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
82 lines
2.5 KiB
Python
Executable File
82 lines
2.5 KiB
Python
Executable File
#!/usr/bin/env python3
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import math
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import os
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import sys
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import cccl.bench
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def elapsed_time_looks_good(x):
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if isinstance(x, float):
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if math.isfinite(x):
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return True
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return False
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def get_largest_problem_size(rt_values):
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# Small problem sizes do not utilize entire GPU.
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# Benchmarking small problem sizes in environments where we do not control
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# distributions comparison, e.g. CI, is not useful because of stability issues.
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elements = []
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for element in rt_values:
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if element.isdigit():
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elements.append(int(element))
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return [str(max(elements))]
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def filter_runtime_workloads_for_ci(rt_values):
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for subbench in rt_values:
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for axis in rt_values[subbench]:
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if axis.startswith("Elements") and axis.endswith("[pow2]"):
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rt_values[subbench][axis] = get_largest_problem_size(
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rt_values[subbench][axis]
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)
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return rt_values
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class BaseRunner:
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def __init__(self):
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self.estimator = cccl.bench.MedianCenterEstimator()
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def __call__(self, algname, ct_workload_space, rt_values):
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failure_occured = False
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rt_values = filter_runtime_workloads_for_ci(rt_values)
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for ct_workload in ct_workload_space:
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bench = cccl.bench.BaseBench(algname)
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if bench.build(): # might throw
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results = bench.run(ct_workload, rt_values, self.estimator, False)
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for subbench in results:
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for point in results[subbench]:
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bench_name = "{}.{}-{}".format(
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bench.algorithm_name(), subbench, point
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)
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bench_name = bench_name.replace(" ", "___")
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bench_name = "".join(
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c if c.isalnum() else "_" for c in bench_name
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)
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elapsed_time = results[subbench][point]
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if elapsed_time_looks_good(elapsed_time):
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print(
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"&&&& PERF {} {} -sec".format(bench_name, elapsed_time)
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)
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else:
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failure_occured = True
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print("&&&& FAILED {}".format(algname))
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if failure_occured:
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sys.exit(1)
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def main():
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print("&&&& RUNNING bench")
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os.environ["CUDA_MODULE_LOADING"] = "EAGER"
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cccl.bench.search(BaseRunner())
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print("&&&& PASSED bench")
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if __name__ == "__main__":
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main()
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