Add GPU strong scaling measurement files
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parent
1696d72a6f
commit
075d11b948
@ -1,6 +1,9 @@
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generate-batch-strong-cpu:
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python scripts/substitute.py strong-batch.j2 < strong-cpu.json
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generate-batch-strong-gpu:
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python scripts/substitute.py strong-batch.j2 < strong-gpu.json
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clean-logs:
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rm logs/*
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12312
experiments/eval/data/strong-gpu-timings.json
Normal file
12312
experiments/eval/data/strong-gpu-timings.json
Normal file
File diff suppressed because it is too large
Load Diff
8
experiments/eval/data/strong-gpu.tsv
Normal file
8
experiments/eval/data/strong-gpu.tsv
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@ -0,0 +1,8 @@
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label nodes tasks mean_time std_time speedup speedup_std
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1 1 1 157.5217688 0.5311809049610176 1.0 0.003372111099358209
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1 1 2 120.15237200000001 0.299200156243067 1.3110167213344732 0.004420894037456185
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1 1 4 75.69762635 0.5769735213369493 2.0809340582447473 0.007017140834839633
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2 2 8 52.110670325 0.940887445857671 3.022831366735062 0.010193323203255446
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4 4 16 28.215493675000005 0.5598524922936691 5.582811012077745 0.018825858979446598
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8 8 32 12.422501156249998 0.31940690526235005 12.680358554102268 0.04275957782413007
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16 16 64 7.70770194375 0.04068644910316383 20.43693048194875 0.06891560011499148
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@ -27,6 +27,7 @@ if __name__ == "__main__":
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scaling_spec = {
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"label": lambda job: job["accounting"][0]["nodes"]["count"],
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"nodes": lambda job: job["accounting"][0]["nodes"]["count"],
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"tasks": lambda job: job["accounting"][0]["tasks"]["count"],
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"mean_time": lambda job: job["means"]["TimeStep"],
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"std_time": lambda job: job["stds"]["TimeStep"],
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"speedup": lambda job: jobs[0]["means"]["TimeStep"] / job["means"]["TimeStep"],
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34
experiments/eval/scripts/generate-strong-gpu-configs.py
Executable file
34
experiments/eval/scripts/generate-strong-gpu-configs.py
Executable file
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#!/usr/bin/env python
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import copy
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import json
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from pathlib import Path
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SIZE = [384, 384, 384]
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with (Path(__file__).parent.parent / "templates" / "strong-gpu.json").open(encoding="utf8") as f:
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template = json.load(f)
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configs = [
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[ 1, 1, 1],
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[ 1, 1, 2],
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[ 1, 2, 2],
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[ 2, 2, 2],
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[ 2, 2, 4],
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[ 2, 4, 4],
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[ 4, 4, 4],
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[ 4, 4, 8],
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]
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out_path = Path(__file__).parent.parent / "generated" / "config"
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for c in configs:
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nc = copy.deepcopy(template)
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nc["Geometry"]["blockcount"] = c
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nc["Geometry"]["blocksize"] = [bs // bc for bc, bs in zip(c, SIZE)]
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nc_out_path = out_path / f"strong-gpu-{c[0]:02}-{c[1]:02}-{c[2]:02}.json"
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print(f"Dumping {(c[0] * c[1] * c[2])} to {nc_out_path}")
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with nc_out_path.open("w", encoding="utf8") as f:
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json.dump(nc, f)
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63
experiments/eval/strong-gpu.json
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63
experiments/eval/strong-gpu.json
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{
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"common": {
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"account": "cellsinsilico",
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"partition": "gpus",
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"extra_sbatch_line": "#SBATCH --gres=gpu:4",
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"logfile_path": "/p/project/cellsinsilico/paulslustigebude/ma/experiments/eval/logs/%x-%A.%a",
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"nastja_binary_path": "/p/project/cellsinsilico/paulslustigebude/nastja/build-cuda/nastja",
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"config_path": "/p/project/cellsinsilico/paulslustigebude/ma/experiments/eval/generated/config/${SLURM_JOB_NAME}.json",
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"output_dir_path": "/p/scratch/cellsinsilico/paul/nastja-out/${SLURM_JOB_NAME}-${SLURM_ARRAY_JOB_ID}.${SLURM_ARRAY_TASK_ID}"
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},
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"batches": [
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{
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"name": "strong-gpu-01-01-01",
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"nodes": 1,
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"tasks": 1,
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"time": "00:15:00",
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"extra_sbatch_line": "#SBATCH --gres=gpu:1"
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},
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{
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"name": "strong-gpu-01-01-02",
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"nodes": 1,
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"tasks": 2,
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"time": "00:15:00",
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"extra_sbatch_line": "#SBATCH --gres=gpu:2"
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},
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{
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"name": "strong-gpu-01-02-02",
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"nodes": 1,
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"tasks": 4,
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"time": "00:15:00"
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},
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{
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"name": "strong-gpu-02-02-02",
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"nodes": 2,
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"tasks": 8,
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"time": "00:15:00"
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},
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{
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"name": "strong-gpu-02-02-04",
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"nodes": 4,
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"tasks": 16,
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"time": "00:15:00"
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},
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{
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"name": "strong-gpu-02-04-04",
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"nodes": 8,
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"tasks": 32,
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"time": "00:15:00"
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},
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{
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"name": "strong-gpu-04-04-04",
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"nodes": 16,
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"tasks": 64,
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"time": "00:15:00"
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},
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{
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"name": "strong-gpu-04-04-08",
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"nodes": 32,
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"tasks": 128,
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"time": "00:15:00"
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}
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]
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}
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263
experiments/eval/templates/strong-gpu.json
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263
experiments/eval/templates/strong-gpu.json
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{
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"Application": "Cells",
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"CellsInSilico": {
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"2D": false,
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"adhesion": {
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"matrix": [
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 450.0],
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 450.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 50.0]
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],
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"polarityenabled": false
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},
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"centerofmass": {
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"steps": 1
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},
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"cleaner": {
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"killdistance": 0,
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"steps": 100
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},
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"contactinhibition": {
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"enabled": false
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},
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"division": {
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"condition": [
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"",
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"",
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"",
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"",
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"",
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"",
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"",
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"",
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"",
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"( volume >= 0.9 * volume0 ) & ( rnd() <= 0.00001 ) & generation < 1"
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],
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"enabled": true,
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"halveSignals": false
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},
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"dynamicecm": {
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"alpha": 2.0,
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"beta": 0.5,
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"c": 4.0,
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"deltat": 0.10000000149011612,
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"ecmCellID": 0,
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"enabled": true,
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"eta": 0.25,
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"k0": 0.10000000149011612,
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"k1": 0.10000000149011612,
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"lambda": 10.0,
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"phi": 1.0,
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"pushSteps": 10,
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"pushWeight": 0.5,
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"stepsPerMcs": 100
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},
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"ecmdegradation": {
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"enabled": false
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},
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"energyfunctions": [
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"Volume00",
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"Surface01",
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"Motility00",
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"Adhesion01",
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"DynamicECM00"
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],
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"liquid": 6,
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"logcellproperties": {
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"enabled": false
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},
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"orientation": {
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"enabled": true,
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"motility": "persistentRandomWalk",
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"motilityamount": [
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0
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],
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"numRandomNumbers": 5,
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"persistenceMagnitude": 0.0,
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"persistentDecay": 0.8,
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"recalculationtime": 200
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},
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"polarity": {
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"enabled": false
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},
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"signaling": {
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"constant": false,
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"enabled": false
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},
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"surface": {
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"default": {
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"storage": "const",
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"value": 400.0
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},
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"lambda": [
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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5.625,
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5.625,
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1.0
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],
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"sizechange": [
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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-0.05,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0
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]
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},
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"temperature": 50.0,
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"visitor": {
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"checkerboard": "01",
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"stepwidth": 10
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},
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"volume": {
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"default": {
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"storage": "const",
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"value": 500.0
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},
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"lambda": [
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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7.5,
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7.5,
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7.5
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],
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"sizechange": [
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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-0.05,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0
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]
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}
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},
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"DefineFunctions": [
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"r_angle()=360*rnd()",
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"r_size()=400*rnd()"
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],
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"Filling": {
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"cells": [
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{
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"box": [
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[
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0,
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0,
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0
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],
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[
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384,
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384,
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384
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]
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],
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"celltype": 0,
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"component": 0,
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"pattern": "const",
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"seed": 0,
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"shape": "cube",
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"value": 0
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},
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{
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"box": [
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[117, 117, 177],
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[267, 267, 267]
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],
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"celltype": 9,
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"center": [192, 192, 192],
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"component": 0,
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"count": 5500,
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"pattern": "voronoi",
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"radius": 75,
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"seed": 758960,
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"shape": "sphere",
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"value": 8
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}
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],
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"initialoutput": false,
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"randomseed": 758959
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},
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"Geometry": {
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"blockcount": [
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4,
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4,
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3
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],
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"blockdefault": "fill",
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"blocksize": [
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96,
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96,
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128
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],
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"blocktype": [
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[
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[
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1
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]
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]
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]
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},
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"Settings": {
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"deltat": 1.0,
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"deltax": 1.0,
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"handleFPE": "signal",
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"logger": {
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"group": 0,
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"steps": 100
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},
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"randomseed": 42,
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"statusoutput": 1,
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"timestepguard": 1,
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"timesteps": 5,
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"cuda": {
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"subblocks": {
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"blockDim": [8, 8, 8]
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}
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}
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}
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}
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