updated create csv and updated plots

analysis/make_csv.py

@@ -27,6 +27,14 @@ def num(pattern, text):
 #min cycles is the standard way to kill the OS/turbo noise.
 best = {}
 
+# flop counter is uint32 and wraps at n=10000 (72*n^2+74*n = 7.2e9 > 2^32), so the logged
+# W(n)/I(n) are garbage there. compute work ourselves; matches the good logs exactly.
+# symmetry variants do half the pairs -> 36 n^2 + 110 n, everything else 72 n^2 + 74 n.
+def flops_per_iter(variant, n):
+    if "symmetry" in variant:
+        return 36 * n * n + 110 * n
+    return 72 * n * n + 74 * n
+
 for variant in sorted(os.listdir(ROOT)):
     logdir = os.path.join(ROOT, variant, "x86")
     if not os.path.isdir(logdir):
@@ -48,20 +56,20 @@ for variant in sorted(os.listdir(ROOT)):
             cycles = num(r"T\(n\):\s*([\d.]+)", block)
             if cycles is None:
                 continue
-            W = num(r"W\(n\):\s*([\d.]+)", block)
 
             key = (variant, n)
-            if key not in best or cycles < best[key][2]:
-                best[key] = (variant, n, cycles, n_iter, W,
-                             num(r"Q\(n\) per iteration:\s*([\d.]+)", block),
-                             num(r"I\(n\):\s*([\d.]+)", block))
+            if key not in best or cycles < best[key][0]:
+                best[key] = (cycles, n_iter,
+                             num(r"Q\(n\) per iteration:\s*([\d.]+)", block))
 
 with open(OUT, "w", newline="") as f:
     w = csv.writer(f)
     w.writerow(["variant", "n", "n_iter", "W", "Q_per_iter", "I", "cycles", "flops_per_cycle"])
     for variant, n in sorted(best):
-        _, _, cycles, n_iter, W, q, I = best[(variant, n)]
+        cycles, n_iter, q = best[(variant, n)]
+        w_iter = flops_per_iter(variant, n)
+        W = w_iter * n_iter
+        I = w_iter / q
         fc = W / cycles
-        w.writerow([variant, n, n_iter, W, q, I, round(cycles, 1), round(fc, 3)])
-
+        w.writerow([variant, n, n_iter, W, q, round(I, 2), round(cycles, 1), round(fc, 3)])
 print("wrote", OUT, "with", len(best), "rows")