experiments/eval/Makefile

@@ -4,9 +4,6 @@ generate-batch-strong-cpu:
 generate-batch-strong-gpu:
 	python scripts/substitute.py strong-batch.j2 < strong-gpu.json
 
-generate-batch-strong-booster:
-	python scripts/substitute.py strong-batch.j2 < strong-booster.json
-
 clean-logs:
 	rm logs/*
 

experiments/eval/data/strong-booster.tsv

@@ -1,9 +0,0 @@
-label	nodes	tasks	mean_time	std_time	speedup	speedup_std	speedup_error
-1	1	1	161.08257980000002	1.0725827874500897	1.0	0.0066585895804611986	0.004614167168572558
-1	1	2	109.2771463	1.3498701967829918	1.474073813730255	0.018208823861030076	0.012618071172974463
-1	1	4	94.5763083	2.921107861707173	1.7032022363258181	0.052605536545441436	0.03645377698685362
-2	2	8	75.06436665	5.345617315509918	2.145926049720424	0.15281950626022228	0.10589851499069955
-4	4	16	42.842002025	1.5314070839490197	3.759921856733072	0.13440013758310768	0.09313454370387199
-8	8	32	16.40522446875	4.395440141154071	9.818980539208297	2.6307924825691393	1.823046180232846
-16	16	64	9.361684956249999	5.010216046116417	17.206579857449583	9.208671612370724	6.381283859970849
-32	32	128	5.214438346875	0.113661681579046	30.891645290338204	0.6733604113945479	0.4666149588180552

experiments/eval/data/strong-cpu.tsv

@@ -1,10 +1,10 @@
-label	nodes	tasks	mean_time	std_time	speedup	speedup_std	speedup_error
+label	nodes	mean_time	std_time	speedup	speedup_std
-1	1	48	1953.3832839708334	14.087268287606769	1.0	0.007211727674340593	0.004711662080569187
+1	1	1132.1093720291667	18.428644622186628	1.0	0.016278148629010596
-2	2	96	1046.3540047312501	6.0880471399828595	1.866847429396085	0.010861959816590314	0.007096480413505672
+2	2	619.9614289104168	7.890810670766857	1.8260964621925766	0.029725469622481192
-4	4	192	566.0709002166666	5.005298021298787	3.4507749527897755	0.030512356378918384	0.019934739500893344
+4	4	348.657283046875	3.3739994706945122	3.247054993762918	0.052856043795043856
-8	8	384	318.1225953708333	9.706195475255436	6.1403475024896865	0.18734731205020752	0.12240024387280224
+8	8	200.79244575312498	1.259910347938215	5.63820699420684	0.09177957145282603
-16	16	768	178.56471887994792	9.393026972005948	10.939357428631386	0.5754422263707719	0.375955587895571
+16	16	111.37033198671875	0.3943541234366863	10.16526889911915	0.16547175799372044
-32	32	1536	99.82801128216144	1.4413062581268035	19.56748670921274	0.28251330150299214	0.18457535698195485
+32	32	65.69918741145833	0.43464214827532205	17.231710415823503	0.28050034328084494
-64	64	3072	55.75610567220052	1.0303316198245824	35.034428255357376	0.6474103379133267	0.42297475410337343
+64	64	38.56257490957031	0.12227397957602136	29.35772247273364	0.4778893698204027
-128	128	6144	31.589132888118492	0.32226687791817415	61.83719226764697	0.6308523555205254	0.41215687227340997
+128	128	21.433497051595047	0.14016683538819355	52.81962944749218	0.8598057786755426
-256	256	12288	19.11704500200738	0.18528928846832715	102.18018965617955	0.9903672160087601	0.6470399144590566
+256	256	13.247708324598525	0.030540850807946187	85.45699711149668	1.3910817003698723

experiments/eval/data/strong-gpu.tsv

@@ -1,9 +1,8 @@
-label	nodes	tasks	mean_time	std_time	speedup	speedup_std	speedup_error
+label	nodes	tasks	mean_time	std_time	speedup	speedup_std
-1	1	1	316.56674059999995	1.3298279828832311	1.0	0.004200782370133899	0.002910993666206366
+1	1	1	157.5217688	0.5311809049610176	1.0	0.003372111099358209
-1	1	2	239.7297573	1.3705192585818793	1.3205150005797797	0.007549297425250635	0.00523139621453709
+1	1	2	120.15237200000001	0.299200156243067	1.3110167213344732	0.004420894037456185
-1	1	4	150.86725470000002	1.463195211854327	2.098313124537819	0.020350616990413285	0.014102258089746373
+1	1	4	75.69762635	0.5769735213369493	2.0809340582447473	0.007017140834839633
-2	2	8	103.758313	1.3959932139830615	3.051001230137579	0.041049019495199714	0.028445519245192213
+2	2	8	52.110670325	0.940887445857671	3.022831366735062	0.010193323203255446
-4	4	16	56.423368849999996	0.8293394846143929	5.610560784514376	0.08246688711191473	0.057146637198176795
+4	4	16	28.215493675000005	0.5598524922936691	5.582811012077745	0.018825858979446598
-8	8	32	24.168551143749998	0.5503559531334744	13.09829202078025	0.29826872726631265	0.20668968287257275
+8	8	32	12.422501156249998	0.31940690526235005	12.680358554102268	0.04275957782413007
-16	16	64	15.05716248125	0.04648043269670786	21.024329185144023	0.06490066896072916	0.044973869063161365
+16	16	64	7.70770194375	0.04068644910316383	20.43693048194875	0.06891560011499148
-32	32	128	9.9111531140625	0.08848604506395903	31.940455056721632	0.28516203039001964	0.19760720531719309

experiments/eval/scripts/build-pgf-datafiles.py

@@ -3,7 +3,6 @@
 
 import argparse
 import json
-import math
 
 
 def print_table(data, spec):
@@ -20,33 +19,18 @@ def print_table(data, spec):
 if __name__ == "__main__":
     p = argparse.ArgumentParser(description="Turn files generated by timing.py into pgf datafiles")
     p.add_argument("timing_file")
-    p.add_argument("--weak", action="store_true")
     args = p.parse_args()
 
     with open(args.timing_file, "r", encoding="utf8") as f:
         jobs = json.load(f)
 
-    if not args.weak:
+    scaling_spec = {
-        scaling_spec = {
+        "label": lambda job: job["accounting"][0]["nodes"]["count"],
-            "label": lambda job: job["accounting"][0]["nodes"]["count"],
+        "nodes": lambda job: job["accounting"][0]["nodes"]["count"],
-            "nodes": lambda job: job["accounting"][0]["nodes"]["count"],
+        "tasks": lambda job: job["accounting"][0]["tasks"]["count"],
-            "tasks": lambda job: job["accounting"][0]["tasks"]["count"],
+        "mean_time": lambda job: job["means"]["TimeStep"],
-            "mean_time": lambda job: job["means"]["TimeStep"],
+        "std_time": lambda job: job["stds"]["TimeStep"],
-            "std_time": lambda job: job["stds"]["TimeStep"],
+        "speedup": lambda job: jobs[0]["means"]["TimeStep"] / job["means"]["TimeStep"],
-            "speedup": lambda job: jobs[0]["means"]["TimeStep"] / job["means"]["TimeStep"],
+        "speedup_std": lambda job: jobs[0]["stds"]["TimeStep"] / job["means"]["TimeStep"],
-            # Standard deviation scaled to speedup
+    }
-            "speedup_std": lambda job: (jobs[0]["means"]["TimeStep"] / job["means"]["TimeStep"]) * (job["stds"]["TimeStep"] / job["means"]["TimeStep"]),
-            # 95% confidence interval
-            "speedup_error": lambda job: (jobs[0]["means"]["TimeStep"] / job["means"]["TimeStep"]) * (job["stds"]["TimeStep"] / job["means"]["TimeStep"]) / math.sqrt(len(jobs)) * 1.96,
-        }
-    else:
-        scaling_spec = {
-            "nodes": lambda job: job["accounting"][0]["nodes"]["count"],
-            "tasks": lambda job: job["accounting"][0]["tasks"]["count"],
-            "mean_time": lambda job: job["means"]["TimeStep"],
-            "std_time": lambda job: job["stds"]["TimeStep"],
-            "efficiency": lambda job: jobs[0]["means"]["TimeStep"] / job["means"]["TimeStep"],
-            "efficiency_error": lambda job: (jobs[0]["means"]["TimeStep"] / job["means"]["TimeStep"]) * (job["stds"]["TimeStep"] / job["means"]["TimeStep"]) / math.sqrt(len(jobs)) * 1.96,
-        }
-
     print_table(jobs, scaling_spec)

experiments/eval/scripts/generate-strong-booster-configs.py

@@ -1,34 +0,0 @@
-#!/usr/bin/env python
-
-import copy
-import json
-
-from pathlib import Path
-
-SIZE = [384, 384, 384]
-
-with (Path(__file__).parent.parent / "templates" / "strong-booster.json").open(encoding="utf8") as f:
-    template = json.load(f)
-
-configs = [
-    [  1,  1,  1],
-    [  1,  1,  2],
-    [  1,  2,  2],
-    [  2,  2,  2],
-    [  2,  2,  4],
-    [  2,  4,  4],
-    [  4,  4,  4],
-    [  4,  4,  8],
-]
-
-out_path = Path(__file__).parent.parent / "generated" / "config"
-
-for c in configs:
-    nc = copy.deepcopy(template)
-    nc["Geometry"]["blockcount"] = c
-    nc["Geometry"]["blocksize"] = [bs // bc for bc, bs in zip(c, SIZE)]
-
-    nc_out_path = out_path / f"strong-booster-{c[0]:02}-{c[1]:02}-{c[2]:02}.json"
-    print(f"Dumping {(c[0] * c[1] * c[2])} to {nc_out_path}")
-    with nc_out_path.open("w", encoding="utf8") as f:
-        json.dump(nc, f)

experiments/eval/scripts/generate-weak-configs.py

@@ -1,152 +0,0 @@
-#!/usr/bin/env python
-
-
-import jinja2
-import json
-import sys
-
-
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Tuple
-
-
-SIZE = (192, 192, 192)
-
-
-templates_env = jinja2.Environment(
-    loader=jinja2.FileSystemLoader(Path(__file__).parent.parent / "templates"),
-    autoescape=jinja2.select_autoescape()
-)
-        
-
-@dataclass
-class Experiment:
-    job_name: str
-    account: str
-    partition: str
-    nastja_binary_path: str
-    nodes: int
-    tasks: int
-    num_blocks: Tuple[int, int, int]
-    domain_scale: Tuple[int, int, int]
-    time: str = "00:15:00"
-    extra_sbatch_line: str = ""
-    logfile_path: str = "/p/project/cellsinsilico/paulslustigebude/ma/experiments/eval/logs/%x-%A.%a"
-    config_path: str = "/p/project/cellsinsilico/paulslustigebude/ma/experiments/eval/generated/config/${SLURM_JOB_NAME}.json"
-    output_dir_path: str = "/p/scratch/cellsinsilico/paul/nastja-out/${SLURM_JOB_NAME}-${SLURM_ARRAY_JOB_ID}.${SLURM_ARRAY_TASK_ID}"
-
-    def get_config(self):
-        with (Path(__file__).parent.parent / "templates" / "weak.json").open(encoding="utf8") as f:
-            config = json.load(f)
-
-        size = (
-            SIZE[0] * self.domain_scale[0],
-            SIZE[1] * self.domain_scale[1],
-            SIZE[2] * self.domain_scale[2],
-        )
-        blocksize = (
-            size[0] // self.num_blocks[0],
-            size[1] // self.num_blocks[1],
-            size[2] // self.num_blocks[2],
-        )
-        config["Geometry"] = {
-            "blockcount": list(self.num_blocks),
-            "blocksize": list(blocksize),
-        }
-
-        cells_filling = [{
-            "box": [
-                [0, 0, 0],
-                list(size)
-            ],
-            "celltype": 0,
-            "component": 0,
-            "pattern": "const",
-            "seed": 0,
-            "shape": "cube",
-            "value": 0,
-        }]
-        for z in range(self.domain_scale[2]):
-            for y in range(self.domain_scale[1]):
-                for x in range(self.domain_scale[0]):
-                    cx = x * SIZE[0] + SIZE[0] // 2
-                    cy = y * SIZE[1] + SIZE[1] // 2
-                    cz = z * SIZE[2] + SIZE[2] // 2
-                    cells_filling.append({
-                        "shape": "sphere",
-                        "pattern": "voronoi",
-                        "count": 715,
-                        "radius": 38,
-                        "center": [cx, cy, cz],
-                        "box": [
-                            [cx - 38, cy - 38, cz - 38],
-                            [cx + 38, cy + 38, cz + 38]
-                        ],
-                        "celltype": 9,
-                        "seed": 758960,
-                    })
-
-        config["Filling"]["cells"] = cells_filling
-
-        return config
-    
-    def write_batch_file(self, out_path: Path):
-        t = templates_env.get_template("strong-batch.j2")
-        t.stream(
-            name=self.job_name,
-            account=self.account,
-            partition=self.partition,
-            nodes=self.nodes,
-            tasks=self.tasks,
-            extra_sbatch_line=self.extra_sbatch_line,
-            time=self.time,
-            logfile_path=self.logfile_path,
-            nastja_binary_path=self.nastja_binary_path,
-            config_path=self.config_path,
-            output_dir_path=self.output_dir_path,
-        ).dump(str(out_path))
-
-
-def make_cpu_ex(x: int, y: int, z: int) -> Experiment:
-    num_blocks = x * y * z
-    assert num_blocks % 48 == 0
-    num_nodes = num_blocks // 48
-
-    assert x % 4 == 0
-    assert y % 4 == 0
-    assert z % 3 == 0
-
-    return Experiment(
-        job_name=f"weak-cpu-{x:02}-{y:02}-{z:02}",
-        account="cellsinsilico",
-        partition="batch",
-        nastja_binary_path="/p/project/cellsinsilico/paulslustigebude/nastja/build-nocuda/nastja",
-        nodes=num_nodes,
-        tasks=num_blocks,
-        num_blocks=(x, y, z),
-        domain_scale=(x // 4, y // 4, z // 3),
-    )
-
-
-experiments = [
-    make_cpu_ex(4, 4, 3),
-    make_cpu_ex(4, 4, 6),
-    make_cpu_ex(4, 4, 12),
-    make_cpu_ex(4, 8, 12),
-    make_cpu_ex(8, 8, 12),
-    make_cpu_ex(8, 8, 24),
-    make_cpu_ex(8, 16, 24),
-    make_cpu_ex(16, 16, 24),
-]
-
-if __name__ == "__main__":
-    outdir = Path(__file__).parent.parent / "generated"
-
-    for e in experiments:
-        print(f"Generating config for {e.job_name}", file=sys.stderr)
-        config_path = (outdir / "config" / e.job_name).with_suffix(".json")
-        with config_path.open("w", encoding="utf8") as f:
-            json.dump(e.get_config(), f, indent=2)
-        print(f"Generating batch file for {e.job_name}", file=sys.stderr)
-        e.write_batch_file(outdir / "batch" / e.job_name)

experiments/eval/scripts/make-latex-table.py

@@ -1,20 +0,0 @@
-#!/usr/bin/env python
-
-
-import argparse
-import pandas
-
-
-def show_seconds(s: float) -> str:
-    return f"{s:.2f}s"
-
-
-if __name__ == '__main__':
-    p = argparse.ArgumentParser(description="Make a latex table from a timings tsv")
-    p.add_argument("timingfile")
-    args = p.parse_args()
-
-    df = pandas.read_csv(args.timingfile, sep="\t")
-
-    for i in range(len(df)):
-        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} \\\\")

experiments/eval/scripts/timing.py

@@ -44,13 +44,12 @@ def get_outdirs(jobid: str):
     return sorted(Path("/p/scratch/cellsinsilico/paul/nastja-out").glob(f"*{jobid}*"))
 
 
-def load_array_mean_timings(jobid: str, excluded_array_indices):
+def load_array_mean_timings(jobid: str):
-    mts = []
+    mts = [
-    for outdir_path in get_outdirs(jobid):
+        load_mean_timings(outdir_path)
-        if any(str(outdir_path).endswith(str(i)) for i in excluded_array_indices):
+        for outdir_path
-            print(f"Not loading timings for {outdir_path} because it was excluded.", file=sys.stderr)
+        in get_outdirs(jobid)
-            continue
+    ]
-        mts.append(load_mean_timings(outdir_path))
 
     return pandas.DataFrame(list(mts), columns=mts[0].index)
 
@@ -76,37 +75,18 @@ if __name__ == "__main__":
     p = argparse.ArgumentParser(description="Load and analzye data from nastja timing files")
     p.add_argument("jobid", nargs="+")
     p.add_argument("--prettify", action="store_true")
-    p.add_argument("--dump-timings", action="store_true")
     args = p.parse_args()
 
     results = []
     for i, jobid in enumerate(args.jobid, 1):
         print(f"({i:2}/{len(args.jobid):2}) Loading accounting data for {jobid}", file=sys.stderr)
         accounting_data = get_accounting_data(jobid)
-
+        array_mean_timings = load_array_mean_timings(jobid)
-        jobs = []
-        excluded_array_indices = []
-        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)
 
         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))

experiments/eval/strong-booster.json

@@ -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"
-    }
-  ]
-}

experiments/eval/strong-cpu.json

@@ -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",

experiments/eval/templates/strong-booster.json

@@ -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]
-      }
-    }
-  }
-}

experiments/eval/templates/strong-cpu.json

@@ -253,6 +253,6 @@
     "randomseed": 42,
     "statusoutput": 1,
     "timestepguard": 1,
-    "timesteps": 10
+    "timesteps": 5
   }
 }

experiments/eval/templates/strong-gpu.json

@@ -253,7 +253,7 @@
     "randomseed": 42,
     "statusoutput": 1,
     "timestepguard": 1,
-    "timesteps": 10,
+    "timesteps": 5,
     "cuda": {
       "subblocks": {
         "blockDim": [8, 8, 8]

experiments/eval/templates/weak.json

@@ -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
-  }
-}