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79227e93d9
...
075d11b948
@ -4,9 +4,6 @@ generate-batch-strong-cpu:
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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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generate-batch-strong-booster:
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python scripts/substitute.py strong-batch.j2 < strong-booster.json
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clean-logs:
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rm logs/*
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File diff suppressed because one or more lines are too long
@ -1,9 +0,0 @@
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label nodes tasks mean_time std_time speedup speedup_std speedup_error
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1 1 1 161.08257980000002 1.0725827874500897 1.0 0.0066585895804611986 0.004614167168572558
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1 1 2 109.2771463 1.3498701967829918 1.474073813730255 0.018208823861030076 0.012618071172974463
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1 1 4 94.5763083 2.921107861707173 1.7032022363258181 0.052605536545441436 0.03645377698685362
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2 2 8 75.06436665 5.345617315509918 2.145926049720424 0.15281950626022228 0.10589851499069955
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4 4 16 42.842002025 1.5314070839490197 3.759921856733072 0.13440013758310768 0.09313454370387199
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8 8 32 16.40522446875 4.395440141154071 9.818980539208297 2.6307924825691393 1.823046180232846
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16 16 64 9.361684956249999 5.010216046116417 17.206579857449583 9.208671612370724 6.381283859970849
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32 32 128 5.214438346875 0.113661681579046 30.891645290338204 0.6733604113945479 0.4666149588180552
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File diff suppressed because one or more lines are too long
@ -1,10 +1,10 @@
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label nodes tasks mean_time std_time speedup speedup_std speedup_error
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1 1 48 1953.3832839708334 14.087268287606769 1.0 0.007211727674340593 0.004711662080569187
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2 2 96 1046.3540047312501 6.0880471399828595 1.866847429396085 0.010861959816590314 0.007096480413505672
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4 4 192 566.0709002166666 5.005298021298787 3.4507749527897755 0.030512356378918384 0.019934739500893344
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8 8 384 318.1225953708333 9.706195475255436 6.1403475024896865 0.18734731205020752 0.12240024387280224
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16 16 768 178.56471887994792 9.393026972005948 10.939357428631386 0.5754422263707719 0.375955587895571
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32 32 1536 99.82801128216144 1.4413062581268035 19.56748670921274 0.28251330150299214 0.18457535698195485
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64 64 3072 55.75610567220052 1.0303316198245824 35.034428255357376 0.6474103379133267 0.42297475410337343
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128 128 6144 31.589132888118492 0.32226687791817415 61.83719226764697 0.6308523555205254 0.41215687227340997
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256 256 12288 19.11704500200738 0.18528928846832715 102.18018965617955 0.9903672160087601 0.6470399144590566
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label nodes mean_time std_time speedup speedup_std
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1 1 1132.1093720291667 18.428644622186628 1.0 0.016278148629010596
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2 2 619.9614289104168 7.890810670766857 1.8260964621925766 0.029725469622481192
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4 4 348.657283046875 3.3739994706945122 3.247054993762918 0.052856043795043856
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8 8 200.79244575312498 1.259910347938215 5.63820699420684 0.09177957145282603
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16 16 111.37033198671875 0.3943541234366863 10.16526889911915 0.16547175799372044
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32 32 65.69918741145833 0.43464214827532205 17.231710415823503 0.28050034328084494
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64 64 38.56257490957031 0.12227397957602136 29.35772247273364 0.4778893698204027
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128 128 21.433497051595047 0.14016683538819355 52.81962944749218 0.8598057786755426
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256 256 13.247708324598525 0.030540850807946187 85.45699711149668 1.3910817003698723
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File diff suppressed because one or more lines are too long
@ -1,9 +1,8 @@
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label nodes tasks mean_time std_time speedup speedup_std speedup_error
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1 1 1 316.56674059999995 1.3298279828832311 1.0 0.004200782370133899 0.002910993666206366
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1 1 2 239.7297573 1.3705192585818793 1.3205150005797797 0.007549297425250635 0.00523139621453709
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1 1 4 150.86725470000002 1.463195211854327 2.098313124537819 0.020350616990413285 0.014102258089746373
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2 2 8 103.758313 1.3959932139830615 3.051001230137579 0.041049019495199714 0.028445519245192213
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4 4 16 56.423368849999996 0.8293394846143929 5.610560784514376 0.08246688711191473 0.057146637198176795
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8 8 32 24.168551143749998 0.5503559531334744 13.09829202078025 0.29826872726631265 0.20668968287257275
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16 16 64 15.05716248125 0.04648043269670786 21.024329185144023 0.06490066896072916 0.044973869063161365
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32 32 128 9.9111531140625 0.08848604506395903 31.940455056721632 0.28516203039001964 0.19760720531719309
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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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@ -3,7 +3,6 @@
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import argparse
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import json
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import math
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def print_table(data, spec):
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@ -20,13 +19,11 @@ def print_table(data, spec):
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if __name__ == "__main__":
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p = argparse.ArgumentParser(description="Turn files generated by timing.py into pgf datafiles")
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p.add_argument("timing_file")
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p.add_argument("--weak", action="store_true")
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args = p.parse_args()
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with open(args.timing_file, "r", encoding="utf8") as f:
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jobs = json.load(f)
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if not args.weak:
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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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@ -34,19 +31,6 @@ if __name__ == "__main__":
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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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# Standard deviation scaled to speedup
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"speedup_std": lambda job: (jobs[0]["means"]["TimeStep"] / job["means"]["TimeStep"]) * (job["stds"]["TimeStep"] / job["means"]["TimeStep"]),
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# 95% confidence interval
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"speedup_error": lambda job: (jobs[0]["means"]["TimeStep"] / job["means"]["TimeStep"]) * (job["stds"]["TimeStep"] / job["means"]["TimeStep"]) / math.sqrt(len(jobs)) * 1.96,
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"speedup_std": lambda job: jobs[0]["stds"]["TimeStep"] / job["means"]["TimeStep"],
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}
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else:
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scaling_spec = {
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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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"efficiency": lambda job: jobs[0]["means"]["TimeStep"] / job["means"]["TimeStep"],
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"efficiency_error": lambda job: (jobs[0]["means"]["TimeStep"] / job["means"]["TimeStep"]) * (job["stds"]["TimeStep"] / job["means"]["TimeStep"]) / math.sqrt(len(jobs)) * 1.96,
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}
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print_table(jobs, scaling_spec)
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@ -1,34 +0,0 @@
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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-booster.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-booster-{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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@ -1,152 +0,0 @@
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#!/usr/bin/env python
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import jinja2
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import json
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import sys
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Tuple
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SIZE = (192, 192, 192)
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templates_env = jinja2.Environment(
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loader=jinja2.FileSystemLoader(Path(__file__).parent.parent / "templates"),
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autoescape=jinja2.select_autoescape()
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)
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@dataclass
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class Experiment:
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job_name: str
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account: str
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partition: str
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nastja_binary_path: str
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nodes: int
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tasks: int
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num_blocks: Tuple[int, int, int]
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domain_scale: Tuple[int, int, int]
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time: str = "00:15:00"
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extra_sbatch_line: str = ""
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logfile_path: str = "/p/project/cellsinsilico/paulslustigebude/ma/experiments/eval/logs/%x-%A.%a"
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config_path: str = "/p/project/cellsinsilico/paulslustigebude/ma/experiments/eval/generated/config/${SLURM_JOB_NAME}.json"
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output_dir_path: str = "/p/scratch/cellsinsilico/paul/nastja-out/${SLURM_JOB_NAME}-${SLURM_ARRAY_JOB_ID}.${SLURM_ARRAY_TASK_ID}"
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def get_config(self):
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with (Path(__file__).parent.parent / "templates" / "weak.json").open(encoding="utf8") as f:
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config = json.load(f)
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size = (
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SIZE[0] * self.domain_scale[0],
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SIZE[1] * self.domain_scale[1],
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SIZE[2] * self.domain_scale[2],
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)
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blocksize = (
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size[0] // self.num_blocks[0],
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size[1] // self.num_blocks[1],
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size[2] // self.num_blocks[2],
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)
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config["Geometry"] = {
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"blockcount": list(self.num_blocks),
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"blocksize": list(blocksize),
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}
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cells_filling = [{
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"box": [
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[0, 0, 0],
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list(size)
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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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for z in range(self.domain_scale[2]):
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for y in range(self.domain_scale[1]):
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for x in range(self.domain_scale[0]):
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cx = x * SIZE[0] + SIZE[0] // 2
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cy = y * SIZE[1] + SIZE[1] // 2
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cz = z * SIZE[2] + SIZE[2] // 2
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cells_filling.append({
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"shape": "sphere",
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"pattern": "voronoi",
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"count": 715,
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"radius": 38,
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"center": [cx, cy, cz],
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"box": [
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[cx - 38, cy - 38, cz - 38],
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[cx + 38, cy + 38, cz + 38]
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],
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"celltype": 9,
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"seed": 758960,
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})
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config["Filling"]["cells"] = cells_filling
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return config
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def write_batch_file(self, out_path: Path):
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t = templates_env.get_template("strong-batch.j2")
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t.stream(
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name=self.job_name,
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account=self.account,
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partition=self.partition,
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nodes=self.nodes,
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tasks=self.tasks,
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extra_sbatch_line=self.extra_sbatch_line,
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time=self.time,
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logfile_path=self.logfile_path,
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nastja_binary_path=self.nastja_binary_path,
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config_path=self.config_path,
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output_dir_path=self.output_dir_path,
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).dump(str(out_path))
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def make_cpu_ex(x: int, y: int, z: int) -> Experiment:
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num_blocks = x * y * z
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assert num_blocks % 48 == 0
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num_nodes = num_blocks // 48
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assert x % 4 == 0
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assert y % 4 == 0
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assert z % 3 == 0
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return Experiment(
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job_name=f"weak-cpu-{x:02}-{y:02}-{z:02}",
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account="cellsinsilico",
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partition="batch",
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nastja_binary_path="/p/project/cellsinsilico/paulslustigebude/nastja/build-nocuda/nastja",
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nodes=num_nodes,
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tasks=num_blocks,
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num_blocks=(x, y, z),
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domain_scale=(x // 4, y // 4, z // 3),
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)
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experiments = [
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make_cpu_ex(4, 4, 3),
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make_cpu_ex(4, 4, 6),
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make_cpu_ex(4, 4, 12),
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make_cpu_ex(4, 8, 12),
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make_cpu_ex(8, 8, 12),
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make_cpu_ex(8, 8, 24),
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make_cpu_ex(8, 16, 24),
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make_cpu_ex(16, 16, 24),
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]
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if __name__ == "__main__":
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outdir = Path(__file__).parent.parent / "generated"
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for e in experiments:
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print(f"Generating config for {e.job_name}", file=sys.stderr)
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config_path = (outdir / "config" / e.job_name).with_suffix(".json")
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with config_path.open("w", encoding="utf8") as f:
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json.dump(e.get_config(), f, indent=2)
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print(f"Generating batch file for {e.job_name}", file=sys.stderr)
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e.write_batch_file(outdir / "batch" / e.job_name)
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@ -1,20 +0,0 @@
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#!/usr/bin/env python
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import argparse
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import pandas
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def show_seconds(s: float) -> str:
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return f"{s:.2f}s"
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if __name__ == '__main__':
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p = argparse.ArgumentParser(description="Make a latex table from a timings tsv")
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p.add_argument("timingfile")
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args = p.parse_args()
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df = pandas.read_csv(args.timingfile, sep="\t")
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for i in range(len(df)):
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print(f"{df['nodes'][i]} & {df['tasks'][i]} & {show_seconds(df['mean_time'][i])} & {show_seconds(df['std_time'][i])} & {df['speedup'][i]:.02f} & {df['speedup_error'][i]:.02f} \\\\")
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@ -44,13 +44,12 @@ def get_outdirs(jobid: str):
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return sorted(Path("/p/scratch/cellsinsilico/paul/nastja-out").glob(f"*{jobid}*"))
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def load_array_mean_timings(jobid: str, excluded_array_indices):
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mts = []
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for outdir_path in get_outdirs(jobid):
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if any(str(outdir_path).endswith(str(i)) for i in excluded_array_indices):
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print(f"Not loading timings for {outdir_path} because it was excluded.", file=sys.stderr)
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continue
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mts.append(load_mean_timings(outdir_path))
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def load_array_mean_timings(jobid: str):
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mts = [
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load_mean_timings(outdir_path)
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for outdir_path
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in get_outdirs(jobid)
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]
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return pandas.DataFrame(list(mts), columns=mts[0].index)
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@ -76,37 +75,18 @@ if __name__ == "__main__":
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p = argparse.ArgumentParser(description="Load and analzye data from nastja timing files")
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p.add_argument("jobid", nargs="+")
|
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p.add_argument("--prettify", action="store_true")
|
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p.add_argument("--dump-timings", action="store_true")
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args = p.parse_args()
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|
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results = []
|
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for i, jobid in enumerate(args.jobid, 1):
|
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print(f"({i:2}/{len(args.jobid):2}) Loading accounting data for {jobid}", file=sys.stderr)
|
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accounting_data = get_accounting_data(jobid)
|
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|
||||
jobs = []
|
||||
excluded_array_indices = []
|
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for array_job in accounting_data["jobs"]:
|
||||
# Get metadata related to array
|
||||
array_main_job = array_job["array"]["job_id"]
|
||||
array_index = array_job["array"]["task_id"]
|
||||
# The last step is the actual job we want the data for
|
||||
# The steps before set up cluster etc.
|
||||
last_step = array_job["steps"][-1]
|
||||
if last_step["state"] != "COMPLETED":
|
||||
print(f"WARNING: {array_main_job}.{array_index} has state {last_step['state']}, excluding it from measurements", file=sys.stderr)
|
||||
excluded_array_indices.append(array_index)
|
||||
continue
|
||||
jobs.append(last_step)
|
||||
|
||||
array_mean_timings = load_array_mean_timings(jobid, excluded_array_indices)
|
||||
if args.dump_timings:
|
||||
print(array_mean_timings, file=sys.stderr)
|
||||
array_mean_timings = load_array_mean_timings(jobid)
|
||||
|
||||
results.append({
|
||||
"jobid": jobid,
|
||||
"means": array_mean_timings.mean().to_dict(),
|
||||
"stds": array_mean_timings.std().to_dict(),
|
||||
"accounting": jobs
|
||||
"accounting": [array_job["steps"][-1] for array_job in accounting_data["jobs"]]
|
||||
})
|
||||
print(json.dumps(results, indent=2 if args.prettify else None))
|
||||
|
||||
@ -1,63 +0,0 @@
|
||||
{
|
||||
"common": {
|
||||
"account": "hkf6",
|
||||
"partition": "booster",
|
||||
"extra_sbatch_line": "#SBATCH --gres=gpu:4",
|
||||
"logfile_path": "/p/project/cellsinsilico/paulslustigebude/ma/experiments/eval/logs/%x-%A.%a",
|
||||
"nastja_binary_path": "/p/project/cellsinsilico/paulslustigebude/nastja/build-cuda/nastja",
|
||||
"config_path": "/p/project/cellsinsilico/paulslustigebude/ma/experiments/eval/generated/config/${SLURM_JOB_NAME}.json",
|
||||
"output_dir_path": "/p/scratch/cellsinsilico/paul/nastja-out/${SLURM_JOB_NAME}-${SLURM_ARRAY_JOB_ID}.${SLURM_ARRAY_TASK_ID}"
|
||||
},
|
||||
"batches": [
|
||||
{
|
||||
"name": "strong-booster-01-01-01",
|
||||
"nodes": 1,
|
||||
"tasks": 1,
|
||||
"time": "00:15:00",
|
||||
"extra_sbatch_line": "#SBATCH --gres=gpu:1"
|
||||
},
|
||||
{
|
||||
"name": "strong-booster-01-01-02",
|
||||
"nodes": 1,
|
||||
"tasks": 2,
|
||||
"time": "00:15:00",
|
||||
"extra_sbatch_line": "#SBATCH --gres=gpu:2"
|
||||
},
|
||||
{
|
||||
"name": "strong-booster-01-02-02",
|
||||
"nodes": 1,
|
||||
"tasks": 4,
|
||||
"time": "00:15:00"
|
||||
},
|
||||
{
|
||||
"name": "strong-booster-02-02-02",
|
||||
"nodes": 2,
|
||||
"tasks": 8,
|
||||
"time": "00:15:00"
|
||||
},
|
||||
{
|
||||
"name": "strong-booster-02-02-04",
|
||||
"nodes": 4,
|
||||
"tasks": 16,
|
||||
"time": "00:15:00"
|
||||
},
|
||||
{
|
||||
"name": "strong-booster-02-04-04",
|
||||
"nodes": 8,
|
||||
"tasks": 32,
|
||||
"time": "00:15:00"
|
||||
},
|
||||
{
|
||||
"name": "strong-booster-04-04-04",
|
||||
"nodes": 16,
|
||||
"tasks": 64,
|
||||
"time": "00:15:00"
|
||||
},
|
||||
{
|
||||
"name": "strong-booster-04-04-08",
|
||||
"nodes": 32,
|
||||
"tasks": 128,
|
||||
"time": "00:15:00"
|
||||
}
|
||||
]
|
||||
}
|
||||
@ -13,19 +13,19 @@
|
||||
"name": "strong-cpu-04-04-03",
|
||||
"nodes": 1,
|
||||
"tasks": 48,
|
||||
"time": "01:00:00"
|
||||
"time": "00:30:00"
|
||||
},
|
||||
{
|
||||
"name": "strong-cpu-04-04-06",
|
||||
"nodes": 2,
|
||||
"tasks": 96,
|
||||
"time": "01:00:00"
|
||||
"time": "00:30:00"
|
||||
},
|
||||
{
|
||||
"name": "strong-cpu-04-04-12",
|
||||
"nodes": 4,
|
||||
"tasks": 192,
|
||||
"time": "00:20:00"
|
||||
"time": "00:10:00"
|
||||
},
|
||||
{
|
||||
"name": "strong-cpu-04-08-12",
|
||||
|
||||
@ -1,263 +0,0 @@
|
||||
{
|
||||
"Application": "Cells",
|
||||
"CellsInSilico": {
|
||||
"2D": false,
|
||||
"adhesion": {
|
||||
"matrix": [
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 450.0],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 450.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 50.0]
|
||||
],
|
||||
"polarityenabled": false
|
||||
},
|
||||
"centerofmass": {
|
||||
"steps": 1
|
||||
},
|
||||
"cleaner": {
|
||||
"killdistance": 0,
|
||||
"steps": 100
|
||||
},
|
||||
"contactinhibition": {
|
||||
"enabled": false
|
||||
},
|
||||
"division": {
|
||||
"condition": [
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"( volume >= 0.9 * volume0 ) & ( rnd() <= 0.00001 ) & generation < 1"
|
||||
],
|
||||
"enabled": true,
|
||||
"halveSignals": false
|
||||
},
|
||||
"dynamicecm": {
|
||||
"alpha": 2.0,
|
||||
"beta": 0.5,
|
||||
"c": 4.0,
|
||||
"deltat": 0.10000000149011612,
|
||||
"ecmCellID": 0,
|
||||
"enabled": true,
|
||||
"eta": 0.25,
|
||||
"k0": 0.10000000149011612,
|
||||
"k1": 0.10000000149011612,
|
||||
"lambda": 10.0,
|
||||
"phi": 1.0,
|
||||
"pushSteps": 10,
|
||||
"pushWeight": 0.5,
|
||||
"stepsPerMcs": 100
|
||||
},
|
||||
"ecmdegradation": {
|
||||
"enabled": false
|
||||
},
|
||||
"energyfunctions": [
|
||||
"Volume00",
|
||||
"Surface01",
|
||||
"Motility00",
|
||||
"Adhesion01",
|
||||
"DynamicECM00"
|
||||
],
|
||||
"liquid": 6,
|
||||
"logcellproperties": {
|
||||
"enabled": false
|
||||
},
|
||||
"orientation": {
|
||||
"enabled": true,
|
||||
"motility": "persistentRandomWalk",
|
||||
"motilityamount": [
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"numRandomNumbers": 5,
|
||||
"persistenceMagnitude": 0.0,
|
||||
"persistentDecay": 0.8,
|
||||
"recalculationtime": 200
|
||||
},
|
||||
"polarity": {
|
||||
"enabled": false
|
||||
},
|
||||
"signaling": {
|
||||
"constant": false,
|
||||
"enabled": false
|
||||
},
|
||||
"surface": {
|
||||
"default": {
|
||||
"storage": "const",
|
||||
"value": 400.0
|
||||
},
|
||||
"lambda": [
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
5.625,
|
||||
5.625,
|
||||
1.0
|
||||
],
|
||||
"sizechange": [
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
-0.05,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0
|
||||
]
|
||||
},
|
||||
"temperature": 50.0,
|
||||
"visitor": {
|
||||
"checkerboard": "01",
|
||||
"stepwidth": 10
|
||||
},
|
||||
"volume": {
|
||||
"default": {
|
||||
"storage": "const",
|
||||
"value": 500.0
|
||||
},
|
||||
"lambda": [
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
7.5,
|
||||
7.5,
|
||||
7.5
|
||||
],
|
||||
"sizechange": [
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
-0.05,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0
|
||||
]
|
||||
}
|
||||
},
|
||||
"DefineFunctions": [
|
||||
"r_angle()=360*rnd()",
|
||||
"r_size()=400*rnd()"
|
||||
],
|
||||
"Filling": {
|
||||
"cells": [
|
||||
{
|
||||
"box": [
|
||||
[
|
||||
0,
|
||||
0,
|
||||
0
|
||||
],
|
||||
[
|
||||
384,
|
||||
384,
|
||||
384
|
||||
]
|
||||
],
|
||||
"celltype": 0,
|
||||
"component": 0,
|
||||
"pattern": "const",
|
||||
"seed": 0,
|
||||
"shape": "cube",
|
||||
"value": 0
|
||||
},
|
||||
{
|
||||
"box": [
|
||||
[117, 117, 177],
|
||||
[267, 267, 267]
|
||||
],
|
||||
"celltype": 9,
|
||||
"center": [192, 192, 192],
|
||||
"component": 0,
|
||||
"count": 5500,
|
||||
"pattern": "voronoi",
|
||||
"radius": 75,
|
||||
"seed": 758960,
|
||||
"shape": "sphere",
|
||||
"value": 8
|
||||
}
|
||||
],
|
||||
"initialoutput": false,
|
||||
"randomseed": 758959
|
||||
},
|
||||
"Geometry": {
|
||||
"blockcount": [
|
||||
4,
|
||||
4,
|
||||
3
|
||||
],
|
||||
"blockdefault": "fill",
|
||||
"blocksize": [
|
||||
96,
|
||||
96,
|
||||
128
|
||||
],
|
||||
"blocktype": [
|
||||
[
|
||||
[
|
||||
1
|
||||
]
|
||||
]
|
||||
]
|
||||
},
|
||||
"Settings": {
|
||||
"deltat": 1.0,
|
||||
"deltax": 1.0,
|
||||
"handleFPE": "signal",
|
||||
"logger": {
|
||||
"group": 0,
|
||||
"steps": 100
|
||||
},
|
||||
"randomseed": 42,
|
||||
"statusoutput": 1,
|
||||
"timestepguard": 1,
|
||||
"timesteps": 10,
|
||||
"cuda": {
|
||||
"subblocks": {
|
||||
"blockDim": [8, 8, 8]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -253,6 +253,6 @@
|
||||
"randomseed": 42,
|
||||
"statusoutput": 1,
|
||||
"timestepguard": 1,
|
||||
"timesteps": 10
|
||||
"timesteps": 5
|
||||
}
|
||||
}
|
||||
|
||||
@ -253,7 +253,7 @@
|
||||
"randomseed": 42,
|
||||
"statusoutput": 1,
|
||||
"timestepguard": 1,
|
||||
"timesteps": 10,
|
||||
"timesteps": 5,
|
||||
"cuda": {
|
||||
"subblocks": {
|
||||
"blockDim": [8, 8, 8]
|
||||
|
||||
@ -1,193 +0,0 @@
|
||||
{
|
||||
"Application": "Cells",
|
||||
"CellsInSilico": {
|
||||
"2D": false,
|
||||
"adhesion": {
|
||||
"matrix": [
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 450.0],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 450.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 50.0]
|
||||
],
|
||||
"polarityenabled": false
|
||||
},
|
||||
"centerofmass": {
|
||||
"steps": 1
|
||||
},
|
||||
"cleaner": {
|
||||
"killdistance": 0,
|
||||
"steps": 100
|
||||
},
|
||||
"contactinhibition": {
|
||||
"enabled": false
|
||||
},
|
||||
"division": {
|
||||
"condition": [
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"( volume >= 0.9 * volume0 ) & ( rnd() <= 0.00001 ) & generation < 1"
|
||||
],
|
||||
"enabled": true,
|
||||
"halveSignals": false
|
||||
},
|
||||
"dynamicecm": {
|
||||
"alpha": 2.0,
|
||||
"beta": 0.5,
|
||||
"c": 4.0,
|
||||
"deltat": 0.1,
|
||||
"ecmCellID": 0,
|
||||
"enabled": true,
|
||||
"eta": 0.25,
|
||||
"k0": 0.1,
|
||||
"k1": 0.1,
|
||||
"lambda": 10.0,
|
||||
"phi": 1.0,
|
||||
"pushSteps": 10,
|
||||
"pushWeight": 0.5,
|
||||
"stepsPerMcs": 100
|
||||
},
|
||||
"ecmdegradation": {
|
||||
"enabled": false
|
||||
},
|
||||
"energyfunctions": [
|
||||
"Volume00",
|
||||
"Surface01",
|
||||
"Motility00",
|
||||
"Adhesion01",
|
||||
"DynamicECM00"
|
||||
],
|
||||
"liquid": 6,
|
||||
"logcellproperties": {
|
||||
"enabled": false
|
||||
},
|
||||
"orientation": {
|
||||
"enabled": true,
|
||||
"motility": "persistentRandomWalk",
|
||||
"motilityamount": [
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"numRandomNumbers": 5,
|
||||
"persistenceMagnitude": 0.0,
|
||||
"persistentDecay": 0.8,
|
||||
"recalculationtime": 200
|
||||
},
|
||||
"polarity": {
|
||||
"enabled": false
|
||||
},
|
||||
"signaling": {
|
||||
"constant": false,
|
||||
"enabled": false
|
||||
},
|
||||
"surface": {
|
||||
"default": {
|
||||
"storage": "const",
|
||||
"value": 400.0
|
||||
},
|
||||
"lambda": [
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
5.625,
|
||||
5.625,
|
||||
1.0
|
||||
],
|
||||
"sizechange": [
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
-0.05,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0
|
||||
]
|
||||
},
|
||||
"temperature": 50.0,
|
||||
"visitor": {
|
||||
"checkerboard": "01",
|
||||
"stepwidth": 10
|
||||
},
|
||||
"volume": {
|
||||
"default": {
|
||||
"storage": "const",
|
||||
"value": 500.0
|
||||
},
|
||||
"lambda": [
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
7.5,
|
||||
7.5,
|
||||
7.5
|
||||
],
|
||||
"sizechange": [
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
-0.05,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0
|
||||
]
|
||||
}
|
||||
},
|
||||
"DefineFunctions": [
|
||||
"r_angle()=360*rnd()",
|
||||
"r_size()=400*rnd()"
|
||||
],
|
||||
"Filling": {
|
||||
"initialoutput": false,
|
||||
"randomseed": 758959
|
||||
},
|
||||
"Settings": {
|
||||
"randomseed": 42,
|
||||
"statusoutput": 1,
|
||||
"timesteps": 10
|
||||
}
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user