56fd68e7dd
CCCL (CUDA C++ Core Libraries) provides: - CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk) - Thrust: high-level parallel algorithms (transform_reduce, sort, scan) - libcudacxx: CUDA C++ standard library (atomics, barriers, memory) - cudax: experimental features (memory resources, allocators) - Tuning policies: per-SM hardware-specific algorithm parameters Competition optimization vectors mapped to CCCL: - Output TPS (83% weight): warp_reduce, block_reduce, device_topk - Input TPS (14% weight): device_scan, block_load, prefetch - Cache TPS (3% weight): prefix caching strategy patterns - Memory (0.9 util): pooled/cached/buddy allocators Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only) License: Apache-2.0
73 lines
2.4 KiB
Python
Executable File
73 lines
2.4 KiB
Python
Executable File
#!/usr/bin/env python3
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import argparse
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import sys
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import cccl.bench
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def parse_arguments():
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parser = argparse.ArgumentParser(description="Verify tuning variant")
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parser.add_argument(
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"--variant", type=str, help="Variant to verify", default=None, required=True
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)
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variant = parser.parse_known_args()[0].variant
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sys.argv.remove("--variant={}".format(variant))
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return variant
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def workload_header(ct_workload_space, rt_workload_space):
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for ct_workload in ct_workload_space:
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for rt_workload in rt_workload_space:
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workload_point = ct_workload + rt_workload
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return ", ".join([x.split("=")[0] for x in workload_point])
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def workload_entry(ct_workload, rt_workload):
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workload_point = ct_workload + rt_workload
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return ", ".join([x.split("=")[1] for x in workload_point])
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class VerifySeeker:
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def __init__(self, variant_label):
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self.label = variant_label
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self.estimator = cccl.bench.MedianCenterEstimator()
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def __call__(self, algname, ct_workload_space, rt_workload_space):
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variant_point = cccl.bench.Config().label_to_variant_point(algname, self.label)
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print(
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"{}, MinS, MedianS, MaxS".format(
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workload_header(ct_workload_space, rt_workload_space)
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)
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)
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for ct_workload in ct_workload_space:
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bench = cccl.bench.Bench(algname, variant_point, list(ct_workload))
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if bench.build():
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base = bench.get_base()
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for rt_workload in rt_workload_space:
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workload_point = ct_workload + rt_workload
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base_samples, base_elapsed = base.do_run(workload_point, None)
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variant_samples, _ = bench.do_run(workload_point, base_elapsed * 10)
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min_speedup = min(base_samples) / min(variant_samples)
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median_speedup = self.estimator(base_samples) / self.estimator(
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variant_samples
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)
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max_speedup = max(base_samples) / max(variant_samples)
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point_str = workload_entry(ct_workload, rt_workload)
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print(
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"{}, {}, {}, {}".format(
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point_str, min_speedup, median_speedup, max_speedup
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)
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)
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def main():
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cccl.bench.search(VerifySeeker(parse_arguments()))
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if __name__ == "__main__":
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main()
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