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kferjaouiandClaude Opus 5.5
b7b786f423
Make room for probes of other CUDA classes. common.py and gpu_span.py stay in perf/ and become shared; the cluster-finder driver, traced process, README and results move to perf/cf/. - common.py: assert_build_fresh() takes the header list, capture_env() takes extra fields, results_dir() takes the probe folder. - cf/cf_common.py: pedestal frames, n_sigma, rebuild-guard headers, DEVICE_PED_TYPE and the <date>_<f32|f64> results naming. - gpu_span.py: optional kernel-name filter; tolerates an engine with no ops. Output without a filter is unchanged. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
50 lines
1.9 KiB
Python
50 lines
1.9 KiB
Python
"""The process nsys traces: train the pedestal, run one batched pass, exit.
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nsys_kernel_probe.py <n_streams> <n_frames> <cluster_dim> <cap> <batch>
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Run by run_probes.py; run directly only under `nsys profile --trace=cuda`.
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The wall time printed is profiler-inflated and is not a throughput number.
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"""
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import sys
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import time
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parent))
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import cf_common # noqa: E402 (puts ../ and the build dir on sys.path)
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from cf_common import common # noqa: E402
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from aare import File, ClusterFinderCUDA # noqa: E402
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n_streams, N, cdim, cap, batch = (int(a) for a in sys.argv[1:6])
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f = File(common.DATA_FILE)
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pd = File(common.PEDESTAL_FILE)
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cf = ClusterFinderCUDA((f.rows, f.cols), cluster_size=(cdim, cdim),
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n_sigma=cf_common.N_SIGMA, max_clusters_per_frame=cap,
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n_streams=n_streams)
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for _ in range(cf_common.N_PEDESTAL_FRAMES):
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cf.push_pedestal_frame(pd.read_frame().copy())
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data = f.read_n(N)
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cf.register_input_buffer(data)
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# Pin the output slots without processing frames: a warm-up pass would advance
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# the device pedestal and make this run incomparable with the next.
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cf.reserve_output_slots(cf.chunk_size_for(min(N, batch)))
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t0 = time.perf_counter()
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n = 0
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for start in range(0, N, batch):
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for cv in cf.find_clusters_batched(data[start:min(start + batch, N)],
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first_frame=start):
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n += cv.size
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t = time.perf_counter() - t0
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cf.unregister_input_buffer()
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h2d = f.rows * f.cols * 2
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slot = 2 + 2 + cdim * cdim * 4 # x, y (uint16) + data (int32)
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print(f"n_streams={n_streams} N={N} cluster={cdim}x{cdim} cap={cap} batch={batch}")
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print(f" H2D/frame={h2d:,} B D2H/frame={cap * slot:,} B "
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f"({slot} B/slot, {100 * n / N / cap:.0f}% filled)")
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print(f" wall={t:.3f}s ({N / t:.0f} FPS, profiler-inflated) clusters/frame={n / N:.2f}")
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