ROCm/composable_kernel
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Ville Pietilä
2025-10-22 14:09:31 +00:00
parent 5065ddd409
commit d8559798d5
1 changed files with 51 additions and 8 deletions
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59
script/benchmark_ck_vs_ck_tile.py
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@@ -149,13 +149,6 @@ def run_analysis(results_file):
label = f"Case_{i+1}"
case_labels.append(label)
print(f"Case {i+1}: {label}")
print(f" CK Time: {ck_time:.6f}s")
print(f" CK Tile Time: {ck_tile_time:.6f}s")
print(f" CK Tile Performance: {ratio:.1f}% of CK performance")
print(f" CK Tile Kernel: {case.get('ck_tile_name', 'N/A')}")
print(f" CK Kernel: {case.get('ck_name', 'N/A')}")
print()
max_cases_to_detailed_plot = 10
if len(test_cases) < max_cases_to_detailed_plot:
@@ -167,7 +160,6 @@ def run_analysis(results_file):
colors = ['green' if ratio >= 100 else 'red' for ratio in performance_ratios]
bars = ax1.bar(x_pos, performance_ratios, color=colors, alpha=0.7)
#ax1.axhline(y=100, color='black', linestyle='--', linewidth=2, label='Parity (100%)')
ax1.set_xlabel('Test Cases')
ax1.set_ylabel('CK Tile Performance (% of CK)')
ax1.set_title('CK Tile vs CK Performance Comparison\n(>100% = CK Tile Faster, <100% = CK Faster)')
@@ -282,6 +274,57 @@ def run_analysis(results_file):
print(f"Performance analysis histogram saved to: {output_file}")
plt.close()
# Collect aggregated statistics for cases where CK is faster
print("\n" + "="*80)
print("CK FASTER TEST CASES - AGGREGATED STATISTICS")
print("="*80)
ck_faster_cases = []
ck_faster_ratios = []
ck_faster_kernels = {} # Track which CK kernels are winning
ck_tile_losing_kernels = {} # Track which CK Tile kernels are losing
for i, case in enumerate(test_cases):
ratio = performance_ratios[i]
if ratio < 100:
ck_faster_cases.append(case)
ck_faster_ratios.append(ratio)
# Count CK kernels that are winning
ck_kernel = case.get('ck_name', 'N/A')
if ck_kernel not in ck_faster_kernels:
ck_faster_kernels[ck_kernel] = {'count': 0, 'ratios': []}
ck_faster_kernels[ck_kernel]['count'] += 1
ck_faster_kernels[ck_kernel]['ratios'].append(ratio)
# Count CK Tile kernels that are losing
ck_tile_kernel = case.get('ck_tile_name', 'N/A')
if ck_tile_kernel not in ck_tile_losing_kernels:
ck_tile_losing_kernels[ck_tile_kernel] = {'count': 0, 'ratios': []}
ck_tile_losing_kernels[ck_tile_kernel]['count'] += 1
ck_tile_losing_kernels[ck_tile_kernel]['ratios'].append(ratio)
if ck_faster_cases:
print(f"Number of cases where CK is faster: {len(ck_faster_cases)}/{len(test_cases)} ({len(ck_faster_cases)/len(test_cases)*100:.1f}%)")
print(f"Average CK performance advantage: {100 - np.mean(ck_faster_ratios):.1f}%")
print(f"Median CK performance advantage: {100 - np.median(ck_faster_ratios):.1f}%")
print(f"Best CK performance advantage: {100 - np.min(ck_faster_ratios):.1f}%")
print(f"Worst CK performance advantage: {100 - np.max(ck_faster_ratios):.1f}%")
print(f"\nTop CK kernels that outperform CK Tile:")
sorted_ck_kernels = sorted(ck_faster_kernels.items(), key=lambda x: x[1]['count'], reverse=True)
for kernel, stats in sorted_ck_kernels[:5]: # Show top 5
avg_advantage = 100 - np.mean(stats['ratios'])
print(f" {kernel}: {stats['count']} wins, avg advantage: {avg_advantage:.1f}%")
print(f"\nCK Tile kernels that lose most often:")
sorted_ck_tile_kernels = sorted(ck_tile_losing_kernels.items(), key=lambda x: x[1]['count'], reverse=True)
for kernel, stats in sorted_ck_tile_kernels[:5]: # Show top 5
avg_disadvantage = np.mean(stats['ratios'])
print(f" {kernel}: {stats['count']} losses, avg performance: {avg_disadvantage:.1f}% of CK")
else:
print("No cases found where CK is faster than CK Tile.")
def main():
args = parse_cli_args()