Add strong scaling GPU actions table

experiments/eval/data/actions-normalized.csv

@@ -0,0 +1,4 @@
+label,BC:cells,BC:dynamicecm,Sweep:Cells,Sweep:DynamicECM,Sweep:DynamicECMDampers,Filling,Other
+CPU,0.04629667717328299,3.88243023434036,0.002164069189058585,82.18255346583861,13.842310640806883,0.005797982839735358,0.03844692981207934
+GPU,0.034897486509106485,41.227398706349675,13.180664898871365,22.432483220415783,19.749714111977486,1.9939606413394697,1.3808809345371023
+Booster,0.09128030041871667,42.736382370072874,12.260298369798207,22.36492489089858,16.070196185259338,4.364304866307413,2.1126130172448816

experiments/eval/data/actions.csv

@@ -0,0 +1,4 @@
+label,BC:cells,BC:dynamicecm,Sweep:Cells,Sweep:DynamicECM,Sweep:DynamicECMDampers,Other
+CPU,0.9047934333333334,75.87579921250001,0.04229322083333333,1606.12465625,270.5255006333333,0.8646950250000001
+GPU,0.11372260000000001,134.35027680000002,42.952648800000006,73.102122,64.3596162,10.9978052
+Booster,0.155221,72.67267939999999,20.848482800000003,38.0312728,27.327165999999995,11.013917200000002

experiments/eval/data/strong-gpu-actions.csv

@@ -0,0 +1,9 @@
+label,BC:cells,BC:dynamicecm,Sweep:Cells,Sweep:DynamicECM,Sweep:DynamicECMDampers,Filling,Other
+1,0.034897486509106485,41.227398706349675,13.180664898871365,22.432483220415783,19.749714111977486,1.9939606413394697,1.3808809345371023
+2,0.7137918070198439,50.7137377526309,8.923981074147767,19.35772833011394,17.571497161475467,1.320924803923136,1.3983390706889622
+4,0.6647403410134337,58.291944402330074,7.087118055335309,16.67352093599457,15.236952119238136,1.092645293437499,0.953078852650984
+8,0.6788278363464717,65.05900206274784,5.2089254465999275,14.249993130938188,13.176763096049008,0.7851584656500297,0.8413299616685318
+16,1.441574901760373,61.1245906198054,4.832237599492141,16.029377636605524,14.996293924006913,0.716990077435801,0.858935240893841
+32,3.3736940900007966,47.94830950907802,5.622259685491874,21.238716402604393,19.97281149065964,0.8196124452570666,1.0245963769081996
+64,6.284545018581091,45.28808866195176,4.502683326147901,21.66574860147326,20.522789117514915,0.6460791296539419,1.0900661446771265
+128,11.788574344478201,40.04977497065291,3.2677482351392375,22.1995263401806,21.209134936528987,0.4598839494487746,1.0253572235712836

experiments/eval/scripts/timing-actions.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python
+
+
+import argparse
+import sys
+
+
+import timing
+
+
+ignore = ["TimeStep"]
+
+
+if __name__ == "__main__":
+    p = argparse.ArgumentParser()
+    p.add_argument("jobs", nargs="+")
+    p.add_argument("--normalize", action="store_true")
+    p.add_argument("--extra-columns", nargs="*")
+    args = p.parse_args()
+
+    columns = [
+        "BC:cells",
+        "BC:dynamicecm",
+        "Sweep:Cells",
+        "Sweep:DynamicECM",
+        "Sweep:DynamicECMDampers",
+    ] + (args.extra_columns or [])
+
+    dfs = dict()
+    labels = []
+    for label, jobid in [jobarg.split(":") for jobarg in args.jobs]:
+        jobs, excluded_array_indices = timing.get_jobs(jobid)
+        df = timing.load_array_mean_timings(jobid, excluded_array_indices).mean()
+        dfs[label] = df
+        labels.extend(df.index)
+
+    labels = set(labels)
+    print(",".join(["label"] + columns + ["Other"]))
+
+    values_by_label = dict()
+    for label, df in dfs.items():
+        values = {"Other": 0}
+        for c in df.index:
+            if c in ignore:
+                continue
+            elif c not in columns:
+                values["Other"] += df[c]
+                print(f"Others+= {c}={df[c]}", file=sys.stderr)
+            else:
+                values[c] = df[c]
+        values_by_label[label] = values
+
+    if args.normalize:
+        print("Normalizing data to 100%...", file=sys.stderr)
+        for values in values_by_label.values():
+            row_length = sum(values.values())
+            for c in values.keys():
+                values[c] *= 100 / row_length
+
+    for label, values in values_by_label.items():
+        print(label + "," + ",".join(f"{values[c]}" for c in columns + ["Other"]))

experiments/eval/scripts/timing.py

@@ -72,18 +72,8 @@ def get_accounting_data(jobid: str):
     return json.loads(sacct_results.stdout.decode("utf8"))
 
 
-if __name__ == "__main__":
+def get_jobs(jobid: str):
-    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)
-
     jobs = []
     excluded_array_indices = []
     for array_job in accounting_data["jobs"]:
@@ -99,6 +89,20 @@ if __name__ == "__main__":
             continue
         jobs.append(last_step)
 
+    return jobs, excluded_array_indices
+
+
+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)
+        jobs, excluded_array_indices = get_jobs(jobid)
         array_mean_timings = load_array_mean_timings(jobid, excluded_array_indices)
         if args.dump_timings:
             print(array_mean_timings, file=sys.stderr)