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https://github.com/NVIDIA/nvbench.git
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Add stream benchmark example
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@@ -60,6 +60,7 @@ various NVBench features and usecases:
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- [Enums and compile-time-constant-integral parameter axes](examples/enums.cu)
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- [Enums and compile-time-constant-integral parameter axes](examples/enums.cu)
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- [Reporting item/sec and byte/sec throughput statistics](examples/throughput.cu)
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- [Reporting item/sec and byte/sec throughput statistics](examples/throughput.cu)
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- [Skipping benchmark configurations](examples/skip.cu)
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- [Skipping benchmark configurations](examples/skip.cu)
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- [Benchmarking on a specific stream](examples/stream.cu)
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- [Benchmarks that sync CUDA devices: `nvbench::exec_tag::sync`](examples/exec_tag_sync.cu)
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- [Benchmarks that sync CUDA devices: `nvbench::exec_tag::sync`](examples/exec_tag_sync.cu)
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- [Manual timing: `nvbench::exec_tag::timer`](examples/exec_tag_timer.cu)
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- [Manual timing: `nvbench::exec_tag::timer`](examples/exec_tag_timer.cu)
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@@ -4,6 +4,7 @@ set(example_srcs
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exec_tag_sync.cu
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exec_tag_sync.cu
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exec_tag_timer.cu
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exec_tag_timer.cu
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skip.cu
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skip.cu
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stream.cu
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throughput.cu
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throughput.cu
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auto_throughput.cu
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auto_throughput.cu
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)
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)
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62
examples/stream.cu
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62
examples/stream.cu
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@@ -0,0 +1,62 @@
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/*
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* Copyright 2022 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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*/
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#include <nvbench/nvbench.cuh>
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// Grab some testing kernels from NVBench:
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#include <nvbench/test_kernels.cuh>
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// Thrust vectors simplify memory management:
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#include <thrust/device_vector.h>
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// A function to benchmark but does not expose an explicit stream argument.
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void copy(int32_t *input, int32_t *output, std::size_t const num_values)
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{
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nvbench::copy_kernel<<<256, 256>>>(input, output, num_values);
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}
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// `stream_bench` copies a 64 MiB buffer of int32_t on a CUDA stream specified
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// by the user.
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//
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// By default, NVBench creates and provides an explicit stream via
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// `launch::get_stream()` to pass to every stream-ordered operation. Sometimes
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// it is inconvenient or impossible to specify an explicit CUDA stream to every
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// stream-ordered operation. In this case, users may specify a target stream via
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// `state::set_cuda_stream`. It is assumed that all work of interest executes on
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// or synchronizes with this stream.
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void stream_bench(nvbench::state &state)
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{
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// Allocate input data:
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const std::size_t num_values = 64 * 1024 * 1024 / sizeof(nvbench::int32_t);
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thrust::device_vector<nvbench::int32_t> input(num_values);
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thrust::device_vector<nvbench::int32_t> output(num_values);
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// Set CUDA default stream as the target stream. Note the default stream
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// is non-owning.
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cudaStream_t default_stream = 0;
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state.set_cuda_stream(
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nvbench::cuda_stream{default_stream, false /*owning = false*/});
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state.exec([&input, &output, num_values](nvbench::launch &) {
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copy(thrust::raw_pointer_cast(input.data()),
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thrust::raw_pointer_cast(output.data()),
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num_values);
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});
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}
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NVBENCH_BENCH(stream_bench);
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