Add booster strong scaling configs

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

@@ -3,6 +3,7 @@
 
 import argparse
 import json
+import math
 
 
 def print_table(data, spec):
@@ -31,6 +32,9 @@ if __name__ == "__main__":
         "mean_time": lambda job: job["means"]["TimeStep"],
         "std_time": lambda job: job["stds"]["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,
     }
     print_table(jobs, scaling_spec)

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

@@ -0,0 +1,34 @@
+#!/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/timing.py

@@ -76,6 +76,7 @@ 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 = []
@@ -99,6 +100,8 @@ if __name__ == "__main__":
             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,