Benchmarking of CUDA cluster finder

python/tests/nsys_kernel_probe.py

@@ -0,0 +1,32 @@
+# Minimal probe for nsys: train pedestal, run one batched pass, print summary.
+# Usage: nsys_kernel_probe.py [n_streams] [n_frames]
+import sys
+sys.path.append('/home/ferjao_k/aare/build')
+
+from pathlib import Path
+import time
+from aare import File, ClusterFinderCUDA
+
+n_streams = int(sys.argv[1]) if len(sys.argv) > 1 else 8
+N         = int(sys.argv[2]) if len(sys.argv) > 2 else 2000
+
+base = Path('/mnt/sls_det_storage/moench_data/2603_MaxIVBeamtime/2026032408/process/xrf/')
+f  = File(base / 'Cu_factor_10_data_master_0.json')
+pd = File(base / 'Cu_factor_10_pedestal_master_0.json')
+
+cf = ClusterFinderCUDA((f.rows, f.cols), (3, 3), n_sigma=5,
+                       max_clusters_per_frame=3000, n_streams=n_streams)
+for _ in range(1000):
+    cf.push_pedestal_frame(pd.read_frame().copy())
+
+data = f.read_n(N)
+cf.register_input_buffer(data)
+
+t0 = time.perf_counter()
+res = cf.find_clusters_batched(data, first_frame=0)
+t = time.perf_counter() - t0
+
+cf.unregister_input_buffer()
+n = sum(cv.size for cv in res)
+print(f'n_streams={n_streams}  N={N}  wall={t:.3f}s  ({N/t:.0f} FPS)  '
+      f'clusters/frame={n/N:.2f}  event kernel_ms={cf.avg_kernel_time_ms():.3f}')