rubydragon/metagraphoptimization.jl
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Fix hemera scripts

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This commit is contained in:
Anton Reinhard
2024-02-26 11:14:40 +01:00
parent fc31299d1e
commit 4584285126
4 changed files with 135 additions and 32 deletions
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80
examples/qed_bench_reduction_steps.jl
View File

@ -47,29 +47,29 @@ function bench(func, inputs)
)
end
# preparation of machine
machine = Machine(
[
MetagraphOptimization.NumaNode(
0,
1,
MetagraphOptimization.default_strategy(MetagraphOptimization.NumaNode),
-1.0,
UUIDs.uuid1(),
),
],
[-1.0;;],
)
# bench and produce data
n_inputs = 10_000
n_inputs = 50_000
optimizer = ReductionOptimizer()
processes = [("ke->kke", 50), ("ke->ke", 1), ("ke->kke", 1), ("ke->kkke", 5), ("ke->kkkke", 5), ("ke->kkkkke", 10), ("ke->kkkkkke", 20)]
processes = [("ke->kke", 5), ("ke->ke", 1), ("ke->kke", 1), ("ke->kkke", 1), ("ke->kkkke", 1), ("ke->kkkkke", 1)]
for (process_str, STEPSIZE) in processes
n = 0
opt_time_cum = 0
# preparation of graph
machine = Machine(
[
MetagraphOptimization.NumaNode(
0,
1,
MetagraphOptimization.default_strategy(MetagraphOptimization.NumaNode),
-1.0,
UUIDs.uuid1(),
),
],
[-1.0;;],
)
process = parse_process(process_str, QEDModel())
graph = gen_graph(process)
inputs = [gen_process_input(process) for _ in 1:n_inputs]
@ -113,3 +113,51 @@ for (process_str, STEPSIZE) in processes
end
CSV.write(results_filename, df)
for (process_str, STEPSIZE) in [("AB->AB", 1), ("AB->ABBB", 1), ("AB->ABBBBB", 1)]
n = 0
opt_time_cum = 0
process = parse_process(process_str, ABCModel())
graph = parse_dag("input/$process_str.txt", ABCModel())
inputs = [gen_process_input(process) for _ in 1:n_inputs]
get_compute_function(graph, process, machine)
while true
func_gen_time = @elapsed func = get_compute_function(graph, process, machine)
res = bench(func, inputs)
graph_properties = get_properties(graph)
push!(
df,
(
Threads.nthreads(),
process_str,
n,
opt_time_cum,
graph_properties.noNodes,
graph_properties.noEdges,
graph_properties.computeEffort,
graph_properties.data,
graph_properties.computeIntensity,
func_gen_time,
res.cpu_compile_time,
res.cpu_single_thread_time,
res.cpu_multi_thread_time,
res.gpu_compile_time,
res.gpu_time,
),
)
CSV.write(results_filename, df)
if fixpoint_reached(optimizer, graph)
break
end
opt_time_cum += @elapsed optimize!(optimizer, graph, STEPSIZE)
n += STEPSIZE
end
end
CSV.write(results_filename, df)

85
examples/qed_bench_reduction_steps_gpu.jl
View File

@ -50,30 +50,35 @@ function bench(func, inputs)
)
end
log("CUDA devices: $(CUDA.devices())")
for dev in CUDA.devices()
log("CUDA device: $(dev)")
end
# preparation of machine
machine = Machine(
[
MetagraphOptimization.NumaNode(
0,
1,
MetagraphOptimization.default_strategy(MetagraphOptimization.NumaNode),
-1.0,
UUIDs.uuid1(),
),
],
[-1.0;;],
)
# bench and produce data
n_inputs = 10_000
n_inputs = 50_000
optimizer = ReductionOptimizer()
processes = [("ke->kke", 50), ("ke->ke", 1), ("ke->kke", 1), ("ke->kkke", 1), ("ke->kkkke", 5)]
processes = [("ke->kke", 50), ("ke->ke", 1), ("ke->kke", 1), ("ke->kkke", 1)]
for (process_str, STEPSIZE) in processes
n = 0
opt_time_cum = 0
# preparation of graph
machine = Machine(
[
MetagraphOptimization.NumaNode(
0,
1,
MetagraphOptimization.default_strategy(MetagraphOptimization.NumaNode),
-1.0,
UUIDs.uuid1(),
),
],
[-1.0;;],
)
process = parse_process(process_str, QEDModel())
graph = gen_graph(process)
inputs = CuVector([gen_process_input(process) for _ in 1:n_inputs])
@ -117,3 +122,51 @@ for (process_str, STEPSIZE) in processes
end
CSV.write(results_filename, df)
for (process_str, STEPSIZE) in [("AB->AB", 1), ("AB->ABBB", 1), ("AB->ABBBBB", 1)]
n = 0
opt_time_cum = 0
process = parse_process(process_str, ABCModel())
graph = parse_dag("input/$process_str.txt", ABCModel())
inputs = CuVector([gen_process_input(process) for _ in 1:n_inputs])
get_compute_function(graph, process, machine)
while true
func_gen_time = @elapsed func = get_compute_function(graph, process, machine)
res = bench(func, inputs)
graph_properties = get_properties(graph)
push!(
df,
(
Threads.nthreads(),
process_str,
n,
opt_time_cum,
graph_properties.noNodes,
graph_properties.noEdges,
graph_properties.computeEffort,
graph_properties.data,
graph_properties.computeIntensity,
func_gen_time,
res.cpu_compile_time,
res.cpu_single_thread_time,
res.cpu_multi_thread_time,
res.gpu_compile_time,
res.gpu_time,
),
)
CSV.write(results_filename, df)
if fixpoint_reached(optimizer, graph)
break
end
opt_time_cum += @elapsed optimize!(optimizer, graph, STEPSIZE)
n += STEPSIZE
end
end
CSV.write(results_filename, df)