents
n_copy = copy(n1)
insert_node!(graph, n_copy)
insert_edge!(graph, n_copy, parent)
for parent in n1Parents
nCopy = copy(n1)
for child in n1_children
insert_edge!(graph, child, n_copy)
insert_node!(graph, nCopy)
insert_edge!(graph, nCopy, parent)
for child in n1Children
insert_edge!(graph, child, nCopy)
end
update_child!(graph, parent, Symbol(to_var_name(n1.id)), Symbol(to_var_name(nCopy.id)))
end
return get_snapshot_diff(graph)

View File

@ -1,25 +1,30 @@
# functions for "cleaning" nodes, i.e. regenerating the possible operations for a node
# These are functions for "cleaning" nodes, i.e. regenerating the possible operations for a node
# function to find node fusions involving the given node if it's a data node
# pushes the found fusion everywhere it needs to be and returns nothing
"""
find_fusions!(graph::DAG, node::DataTaskNode)
Find node fusions involving the given data node. The function pushes the found [`NodeFusion`](@ref) (if any) everywhere it needs to be and returns nothing.
Does nothing if the node already has a node fusion set. Since it's a data node, only one node fusion can be possible with it.
"""
function find_fusions!(graph::DAG, node::DataTaskNode)
# if there is already a fusion here, skip
# if there is already a fusion here, skip to avoid duplicates
if !ismissing(node.nodeFusion)
return nothing
end
if length(node.parents) != 1 || length(node.children) != 1
if length(parents(node)) != 1 || length(children(node)) != 1
return nothing
end
child_node = first(node.children)
parent_node = first(node.parents)
child_node = first(children(node))
parent_node = first(parents(node))
if !(child_node in graph) || !(parent_node in graph)
error("Parents/Children that are not in the graph!!!")
end
if length(child_node.parents) != 1
if length(parents(child_node)) != 1
return nothing
end
@ -32,20 +37,29 @@ function find_fusions!(graph::DAG, node::DataTaskNode)
return nothing
end
"""
find_fusions!(graph::DAG, node::ComputeTaskNode)
Find node fusions involving the given compute node. The function pushes the found [`NodeFusion`](@ref)s (if any) everywhere they need to be and returns nothing.
"""
function find_fusions!(graph::DAG, node::ComputeTaskNode)
# just find fusions in neighbouring DataTaskNodes
for child in node.children
for child in children(node)
find_fusions!(graph, child)
end
for parent in node.parents
for parent in parents(node)
find_fusions!(graph, parent)
end
return nothing
end
"""
find_reductions!(graph::DAG, node::Node)
Find node reductions involving the given node. The function pushes the found [`NodeReduction`](@ref) (if any) everywhere it needs to be and returns nothing.
"""
function find_reductions!(graph::DAG, node::Node)
# there can only be one reduction per node, avoid adding duplicates
if !ismissing(node.nodeReduction)
@ -57,14 +71,8 @@ function find_reductions!(graph::DAG, node::Node)
partners_ = partners(node)
delete!(partners_, node)
for partner in partners_
if partner graph.nodes
error("Partner is not part of the graph")
end
@assert partner in graph.nodes
if can_reduce(node, partner)
if Set(node.children) != Set(partner.children)
error("Not equal children")
end
if reductionVector === nothing
# only when there's at least one reduction partner, insert the vector
reductionVector = Vector{Node}()
@ -91,6 +99,11 @@ function find_reductions!(graph::DAG, node::Node)
return nothing
end
"""
find_splits!(graph::DAG, node::Node)
Find the node split of the given node. The function pushes the found [`NodeSplit`](@ref) (if any) everywhere it needs to be and returns nothing.
"""
function find_splits!(graph::DAG, node::Node)
if !ismissing(node.nodeSplit)
return nothing
@ -105,11 +118,20 @@ function find_splits!(graph::DAG, node::Node)
return nothing
end
# "clean" the operations on a dirty node
function clean_node!(graph::DAG, node::Node)
"""
clean_node!(graph::DAG, node::Node)
Sort this node's parent and child sets, then find fusions, reductions and splits involving it. Needs to be called after the node was changed in some way.
"""
function clean_node!(
graph::DAG,
node::Union{DataTaskNode{TaskType}, ComputeTaskNode{TaskType}},
) where {TaskType <: AbstractTask}
sort_node!(node)
find_fusions!(graph, node)
find_reductions!(graph, node)
return find_splits!(graph, node)
find_splits!(graph, node)
return nothing
end

View File

@ -2,10 +2,12 @@
using Base.Threads
function insert_operation!(
nf::NodeFusion,
locks::Dict{ComputeTaskNode, SpinLock},
)
"""
insert_operation!(nf::NodeFusion, locks::Dict{ComputeTaskNode, SpinLock})
Insert the given node fusion into its input nodes' operation caches. For the compute nodes, locking via the given `locks` is employed to have safe multi-threading. For a large set of nodes, contention on the locks should be very small.
"""
function insert_operation!(nf::NodeFusion, locks::Dict{ComputeTaskNode, SpinLock})
n1 = nf.input[1]
n2 = nf.input[2]
n3 = nf.input[3]
@ -20,6 +22,11 @@ function insert_operation!(
return nothing
end
"""
insert_operation!(nf::NodeReduction)
Insert the given node reduction into its input nodes' operation caches. This is thread-safe.
"""
function insert_operation!(nr::NodeReduction)
for n in nr.input
n.nodeReduction = nr
@ -27,15 +34,22 @@ function insert_operation!(nr::NodeReduction)
return nothing
end
"""
insert_operation!(nf::NodeSplit)
Insert the given node split into its input node's operation cache. This is thread-safe.
"""
function insert_operation!(ns::NodeSplit)
ns.input.nodeSplit = ns
return nothing
end
function nr_insertion!(
operations::PossibleOperations,
nodeReductions::Vector{Vector{NodeReduction}},
)
"""
nr_insertion!(operations::PossibleOperations, nodeReductions::Vector{Vector{NodeReduction}})
Insert the node reductions into the graph and the nodes' caches. Employs multithreading for speedup.
"""
function nr_insertion!(operations::PossibleOperations, nodeReductions::Vector{Vector{NodeReduction}})
total_len = 0
for vec in nodeReductions
total_len += length(vec)
@ -58,11 +72,12 @@ function nr_insertion!(
return nothing
end
function nf_insertion!(
graph::DAG,
operations::PossibleOperations,
nodeFusions::Vector{Vector{NodeFusion}},
)
"""
nf_insertion!(graph::DAG, operations::PossibleOperations, nodeFusions::Vector{Vector{NodeFusion}})
Insert the node fusions into the graph and the nodes' caches. Employs multithreading for speedup.
"""
function nf_insertion!(graph::DAG, operations::PossibleOperations, nodeFusions::Vector{Vector{NodeFusion}})
total_len = 0
for vec in nodeFusions
total_len += length(vec)
@ -92,10 +107,12 @@ function nf_insertion!(
return nothing
end
function ns_insertion!(
operations::PossibleOperations,
nodeSplits::Vector{Vector{NodeSplit}},
)
"""
ns_insertion!(operations::PossibleOperations, nodeSplits::Vector{Vector{NodeSplits}})
Insert the node splits into the graph and the nodes' caches. Employs multithreading for speedup.
"""
function ns_insertion!(operations::PossibleOperations, nodeSplits::Vector{Vector{NodeSplit}})
total_len = 0
for vec in nodeSplits
total_len += length(vec)
@ -118,8 +135,14 @@ function ns_insertion!(
return nothing
end
# function to generate all possible operations on the graph
function generate_options(graph::DAG)
"""
generate_operations(graph::DAG)
Generate all possible operations on the graph. Used initially when the graph is freshly assembled or parsed. Uses multithreading for speedup.
Safely inserts all the found operations into the graph and its nodes.
"""
function generate_operations(graph::DAG)
generatedFusions = [Vector{NodeFusion}() for _ in 1:nthreads()]
generatedReductions = [Vector{NodeReduction}() for _ in 1:nthreads()]
generatedSplits = [Vector{NodeSplit}() for _ in 1:nthreads()]
@ -180,31 +203,27 @@ function generate_options(graph::DAG)
# --- find possible node fusions ---
@threads for node in nodeArray
if (typeof(node) <: DataTaskNode)
if length(node.parents) != 1
if length(parents(node)) != 1
# data node can only have a single parent
continue
end
parent_node = first(node.parents)
parent_node = first(parents(node))
if length(node.children) != 1
if length(children(node)) != 1
# this node is an entry node or has multiple children which should not be possible
continue
end
child_node = first(node.children)
if (length(child_node.parents) != 1)
child_node = first(children(node))
if (length(parents(child_node)) != 1)
continue
end
push!(
generatedFusions[threadid()],
NodeFusion((child_node, node, parent_node)),
)
push!(generatedFusions[threadid()], NodeFusion((child_node, node, parent_node)))
end
end
# launch thread for node fusion insertion
nf_task =
@task nf_insertion!(graph, graph.possibleOperations, generatedFusions)
nf_task = @task nf_insertion!(graph, graph.possibleOperations, generatedFusions)
schedule(nf_task)
# find possible node splits

View File

@ -2,16 +2,19 @@
using Base.Threads
"""
get_operations(graph::DAG)
Return the [`PossibleOperations`](@ref) of the graph at the current state.
"""
function get_operations(graph::DAG)
apply_all!(graph)
if isempty(graph.possibleOperations)
generate_options(graph)
generate_operations(graph)
end
for node in graph.dirtyNodes
clean_node!(graph, node)
end
clean_node!.(Ref(graph), graph.dirtyNodes)
empty!(graph.dirtyNodes)
return graph.possibleOperations

39
src/operation/iterate.jl Normal file
View File

@ -0,0 +1,39 @@
import Base.iterate
const _POSSIBLE_OPERATIONS_FIELDS = fieldnames(PossibleOperations)
_POIteratorStateType =
NamedTuple{(:result, :state), Tuple{Union{NodeFusion, NodeReduction, NodeSplit}, Tuple{Symbol, Int64}}}
@inline function iterate(possibleOperations::PossibleOperations)::Union{Nothing, _POIteratorStateType}
for fieldname in _POSSIBLE_OPERATIONS_FIELDS
iterator = iterate(getfield(possibleOperations, fieldname))
if (!isnothing(iterator))
return (result = iterator[1], state = (fieldname, iterator[2]))
end
end
return nothing
end
@inline function iterate(possibleOperations::PossibleOperations, state)::Union{Nothing, _POIteratorStateType}
newStateSym = state[1]
newStateIt = iterate(getfield(possibleOperations, newStateSym), state[2])
if !isnothing(newStateIt)
return (result = newStateIt[1], state = (newStateSym, newStateIt[2]))
end
# cycle to next field
index = findfirst(x -> x == newStateSym, _POSSIBLE_OPERATIONS_FIELDS) + 1
while index <= length(_POSSIBLE_OPERATIONS_FIELDS)
newStateSym = _POSSIBLE_OPERATIONS_FIELDS[index]
newStateIt = iterate(getfield(possibleOperations, newStateSym))
if !isnothing(newStateIt)
return (result = newStateIt[1], state = (newStateSym, newStateIt[2]))
end
index += 1
end
return nothing
end

View File

@ -1,3 +1,8 @@
"""
show(io::IO, ops::PossibleOperations)
Print a string representation of the set of possible operations to io.
"""
function show(io::IO, ops::PossibleOperations)
print(io, length(ops.nodeFusions))
println(io, " Node Fusions: ")
@ -16,23 +21,38 @@ function show(io::IO, ops::PossibleOperations)
end
end
"""
show(io::IO, op::NodeReduction)
Print a string representation of the node reduction to io.
"""
function show(io::IO, op::NodeReduction)
print(io, "NR: ")
print(io, length(op.input))
print(io, "x")
return print(io, op.input[1].task)
return print(io, task(op.input[1]))
end
"""
show(io::IO, op::NodeSplit)
Print a string representation of the node split to io.
"""
function show(io::IO, op::NodeSplit)
print(io, "NS: ")
return print(io, op.input.task)
return print(io, task(op.input))
end
"""
show(io::IO, op::NodeFusion)
Print a string representation of the node fusion to io.
"""
function show(io::IO, op::NodeFusion)
print(io, "NF: ")
print(io, op.input[1].task)
print(io, task(op.input[1]))
print(io, "->")
print(io, op.input[2].task)
print(io, task(op.input[2]))
print(io, "->")
return print(io, op.input[3].task)
return print(io, task(op.input[3]))
end

View File

@ -1,34 +1,122 @@
# An abstract base class for operations
# an operation can be applied to a DAG
"""
Operation
An abstract base class for operations. An operation can be applied to a [`DAG`](@ref), changing its nodes and edges.
Possible operations on a [`DAG`](@ref) can be retrieved using [`get_operations`](@ref).
See also: [`push_operation!`](@ref), [`pop_operation!`](@ref)
"""
abstract type Operation end
# An abstract base class for already applied operations
# an applied operation can be reversed iff it is the last applied operation on the DAG
"""
AppliedOperation
An abstract base class for already applied operations.
An applied operation can be reversed iff it is the last applied operation on the DAG.
Every applied operation stores a [`Diff`](@ref) from when it was initially applied to be able to revert the operation.
See also: [`revert_operation!`](@ref).
"""
abstract type AppliedOperation end
struct NodeFusion <: Operation
input::Tuple{ComputeTaskNode, DataTaskNode, ComputeTaskNode}
"""
NodeFusion <: Operation
The NodeFusion operation. Represents the fusing of a chain of compute node -> data node -> compute node.
After the node fusion is applied, the graph has 2 fewer nodes and edges, and a new [`FusedComputeTask`](@ref) with the two input compute nodes as parts.
# Requirements for successful application
A chain of (n1, n2, n3) can be fused if:
- All nodes are in the graph.
- (n1, n2) is an edge in the graph.
- (n2, n3) is an edge in the graph.
- n2 has exactly one parent (n3) and exactly one child (n1).
- n1 has exactly one parent (n2).
[`is_valid_node_fusion_input`](@ref) can be used to `@assert` these requirements.
See also: [`can_fuse`](@ref)
"""
struct NodeFusion{TaskType1 <: AbstractComputeTask, TaskType2 <: AbstractDataTask, TaskType3 <: AbstractComputeTask} <:
Operation
input::Tuple{ComputeTaskNode{TaskType1}, DataTaskNode{TaskType2}, ComputeTaskNode{TaskType3}}
end
struct AppliedNodeFusion <: AppliedOperation
operation::NodeFusion
"""
AppliedNodeFusion <: AppliedOperation
The applied version of the [`NodeFusion`](@ref).
"""
struct AppliedNodeFusion{
TaskType1 <: AbstractComputeTask,
TaskType2 <: AbstractDataTask,
TaskType3 <: AbstractComputeTask,
} <: AppliedOperation
operation::NodeFusion{TaskType1, TaskType2, TaskType3}
diff::Diff
end
struct NodeReduction <: Operation
input::Vector{Node}
"""
NodeReduction <: Operation
The NodeReduction operation. Represents the reduction of two or more nodes with one another.
Only one of the input nodes is kept, while all others are deleted and their parents are accumulated in the kept node's parents instead.
After the node reduction is applied, the graph has `length(nr.input) - 1` fewer nodes.
# Requirements for successful application
A vector of nodes can be reduced if:
- All nodes are in the graph.
- All nodes have the same task type.
- All nodes have the same set of children.
[`is_valid_node_reduction_input`](@ref) can be used to `@assert` these requirements.
See also: [`can_reduce`](@ref)
"""
struct NodeReduction{NodeType <: Node} <: Operation
input::Vector{NodeType}
end
struct AppliedNodeReduction <: AppliedOperation
operation::NodeReduction
"""
AppliedNodeReduction <: AppliedOperation
The applied version of the [`NodeReduction`](@ref).
"""
struct AppliedNodeReduction{NodeType <: Node} <: AppliedOperation
operation::NodeReduction{NodeType}
diff::Diff
end
struct NodeSplit <: Operation
input::Node
"""
NodeSplit <: Operation
The NodeSplit operation. Represents the split of its input node into one node for each of its parents. It is the reverse operation to the [`NodeReduction`](@ref).
# Requirements for successful application
A node can be split if:
- It is in the graph.
- It has at least 2 parents.
[`is_valid_node_split_input`](@ref) can be used to `@assert` these requirements.
See also: [`can_split`](@ref)
"""
struct NodeSplit{NodeType <: Node} <: Operation
input::NodeType
end
struct AppliedNodeSplit <: AppliedOperation
operation::NodeSplit
"""
AppliedNodeSplit <: AppliedOperation
The applied version of the [`NodeSplit`](@ref).
"""
struct AppliedNodeSplit{NodeType <: Node} <: AppliedOperation
operation::NodeSplit{NodeType}
diff::Diff
end

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@ -1,10 +1,17 @@
"""
isempty(operations::PossibleOperations)
Return whether `operations` is empty, i.e. all of its fields are empty.
"""
function isempty(operations::PossibleOperations)
return isempty(operations.nodeFusions) &&
isempty(operations.nodeReductions) &&
isempty(operations.nodeSplits)
return isempty(operations.nodeFusions) && isempty(operations.nodeReductions) && isempty(operations.nodeSplits)
end
"""
length(operations::PossibleOperations)
Return a named tuple with the number of each of the operation types as a named tuple. The fields are named the same as the [`PossibleOperations`](@ref)'.
"""
function length(operations::PossibleOperations)
return (
nodeFusions = length(operations.nodeFusions),
@ -13,44 +20,69 @@ function length(operations::PossibleOperations)
)
end
"""
delete!(operations::PossibleOperations, op::NodeFusion)
Delete the given node fusion from the possible operations.
"""
function delete!(operations::PossibleOperations, op::NodeFusion)
delete!(operations.nodeFusions, op)
return operations
end
"""
delete!(operations::PossibleOperations, op::NodeReduction)
Delete the given node reduction from the possible operations.
"""
function delete!(operations::PossibleOperations, op::NodeReduction)
delete!(operations.nodeReductions, op)
return operations
end
"""
delete!(operations::PossibleOperations, op::NodeSplit)
Delete the given node split from the possible operations.
"""
function delete!(operations::PossibleOperations, op::NodeSplit)
delete!(operations.nodeSplits, op)
return operations
end
"""
can_fuse(n1::ComputeTaskNode, n2::DataTaskNode, n3::ComputeTaskNode)
Return whether the given nodes can be fused. See [`NodeFusion`](@ref) for the requirements.
"""
function can_fuse(n1::ComputeTaskNode, n2::DataTaskNode, n3::ComputeTaskNode)
if !is_child(n1, n2) || !is_child(n2, n3)
# the checks are redundant but maybe a good sanity check
return false
end
if length(n2.parents) != 1 ||
length(n2.children) != 1 ||
length(n1.parents) != 1
if length(parents(n2)) != 1 || length(children(n2)) != 1 || length(parents(n1)) != 1
return false
end
return true
end
function can_reduce(n1::Node, n2::Node)
if (n1.task != n2.task)
return false
end
"""
can_reduce(n1::Node, n2::Node)
n1_length = length(n1.children)
n2_length = length(n2.children)
Return whether the given two nodes can be reduced. See [`NodeReduction`](@ref) for the requirements.
"""
function can_reduce(n1::Node, n2::Node)
return false
end
function can_reduce(
n1::NodeType,
n2::NodeType,
) where {TaskType <: AbstractTask, NodeType <: Union{DataTaskNode{TaskType}, ComputeTaskNode{TaskType}}}
n1_length = length(children(n1))
n2_length = length(children(n2))
if (n1_length != n2_length)
return false
@ -59,19 +91,19 @@ function can_reduce(n1::Node, n2::Node)
# this seems to be the most common case so do this first
# doing it manually is a lot faster than using the sets for a general solution
if (n1_length == 2)
if (n1.children[1] != n2.children[1])
if (n1.children[1] != n2.children[2])
if (children(n1)[1] != children(n2)[1])
if (children(n1)[1] != children(n2)[2])
return false
end
# 1_1 == 2_2
if (n1.children[2] != n2.children[1])
if (children(n1)[2] != children(n2)[1])
return false
end
return true
end
# 1_1 == 2_1
if (n1.children[2] != n2.children[2])
if (children(n1)[2] != children(n2)[2])
return false
end
return true
@ -79,33 +111,63 @@ function can_reduce(n1::Node, n2::Node)
# this is simple
if (n1_length == 1)
return n1.children[1] == n2.children[1]
return children(n1)[1] == children(n2)[1]
end
# this takes a long time
return Set(n1.children) == Set(n2.children)
return Set(children(n1)) == Set(children(n2))
end
"""
can_split(n1::Node)
Return whether the given node can be split. See [`NodeSplit`](@ref) for the requirements.
"""
function can_split(n::Node)
return length(parents(n)) > 1
end
"""
==(op1::Operation, op2::Operation)
Fallback implementation of operation equality. Return false. Actual comparisons are done by the overloads of same type operation comparisons.
"""
function ==(op1::Operation, op2::Operation)
return false
end
function ==(op1::NodeFusion, op2::NodeFusion)
"""
==(op1::NodeFusion, op2::NodeFusion)
Equality comparison between two node fusions. Two node fusions are considered equal if they have the same inputs.
"""
function ==(
op1::NodeFusion{ComputeTaskType1, DataTaskType, ComputeTaskType2},
op2::NodeFusion{ComputeTaskType1, DataTaskType, ComputeTaskType2},
) where {
ComputeTaskType1 <: AbstractComputeTask,
DataTaskType <: AbstractDataTask,
ComputeTaskType2 <: AbstractComputeTask,
}
# there can only be one node fusion on a given data task, so if the data task is the same, the fusion is the same
return op1.input[2] == op2.input[2]
end
"""
==(op1::NodeReduction, op2::NodeReduction)
Equality comparison between two node reductions. Two node reductions are considered equal when they have the same inputs.
"""
function ==(op1::NodeReduction, op2::NodeReduction)
# node reductions are equal exactly if their first input is the same
return op1.input[1].id == op2.input[1].id
end
"""
==(op1::NodeSplit, op2::NodeSplit)
Equality comparison between two node splits. Two node splits are considered equal if they have the same input node.
"""
function ==(op1::NodeSplit, op2::NodeSplit)
return op1.input == op2.input
end
copy(id::UUID) = UUID(id.value)

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@ -2,24 +2,19 @@
# should be called with @assert
# the functions throw their own errors though, to still have helpful error messages
function is_valid_node_fusion_input(
graph::DAG,
n1::ComputeTaskNode,
n2::DataTaskNode,
n3::ComputeTaskNode,
)
"""
is_valid_node_fusion_input(graph::DAG, n1::ComputeTaskNode, n2::DataTaskNode, n3::ComputeTaskNode)
Assert for a gven node fusion input whether the nodes can be fused. For the requirements of a node fusion see [`NodeFusion`](@ref).
Intended for use with `@assert` or `@test`.
"""
function is_valid_node_fusion_input(graph::DAG, n1::ComputeTaskNode, n2::DataTaskNode, n3::ComputeTaskNode)
if !(n1 in graph) || !(n2 in graph) || !(n3 in graph)
throw(
AssertionError(
"[Node Fusion] The given nodes are not part of the given graph",
),
)
throw(AssertionError("[Node Fusion] The given nodes are not part of the given graph"))
end
if !is_child(n1, n2) ||
!is_child(n2, n3) ||
!is_parent(n3, n2) ||
!is_parent(n2, n1)
if !is_child(n1, n2) || !is_child(n2, n3) || !is_parent(n3, n2) || !is_parent(n2, n1)
throw(
AssertionError(
"[Node Fusion] The given nodes are not connected by edges which is required for node fusion",
@ -28,49 +23,47 @@ function is_valid_node_fusion_input(
end
if length(n2.parents) > 1
throw(
AssertionError(
"[Node Fusion] The given data node has more than one parent",
),
)
throw(AssertionError("[Node Fusion] The given data node has more than one parent"))
end
if length(n2.children) > 1
throw(
AssertionError(
"[Node Fusion] The given data node has more than one child",
),
)
throw(AssertionError("[Node Fusion] The given data node has more than one child"))
end
if length(n1.parents) > 1
throw(
AssertionError(
"[Node Fusion] The given n1 has more than one parent",
),
)
throw(AssertionError("[Node Fusion] The given n1 has more than one parent"))
end
@assert is_valid(graph, n1)
@assert is_valid(graph, n2)
@assert is_valid(graph, n3)
return true
end
"""
is_valid_node_reduction_input(graph::DAG, nodes::Vector{Node})
Assert for a gven node reduction input whether the nodes can be reduced. For the requirements of a node reduction see [`NodeReduction`](@ref).
Intended for use with `@assert` or `@test`.
"""
function is_valid_node_reduction_input(graph::DAG, nodes::Vector{Node})
for n in nodes
if n graph
throw(
AssertionError(
"[Node Reduction] The given nodes are not part of the given graph",
),
)
throw(AssertionError("[Node Reduction] The given nodes are not part of the given graph"))
end
@assert is_valid(graph, n)
end
t = typeof(nodes[1].task)
t = typeof(task(nodes[1]))
for n in nodes
if typeof(n.task) != t
throw(
AssertionError(
"[Node Reduction] The given nodes are not of the same type",
),
)
if typeof(task(n)) != t
throw(AssertionError("[Node Reduction] The given nodes are not of the same type"))
end
if (typeof(n) <: DataTaskNode)
if (n.name != nodes[1].name)
throw(AssertionError("[Node Reduction] The given nodes do not have the same name"))
end
end
end
@ -88,13 +81,16 @@ function is_valid_node_reduction_input(graph::DAG, nodes::Vector{Node})
return true
end
"""
is_valid_node_split_input(graph::DAG, n1::Node)
Assert for a gven node split input whether the node can be split. For the requirements of a node split see [`NodeSplit`](@ref).
Intended for use with `@assert` or `@test`.
"""
function is_valid_node_split_input(graph::DAG, n1::Node)
if n1 graph
throw(
AssertionError(
"[Node Split] The given node is not part of the given graph",
),
)
throw(AssertionError("[Node Split] The given node is not part of the given graph"))
end
if length(n1.parents) <= 1
@ -105,28 +101,46 @@ function is_valid_node_split_input(graph::DAG, n1::Node)
)
end
@assert is_valid(graph, n1)
return true
end
"""
is_valid(graph::DAG, nr::NodeReduction)
Assert for a given [`NodeReduction`](@ref) whether it is a valid operation in the graph.
Intended for use with `@assert` or `@test`.
"""
function is_valid(graph::DAG, nr::NodeReduction)
@assert is_valid_node_reduction_input(graph, nr.input)
@assert nr in graph.possibleOperations.nodeReductions "NodeReduction is not part of the graph's possible operations!"
#@assert nr in graph.possibleOperations.nodeReductions "NodeReduction is not part of the graph's possible operations!"
return true
end
"""
is_valid(graph::DAG, nr::NodeSplit)
Assert for a given [`NodeSplit`](@ref) whether it is a valid operation in the graph.
Intended for use with `@assert` or `@test`.
"""
function is_valid(graph::DAG, ns::NodeSplit)
@assert is_valid_node_split_input(graph, ns.input)
@assert ns in graph.possibleOperations.nodeSplits "NodeSplit is not part of the graph's possible operations!"
#@assert ns in graph.possibleOperations.nodeSplits "NodeSplit is not part of the graph's possible operations!"
return true
end
"""
is_valid(graph::DAG, nr::NodeFusion)
Assert for a given [`NodeFusion`](@ref) whether it is a valid operation in the graph.
Intended for use with `@assert` or `@test`.
"""
function is_valid(graph::DAG, nf::NodeFusion)
@assert is_valid_node_fusion_input(
graph,
nf.input[1],
nf.input[2],
nf.input[3],
)
@assert nf in graph.possibleOperations.nodeFusions "NodeFusion is not part of the graph's possible operations!"
@assert is_valid_node_fusion_input(graph, nf.input[1], nf.input[2], nf.input[3])
#@assert nf in graph.possibleOperations.nodeFusions "NodeFusion is not part of the graph's possible operations!"
return true
end

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@ -0,0 +1,73 @@
"""
GreedyOptimizer
An implementation of the greedy optimization algorithm, simply choosing the best next option evaluated with the given estimator.
The fixpoint is reached when any leftover operation would increase the graph's total cost according to the given estimator.
"""
struct GreedyOptimizer{EstimatorType <: AbstractEstimator} <: AbstractOptimizer
estimator::EstimatorType
end
function optimize_step!(optimizer::GreedyOptimizer, graph::DAG)
# generate all options
operations = get_operations(graph)
if isempty(operations)
return false
end
result = nothing
lowestCost = reduce(
(acc, op) -> begin
op_cost = operation_effect(optimizer.estimator, graph, op)
if op_cost < acc
result = op
return op_cost
end
return acc
end,
operations;
init = typemax(cost_type(optimizer.estimator)),
)
if lowestCost > zero(cost_type(optimizer.estimator))
return false
end
push_operation!(graph, result)
return true
end
function fixpoint_reached(optimizer::GreedyOptimizer, graph::DAG)
# generate all options
operations = get_operations(graph)
if isempty(operations)
return true
end
lowestCost = reduce(
(acc, op) -> begin
op_cost = operation_effect(optimizer.estimator, graph, op)
if op_cost < acc
return op_cost
end
return acc
end,
operations;
init = typemax(cost_type(optimizer.estimator)),
)
if lowestCost > zero(cost_type(optimizer.estimator))
return true
end
return false
end
function optimize_to_fixpoint!(optimizer::GreedyOptimizer, graph::DAG)
while optimize_step!(optimizer, graph)
end
return nothing
end

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@ -0,0 +1,60 @@
"""
AbstractOptimizer
Abstract base type for optimizer implementations.
"""
abstract type AbstractOptimizer end
"""
optimize_step!(optimizer::AbstractOptimizer, graph::DAG)
Interface function that must be implemented by implementations of [`AbstractOptimizer`](@ref). Returns `true` if an operations has been applied, `false` if not, usually when a fixpoint of the algorithm has been reached.
It should do one smallest logical step on the given [`DAG`](@ref), muting the graph and, if necessary, the optimizer's state.
"""
function optimize_step! end
"""
optimize!(optimizer::AbstractOptimizer, graph::DAG, n::Int)
Function calling the given optimizer `n` times, muting the graph. Returns `true` if the requested number of operations has been applied, `false` if not, usually when a fixpoint of the algorithm has been reached.
If a more efficient method exists, this can be overloaded for a specific optimizer.
"""
function optimize!(optimizer::AbstractOptimizer, graph::DAG, n::Int)
for i in 1:n
if !optimize_step!(optimizer, graph)
return false
end
end
return true
end
"""
fixpoint_reached(optimizer::AbstractOptimizer, graph::DAG)
Interface function that can be implemented by optimization algorithms that can reach a fixpoint, returning as a `Bool` whether it has been reached. The default implementation returns `false`.
See also: [`optimize_to_fixpoint!`](@ref)
"""
function fixpoint_reached(optimizer::AbstractOptimizer, graph::DAG)
return false
end
"""
optimize_to_fixpoint!(optimizer::AbstractOptimizer, graph::DAG)
Interface function that can be implemented by optimization algorithms that can reach a fixpoint. The algorithm will be run until that fixpoint is reached, at which point [`fixpoint_reached`](@ref) should return true.
A usual implementation might look like this:
```julia
function optimize_to_fixpoint!(optimizer::MyOptimizer, graph::DAG)
while !fixpoint_reached(optimizer, graph)
optimize_step!(optimizer, graph)
end
return nothing
end
```
"""
function optimize_to_fixpoint! end

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@ -0,0 +1,49 @@
using Random
"""
RandomWalkOptimizer
An optimizer that randomly pushes or pops operations. It doesn't optimize in any direction and is useful mainly for testing purposes.
This algorithm never reaches a fixpoint, so it does not implement [`optimize_to_fixpoint`](@ref).
"""
struct RandomWalkOptimizer <: AbstractOptimizer
rng::AbstractRNG
end
function optimize_step!(optimizer::RandomWalkOptimizer, graph::DAG)
operations = get_operations(graph)
if sum(length(operations)) == 0 && length(graph.appliedOperations) + length(graph.operationsToApply) == 0
# in case there are zero operations possible at all on the graph
return false
end
r = optimizer.rng
# try until something was applied or popped
while true
# choose push or pop
if rand(r, Bool)
# push
# choose one of fuse/split/reduce
option = rand(r, 1:3)
if option == 1 && !isempty(operations.nodeFusions)
push_operation!(graph, rand(r, collect(operations.nodeFusions)))
return true
elseif option == 2 && !isempty(operations.nodeReductions)
push_operation!(graph, rand(r, collect(operations.nodeReductions)))
return true
elseif option == 3 && !isempty(operations.nodeSplits)
push_operation!(graph, rand(r, collect(operations.nodeSplits)))
return true
end
else
# pop
if (can_pop(graph))
pop_operation!(graph)
return true
end
end
end
end

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@ -0,0 +1,30 @@
"""
ReductionOptimizer
An optimizer that simply applies an available [`NodeReduction`](@ref) on each step. It implements [`optimize_to_fixpoint`](@ref). The fixpoint is reached when there are no more possible [`NodeReduction`](@ref)s in the graph.
"""
struct ReductionOptimizer <: AbstractOptimizer end
function optimize_step!(optimizer::ReductionOptimizer, graph::DAG)
# generate all options
operations = get_operations(graph)
if fixpoint_reached(optimizer, graph)
return false
end
push_operation!(graph, first(operations.nodeReductions))
return true
end
function fixpoint_reached(optimizer::ReductionOptimizer, graph::DAG)
operations = get_operations(graph)
return isempty(operations.nodeReductions)
end
function optimize_to_fixpoint!(optimizer::ReductionOptimizer, graph::DAG)
while !fixpoint_reached(optimizer, graph)
optimize_step!(optimizer, graph)
end
return nothing
end

72
src/properties/create.jl Normal file
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@ -0,0 +1,72 @@
"""
GraphProperties()
Create an empty [`GraphProperties`](@ref) object.
"""
function GraphProperties()
return (data = 0.0, computeEffort = 0.0, computeIntensity = 0.0, noNodes = 0, noEdges = 0)::GraphProperties
end
@inline function _props(
node::DataTaskNode{TaskType},
)::Tuple{Float64, Float64, Int64} where {TaskType <: AbstractDataTask}
return (data(task(node)) * length(parents(node)), 0.0, length(parents(node)))
end
@inline function _props(
node::ComputeTaskNode{TaskType},
)::Tuple{Float64, Float64, Int64} where {TaskType <: AbstractComputeTask}
return (0.0, compute_effort(task(node)), length(parents(node)))
end
"""
GraphProperties(graph::DAG)
Calculate the graph's properties and return the constructed [`GraphProperties`](@ref) object.
"""
function GraphProperties(graph::DAG)
# make sure the graph is fully generated
apply_all!(graph)
d = 0.0
ce = 0.0
ed = 0
for node in graph.nodes
props = _props(node)
d += props[1]
ce += props[2]
ed += props[3]
end
return (
data = d,
computeEffort = ce,
computeIntensity = (d == 0) ? 0.0 : ce / d,
noNodes = length(graph.nodes),
noEdges = ed,
)::GraphProperties
end
"""
GraphProperties(diff::Diff)
Create the graph properties difference from a given [`Diff`](@ref).
The graph's properties after applying the [`Diff`](@ref) will be `get_properties(graph) + GraphProperties(diff)`.
For reverting a diff, it's `get_properties(graph) - GraphProperties(diff)`.
"""
function GraphProperties(diff::Diff)
ce =
reduce(+, compute_effort(task(n)) for n in diff.addedNodes; init = 0.0) -
reduce(+, compute_effort(task(n)) for n in diff.removedNodes; init = 0.0)
d =
reduce(+, data(task(n)) for n in diff.addedNodes; init = 0.0) -
reduce(+, data(task(n)) for n in diff.removedNodes; init = 0.0)
return (
data = d,
computeEffort = ce,
computeIntensity = (d == 0) ? 0.0 : ce / d,
noNodes = length(diff.addedNodes) - length(diff.removedNodes),
noEdges = length(diff.addedEdges) - length(diff.removedEdges),
)::GraphProperties
end

16
src/properties/type.jl Normal file
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@ -0,0 +1,16 @@
"""
GraphProperties
Representation of a [`DAG`](@ref)'s properties.
# Fields:
`.data`: The total data transfer.\\
`.computeEffort`: The total compute effort.\\
`.computeIntensity`: The compute intensity, will always equal `.computeEffort / .data`.\\
`.noNodes`: Number of [`Node`](@ref)s.\\
`.noEdges`: Number of [`Edge`](@ref)s.
"""
const GraphProperties = NamedTuple{
(:data, :computeEffort, :computeIntensity, :noNodes, :noEdges),
Tuple{Float64, Float64, Float64, Int, Int},
}

54
src/properties/utility.jl Normal file
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@ -0,0 +1,54 @@
"""
-(prop1::GraphProperties, prop2::GraphProperties)
Subtract `prop1` from `prop2` and return the result as a new [`GraphProperties`](@ref).
Also take care to keep consistent compute intensity.
"""
function -(prop1::GraphProperties, prop2::GraphProperties)
return (
data = prop1.data - prop2.data,
computeEffort = prop1.computeEffort - prop2.computeEffort,
computeIntensity = if (prop1.data - prop2.data == 0)
0.0
else
(prop1.computeEffort - prop2.computeEffort) / (prop1.data - prop2.data)
end,
noNodes = prop1.noNodes - prop2.noNodes,
noEdges = prop1.noEdges - prop2.noEdges,
)::GraphProperties
end
"""
+(prop1::GraphProperties, prop2::GraphProperties)
Add `prop1` and `prop2` and return the result as a new [`GraphProperties`](@ref).
Also take care to keep consistent compute intensity.
"""
function +(prop1::GraphProperties, prop2::GraphProperties)
return (
data = prop1.data + prop2.data,
computeEffort = prop1.computeEffort + prop2.computeEffort,
computeIntensity = if (prop1.data + prop2.data == 0)
0.0
else
(prop1.computeEffort + prop2.computeEffort) / (prop1.data + prop2.data)
end,
noNodes = prop1.noNodes + prop2.noNodes,
noEdges = prop1.noEdges + prop2.noEdges,
)::GraphProperties
end
"""
-(prop::GraphProperties)
Unary negation of the graph properties. `.computeIntensity` will not be negated because `.data` and `.computeEffort` both are.
"""
function -(prop::GraphProperties)
return (
data = -prop.data,
computeEffort = -prop.computeEffort,
computeIntensity = prop.computeIntensity, # no negation here!
noNodes = -prop.noNodes,
noEdges = -prop.noEdges,
)::GraphProperties
end

50
src/scheduler/greedy.jl Normal file
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@ -0,0 +1,50 @@
"""
GreedyScheduler
A greedy implementation of a scheduler, creating a topological ordering of nodes and naively balancing them onto the different devices.
"""
struct GreedyScheduler end
function schedule_dag(::GreedyScheduler, graph::DAG, machine::Machine)
nodeQueue = PriorityQueue{Node, Int}()
# use a priority equal to the number of unseen children -> 0 are nodes that can be added
for node in get_entry_nodes(graph)
enqueue!(nodeQueue, node => 0)
end
schedule = Vector{Node}()
sizehint!(schedule, length(graph.nodes))
# keep an accumulated cost of things scheduled to this device so far
deviceAccCost = PriorityQueue{AbstractDevice, Int}()
for device in machine.devices
enqueue!(deviceAccCost, device => 0)
end
node = nothing
while !isempty(nodeQueue)
@assert peek(nodeQueue)[2] == 0
node = dequeue!(nodeQueue)
# assign the device with lowest accumulated cost to the node (if it's a compute node)
if (isa(node, ComputeTaskNode))
lowestDevice = peek(deviceAccCost)[1]
node.device = lowestDevice
deviceAccCost[lowestDevice] = compute_effort(task(node))
end
push!(schedule, node)
for parent in parents(node)
# reduce the priority of all parents by one
if (!haskey(nodeQueue, parent))
enqueue!(nodeQueue, parent => length(children(parent)) - 1)
else
nodeQueue[parent] = nodeQueue[parent] - 1
end
end
end
return schedule
end

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@ -0,0 +1,18 @@
"""
Scheduler
Abstract base type for scheduler implementations. The scheduler is used to assign each node to a device and create a topological ordering of tasks.
"""
abstract type Scheduler end
"""
schedule_dag(::Scheduler, ::DAG, ::Machine)
Interface functions that must be implemented for implementations of [`Scheduler`](@ref).
The function assigns each [`ComputeTaskNode`](@ref) of the [`DAG`](@ref) to one of the devices in the given [`Machine`](@ref) and returns a `Vector{Node}` representing a topological ordering.
[`DataTaskNode`](@ref)s are not scheduled to devices since they do not compute. Instead, a data node transfers data from the [`AbstractDevice`](@ref) of their child to all [`AbstractDevice`](@ref)s of its parents.
"""
function schedule_dag end

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@ -1,11 +1,26 @@
"""
==(t1::AbstractTask, t2::AbstractTask)
Fallback implementation of equality comparison between two abstract tasks. Always returns false. For equal specific types of t1 and t2, a more specific comparison is called instead, doing an actual comparison.
"""
function ==(t1::AbstractTask, t2::AbstractTask)
return false
end
"""
==(t1::AbstractComputeTask, t2::AbstractComputeTask)
Equality comparison between two compute tasks.
"""
function ==(t1::AbstractComputeTask, t2::AbstractComputeTask)
return typeof(t1) == typeof(t2)
end
"""
==(t1::AbstractDataTask, t2::AbstractDataTask)
Equality comparison between two data tasks.
"""
function ==(t1::AbstractDataTask, t2::AbstractDataTask)
return data(t1) == data(t2)
end

89
src/task/compute.jl Normal file
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@ -0,0 +1,89 @@
"""
compute(t::FusedComputeTask, data)
Compute a [`FusedComputeTask`](@ref). This simply asserts false and should not be called. Fused Compute Tasks generate their expressions directly through the other tasks instead.
"""
function compute(t::FusedComputeTask, data)
@assert false "This is not implemented and should never be called"
end
"""
get_expression(t::FusedComputeTask, device::AbstractDevice, inExprs::Vector{String}, outExpr::String)
Generate code evaluating a [`FusedComputeTask`](@ref) on `inExprs`, providing the output on `outExpr`.
`inExprs` should be of the correct types and may be heterogeneous. `outExpr` will be of the type of the output of `T2` of t.
"""
function get_expression(t::FusedComputeTask, device::AbstractDevice, inExprs::Vector, outExpr)
inExprs1 = Vector()
for sym in t.t1_inputs
push!(inExprs1, gen_access_expr(device, sym))
end
outExpr1 = gen_access_expr(device, t.t1_output)
inExprs2 = Vector()
for sym in t.t2_inputs
push!(inExprs2, gen_access_expr(device, sym))
end
expr1 = get_expression(t.first_task, device, inExprs1, outExpr1)
expr2 = get_expression(t.second_task, device, [inExprs2..., outExpr1], outExpr)
full_expr = Expr(:block, expr1, expr2)
return full_expr
end
"""
get_expression(node::ComputeTaskNode)
Generate and return code for a given [`ComputeTaskNode`](@ref).
"""
function get_expression(node::ComputeTaskNode)
@assert length(children(node)) <= children(task(node)) "Node $(node) has too many children for its task: node has $(length(node.children)) versus task has $(children(task(node)))\nNode's children: $(getfield.(node.children, :children))"
@assert !ismissing(node.device) "Trying to get expression for an unscheduled ComputeTaskNode\nNode: $(node)"
inExprs = Vector()
for id in getfield.(children(node), :id)
push!(inExprs, gen_access_expr(node.device, Symbol(to_var_name(id))))
end
outExpr = gen_access_expr(node.device, Symbol(to_var_name(node.id)))
return get_expression(task(node), node.device, inExprs, outExpr)
end
"""
get_expression(node::DataTaskNode)
Generate and return code for a given [`DataTaskNode`](@ref).
"""
function get_expression(node::DataTaskNode)
@assert length(children(node)) == 1 "Trying to call get_expression on a data task node that has $(length(node.children)) children instead of 1"
# TODO: dispatch to device implementations generating the copy commands
child = children(node)[1]
inExpr = eval(gen_access_expr(child.device, Symbol(to_var_name(child.id))))
outExpr = eval(gen_access_expr(child.device, Symbol(to_var_name(node.id))))
dataTransportExp = Meta.parse("$outExpr = $inExpr")
return dataTransportExp
end
"""
get_init_expression(node::DataTaskNode, device::AbstractDevice)
Generate and return code for the initial input reading expression for [`DataTaskNode`](@ref)s with 0 children, i.e., entry nodes.
See also: [`get_entry_nodes`](@ref)
"""
function get_init_expression(node::DataTaskNode, device::AbstractDevice)
@assert isempty(children(node)) "Trying to call get_init_expression on a data task node that is not an entry node."
inExpr = eval(gen_access_expr(device, Symbol("$(to_var_name(node.id))_in")))
outExpr = eval(gen_access_expr(device, Symbol(to_var_name(node.id))))
dataTransportExp = Meta.parse("$outExpr = $inExpr")
return dataTransportExp
end

32
src/task/create.jl Normal file
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@ -0,0 +1,32 @@
"""
copy(t::AbstractDataTask)
Fallback implementation of the copy of an abstract data task, throwing an error.
"""
copy(t::AbstractDataTask) = error("Need to implement copying for your data tasks!")
"""
copy(t::AbstractComputeTask)
Return a copy of the given compute task.
"""
copy(t::AbstractComputeTask) = typeof(t)()
"""
copy(t::FusedComputeTask)
Return a copy of th egiven [`FusedComputeTask`](@ref).
"""
function copy(t::FusedComputeTask)
return FusedComputeTask(copy(t.first_task), copy(t.second_task), copy(t.t1_inputs), t.t1_output, copy(t.t2_inputs))
end
function FusedComputeTask(
T1::Type{<:AbstractComputeTask},
T2::Type{<:AbstractComputeTask},
t1_inputs::Vector{String},
t1_output::String,
t2_inputs::Vector{String},
)
return FusedComputeTask(T1(), T2(), t1_inputs, t1_output, t2_inputs)
end

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@ -1,4 +1,8 @@
"""
show(io::IO, t::FusedComputeTask)
Print a string representation of the fused compute task to io.
"""
function show(io::IO, t::FusedComputeTask)
(T1, T2) = get_types(t)
return print(io, "ComputeFuse(", T1(), ", ", T2(), ")")
return print(io, "ComputeFuse($(t.first_task), $(t.second_task))")
end

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@ -1,33 +1,82 @@
"""
compute(t::AbstractTask; data...)
Fallback implementation of the compute function of a compute task, throwing an error.
"""
function compute(t::AbstractTask; data...)
return error("Need to implement compute()")
end
function compute_effort(t::AbstractTask)
"""
compute(t::FusedComputeTask; data...)
Compute a fused compute task.
"""
function compute(t::FusedComputeTask; data...)
(T1, T2) = collect(typeof(t).parameters)
return compute(T2(), compute(T1(), data))
end
"""
compute(t::AbstractDataTask; data...)
The compute function of a data task, always the identity function, regardless of the specific task.
"""
compute(t::AbstractDataTask; data...) = data
"""
compute_effort(t::AbstractTask)
Fallback implementation of the compute effort of a task, throwing an error.
"""
function compute_effort(t::AbstractTask)::Float64
# default implementation using compute
return error("Need to implement compute_effort()")
end
function data(t::AbstractTask)
"""
data(t::AbstractTask)
Fallback implementation of the data of a task, throwing an error.
"""
function data(t::AbstractTask)::Float64
return error("Need to implement data()")
end
compute_effort(t::AbstractDataTask) = 0
compute(t::AbstractDataTask; data...) = data
data(t::AbstractDataTask) = getfield(t, :data)
"""
compute_effort(t::AbstractDataTask)
data(t::AbstractComputeTask) = 0
Return the compute effort of a data task, always zero, regardless of the specific task.
"""
compute_effort(t::AbstractDataTask)::Float64 = 0.0
function compute_effort(t::FusedComputeTask)
(T1, T2) = collect(typeof(t).parameters)
return compute_effort(T1()) + compute_effort(T2())
"""
data(t::AbstractDataTask)
Return the data of a data task. Given by the task's `.data` field.
"""
data(t::AbstractDataTask)::Float64 = getfield(t, :data)
"""
data(t::AbstractComputeTask)
Return the data of a compute task, always zero, regardless of the specific task.
"""
data(t::AbstractComputeTask)::Float64 = 0.0
"""
compute_effort(t::FusedComputeTask)
Return the compute effort of a fused compute task.
"""
function compute_effort(t::FusedComputeTask)::Float64
return compute_effort(t.first_task) + compute_effort(t.second_task)
end
# actual compute functions for the tasks can stay undefined for now
# compute(t::ComputeTaskU, data::Any) = mycomputation(data)
"""
get_types(::FusedComputeTask{T1, T2})
function compute_intensity(t::AbstractTask)::UInt64
if data(t) == 0
return typemax(UInt64)
end
return compute_effort(t) / data(t)
end
Return a tuple of a the fused compute task's components' types.
"""
get_types(t::FusedComputeTask) = (typeof(t.first_task), typeof(t.second_task))

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@ -1,13 +1,38 @@
"""
AbstractTask
The shared base type for any task.
"""
abstract type AbstractTask end
"""
AbstractComputeTask <: AbstractTask
The shared base type for any compute task.
"""
abstract type AbstractComputeTask <: AbstractTask end
"""
AbstractDataTask <: AbstractTask
The shared base type for any data task.
"""
abstract type AbstractDataTask <: AbstractTask end
struct FusedComputeTask{T1 <: AbstractComputeTask, T2 <: AbstractComputeTask} <:
AbstractComputeTask end
"""
FusedComputeTask{T1 <: AbstractComputeTask, T2 <: AbstractComputeTask} <: AbstractComputeTask
get_types(::FusedComputeTask{T1, T2}) where {T1, T2} = (T1, T2)
A fused compute task made up of the computation of first `T1` and then `T2`.
copy(t::AbstractDataTask) =
error("Need to implement copying for your data tasks!")
copy(t::AbstractComputeTask) = typeof(t)()
Also see: [`get_types`](@ref).
"""
struct FusedComputeTask <: AbstractComputeTask
first_task::AbstractComputeTask
second_task::AbstractComputeTask
# the names of the inputs for T1
t1_inputs::Vector{Symbol}
# output name of T1
t1_output::Symbol
# t2_inputs doesn't include the output of t1, that's implicit
t2_inputs::Vector{Symbol}
end

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@ -1,28 +1,55 @@
"""
NodeIdTrie
# helper struct for NodeTrie
mutable struct NodeIdTrie
value::Vector{Node}
children::Dict{UUID, NodeIdTrie}
Helper struct for [`NodeTrie`](@ref). After the Trie's first level, every Trie level contains the vector of nodes that had children up to that level, and the TrieNode's children by UUID of the node's children.
"""
mutable struct NodeIdTrie{NodeType <: Node}
value::Vector{NodeType}
children::Dict{UUID, NodeIdTrie{NodeType}}
end
# Trie data structure for node reduction, inserts nodes by children
# Assumes that given nodes have ordered vectors of children (see sort_node)
# First level is the task type and thus does not have a value
# Should be constructed with all Types that will be used
"""
NodeTrie
Trie data structure for node reduction, inserts nodes by children.
Assumes that given nodes have ordered vectors of children (see [`sort_node!`](@ref)).
First insertion level is the node's own task type and thus does not have a value (every node has a task type).
See also: [`insert!`](@ref) and [`collect`](@ref)
"""
mutable struct NodeTrie
children::Dict{DataType, NodeIdTrie}
end
"""
NodeTrie()
Constructor for an empty [`NodeTrie`](@ref).
"""
function NodeTrie()
return NodeTrie(Dict{DataType, NodeIdTrie}())
end
function NodeIdTrie()
return NodeIdTrie(Vector{Node}(), Dict{UUID, NodeIdTrie}())
"""
NodeIdTrie()
Constructor for an empty [`NodeIdTrie`](@ref).
"""
function NodeIdTrie{NodeType}() where {NodeType <: Node}
return NodeIdTrie(Vector{NodeType}(), Dict{UUID, NodeIdTrie{NodeType}}())
end
function insert_helper!(trie::NodeIdTrie, node::Node, depth::Int)
if (length(node.children) == depth)
"""
insert_helper!(trie::NodeIdTrie, node::Node, depth::Int)
Insert the given node into the trie. The depth is used to iterate through the trie layers, while the function calls itself recursively until it ran through all children of the node.
"""
function insert_helper!(
trie::NodeIdTrie{NodeType},
node::NodeType,
depth::Int,
) where {TaskType <: AbstractTask, NodeType <: Union{DataTaskNode{TaskType}, ComputeTaskNode{TaskType}}}
if (length(children(node)) == depth)
push!(trie.value, node)
return nothing
end
@ -31,19 +58,31 @@ function insert_helper!(trie::NodeIdTrie, node::Node, depth::Int)
id = node.children[depth].id
if (!haskey(trie.children, id))
trie.children[id] = NodeIdTrie()
trie.children[id] = NodeIdTrie{NodeType}()
end
return insert_helper!(trie.children[id], node, depth)
end
function insert!(trie::NodeTrie, node::Node)
t = typeof(node.task)
if (!haskey(trie.children, t))
trie.children[t] = NodeIdTrie()
"""
insert!(trie::NodeTrie, node::Node)
Insert the given node into the trie. It's sorted by its type in the first layer, then by its children in the following layers.
"""
function insert!(
trie::NodeTrie,
node::NodeType,
) where {TaskType <: AbstractTask, NodeType <: Union{DataTaskNode{TaskType}, ComputeTaskNode{TaskType}}}
if (!haskey(trie.children, NodeType))
trie.children[NodeType] = NodeIdTrie{NodeType}()
end
return insert_helper!(trie.children[typeof(node.task)], node, 0)
return insert_helper!(trie.children[NodeType], node, 0)
end
"""
collect_helper(trie::NodeIdTrie, acc::Set{Vector{Node}})
Collects the Vectors of this [`NodeIdTrie`](@ref) node and all its children and puts them in the `acc` argument.
"""
function collect_helper(trie::NodeIdTrie, acc::Set{Vector{Node}})
if (length(trie.value) >= 2)
push!(acc, trie.value)
@ -55,7 +94,11 @@ function collect_helper(trie::NodeIdTrie, acc::Set{Vector{Node}})
return nothing
end
# returns all sets of multiple nodes that have accumulated in leaves
"""
collect(trie::NodeTrie)
Return all sets of at least 2 [`Node`](@ref)s that have accumulated in leaves of the trie.
"""
function collect(trie::NodeTrie)
acc = Set{Vector{Node}}()
for (t, child) in trie.children

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