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b5e4b4ba31
Per PR review suggestion: - `cuda.parallel` - device-wide algorithms/Thrust - `cuda.cooperative` - Cooperative algorithsm/CUB - `cuda.bench` - Benchmarking/NVBench
64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
# Copyright 2025 NVIDIA Corporation
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#
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# Licensed under the Apache License, Version 2.0 with the LLVM exception
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# (the "License"); you may not use this file except in compliance with
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# the License.
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#
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# You may obtain a copy of the License at
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#
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# http://llvm.org/foundation/relicensing/LICENSE.txt
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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import cuda.bench as bench
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import cupy as cp
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def as_cp_ExternalStream(
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cs: bench.CudaStream, dev_id: int | None = -1
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) -> cp.cuda.ExternalStream:
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h = cs.addressof()
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return cp.cuda.ExternalStream(h, dev_id)
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def cupy_extract_by_mask(state: bench.State):
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n_cols = state.get_int64("numCols")
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n_rows = state.get_int64("numRows")
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dev_id = state.get_device()
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cp_s = as_cp_ExternalStream(state.get_stream(), dev_id)
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state.collect_cupti_metrics()
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state.add_element_count(n_rows * n_cols, "# Elements")
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int32_dt = cp.dtype(cp.int32)
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bool_dt = cp.dtype(cp.bool_)
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state.add_global_memory_reads(
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n_rows * n_cols * (int32_dt.itemsize + bool_dt.itemsize)
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)
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state.add_global_memory_writes(n_rows * n_cols * (int32_dt.itemsize))
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with cp_s:
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X = cp.full((n_cols, n_rows), fill_value=3, dtype=int32_dt)
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mask = cp.ones((n_cols, n_rows), dtype=bool_dt)
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_ = X[mask]
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def launcher(launch: bench.Launch):
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with as_cp_ExternalStream(launch.get_stream(), dev_id):
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_ = X[mask]
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state.exec(launcher, sync=True)
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if __name__ == "__main__":
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b = bench.register(cupy_extract_by_mask)
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b.add_int64_axis("numCols", [1024, 2048, 4096, 2 * 4096])
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b.add_int64_axis("numRows", [1024, 2048, 4096, 2 * 4096])
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bench.run_all_benchmarks(sys.argv)
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