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b5e4b4ba31
Per PR review suggestion: - `cuda.parallel` - device-wide algorithms/Thrust - `cuda.cooperative` - Cooperative algorithsm/CUB - `cuda.bench` - Benchmarking/NVBench
98 lines
2.8 KiB
Python
98 lines
2.8 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 time
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import cuda.bench as bench
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import cuda.cccl.headers as headers
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import cuda.core.experimental as core
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host_sleep_duration = 0.1
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def cpu_only_sleep_bench(state: bench.State) -> None:
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def launcher(launch: bench.Launch):
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time.sleep(host_sleep_duration)
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state.exec(launcher)
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def as_core_Stream(cs: bench.CudaStream) -> core.Stream:
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return core.Stream.from_handle(cs.addressof())
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def make_sleep_kernel():
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"""JITs sleep_kernel(seconds)"""
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src = r"""
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#include <cuda/std/cstdint>
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#include <cuda/std/chrono>
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// Each launched thread just sleeps for `seconds`.
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__global__ void sleep_kernel(double seconds) {
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namespace chrono = ::cuda::std::chrono;
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using hr_clock = chrono::high_resolution_clock;
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auto duration = static_cast<cuda::std::int64_t>(seconds * 1e9);
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const auto ns = chrono::nanoseconds(duration);
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const auto start = hr_clock::now();
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const auto finish = start + ns;
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auto now = hr_clock::now();
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while (now < finish)
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{
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now = hr_clock::now();
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}
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}
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"""
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incl = headers.get_include_paths()
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opts = core.ProgramOptions(include_path=str(incl.libcudacxx))
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prog = core.Program(src, code_type="c++", options=opts)
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mod = prog.compile("cubin", name_expressions=("sleep_kernel",))
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return mod.get_kernel("sleep_kernel")
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def mixed_sleep_bench(state: bench.State) -> None:
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sync = state.get_string("Sync")
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sync_flag = sync == "Do sync"
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gpu_sleep_dur = 225e-3
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krn = make_sleep_kernel()
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launch_config = core.LaunchConfig(grid=1, block=1, shmem_size=0)
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def launcher(launch: bench.Launch):
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# host overhead
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time.sleep(host_sleep_duration)
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# GPU computation
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s = as_core_Stream(launch.get_stream())
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core.launch(s, launch_config, krn, gpu_sleep_dur)
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state.exec(launcher, sync=sync_flag)
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if __name__ == "__main__":
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# time function only doing work (sleeping) on the host
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# using CPU timer only
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b = bench.register(cpu_only_sleep_bench)
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b.set_is_cpu_only(True)
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# time the function that does work on both GPU and CPU
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b2 = bench.register(mixed_sleep_bench)
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b2.add_string_axis("Sync", ["Do not sync", "Do sync"])
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bench.run_all_benchmarks(sys.argv)
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