data: cat 3 SGEMM repos — siboehm, wangzyon, edtallison (full clone, no --depth)

Sources:
  siboehm/SGEMM_CUDA        → upstream_ref/sgemm_siboehm/     (25 files)
  wangzyon/NVIDIA_SGEMM_PRACTICE → upstream_ref/nvidia_sgemm_practice/ (23 files, filled gaps)
  edtallison/sgemm-cuda      → upstream_ref/sgemm_edtallison/  (41 files)

All files cat'd one by one from git clone (no --depth).
These are the 3 public SGEMM repos that can compile on CUDA 10.2 + CoreX ivcore10.

Key files for BI-V100 porting:
  kernel 10 (warp tiling) — already proven on device with WARPSIZE=64
  kernel 11/12 (double buffering) — next optimization target
  sgemm.cu + runner.cu — complete build+benchmark harness
  CMakeLists.txt — build system reference

upstream_ref/nvidia_sgemm_practice/plot.py

@@ -0,0 +1,66 @@
+import os
+import re
+import matplotlib.pyplot as plt
+from matplotlib.pyplot import MultipleLocator
+import argparse
+
+
+def parse_file(file):
+    with open(file, 'r') as f:
+        lines = [line.strip() for line in f.readlines()]
+
+    data = []
+    pattern = "Average elasped time: \((.*?)\) second, performance: \((.*?)\) GFLOPS. size: \((.*?)\)."
+    for line in lines:
+        r = re.match(pattern, line)
+        if r:
+            gflops = float(r.group(2))
+            data.append(gflops)
+    return data
+
+
+def plot(num1, num2, y1, y2, save_dir):
+    x = [(i + 1) * 256 for i in range(len(y1))]
+    fig = plt.figure(figsize=(12, 10))
+    if num1 == 0:
+        num1 = "culas"
+
+    plt.plot(x, y1, c='k', linewidth=2, label=f"kernel_{num1}")
+    plt.plot(x, y2, c='b', linewidth=2, label=f"kernel_{num2}")
+    plt.legend()
+
+    plt.scatter(x, y1, marker="s", s=60, c='', edgecolors='k', linewidth=2)
+    plt.scatter(x, y2, marker="^", s=60, c='', edgecolors='b', linewidth=2)
+
+    plt.tick_params(labelsize=10)
+    plt.xlabel("Matrix size (M=N=K)", fontsize=12, fontweight='bold')
+    plt.ylabel("Performance (GFLOPS)", fontsize=12, fontweight='bold')
+
+    plt.title(f"Comparison bewteen: kernel_{num1} and kernel_{num2}", fontsize=16, fontweight='bold')
+
+    x_major_locator = MultipleLocator(256)
+    plt.gca().xaxis.set_major_locator(x_major_locator)
+
+    plt.savefig(f"{save_dir}/kernel_{num1}_vs_{num2}.png")
+
+
+def main(args):
+    root = os.path.dirname(os.path.abspath(__file__))
+    data1 = parse_file(os.path.join(root, f'test/test_kernel_{args.one}.txt'))
+    data2 = parse_file(os.path.join(root, f'test/test_kernel_{args.another}.txt'))
+    plot(args.one, args.another, data1, data2, args.save_dir)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='plot kernel performance')
+    parser.add_argument('one', type=int, help='one kernel num')
+    parser.add_argument('another', type=int, help='another kernel num')
+    parser.add_argument('--save_dir', default='images')
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)
+
+# python plot.py 0 1