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
94 lines
2.6 KiB
Plaintext
94 lines
2.6 KiB
Plaintext
#include <thrust/functional.h>
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#include <thrust/system/detail/internal/decompose.h>
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#include <thrust/system/omp/detail/reduce_intervals.h>
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#include <unittest/unittest.h>
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// CPP reference implementation
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template <typename InputIterator, typename OutputIterator, typename BinaryFunction, typename Decomposition>
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void reduce_intervals(InputIterator input, OutputIterator output, BinaryFunction binary_op, Decomposition decomp)
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{
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using OutputType = thrust::detail::it_value_t<OutputIterator>;
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using index_type = typename Decomposition::index_type;
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// wrap binary_op
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thrust::detail::wrapped_function<BinaryFunction, OutputType> wrapped_binary_op{binary_op};
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for (index_type i = 0; i < decomp.size(); ++i, ++output)
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{
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InputIterator begin = input + decomp[i].begin();
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InputIterator end = input + decomp[i].end();
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if (begin != end)
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{
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OutputType sum = *begin;
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++begin;
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while (begin != end)
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{
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sum = wrapped_binary_op(sum, *begin);
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++begin;
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}
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*output = sum;
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}
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}
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}
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void TestOmpReduceIntervalsSimple()
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{
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using T = int;
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using Vector = thrust::device_vector<T>;
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using thrust::system::detail::internal::uniform_decomposition;
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using thrust::system::omp::detail::reduce_intervals;
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Vector input(10, 1);
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thrust::omp::tag omp_tag;
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{
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uniform_decomposition<int> decomp(10, 10, 1);
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Vector output(decomp.size());
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reduce_intervals(omp_tag, input.begin(), output.begin(), ::cuda::std::plus<T>(), decomp);
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ASSERT_EQUAL(output[0], 10);
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}
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{
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uniform_decomposition<int> decomp(10, 6, 2);
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Vector output(decomp.size());
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reduce_intervals(omp_tag, input.begin(), output.begin(), ::cuda::std::plus<T>(), decomp);
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ASSERT_EQUAL(output[0], 6);
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ASSERT_EQUAL(output[1], 4);
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}
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}
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DECLARE_UNITTEST(TestOmpReduceIntervalsSimple);
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template <typename T>
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struct TestOmpReduceIntervals
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{
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void operator()(const size_t n)
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{
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using thrust::system::detail::internal::uniform_decomposition;
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using thrust::system::omp::detail::reduce_intervals;
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thrust::host_vector<T> h_input = unittest::random_integers<T>(n);
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thrust::device_vector<T> d_input = h_input;
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uniform_decomposition<size_t> decomp(n, 7, 100);
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thrust::host_vector<T> h_output(decomp.size());
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thrust::device_vector<T> d_output(decomp.size());
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::reduce_intervals(h_input.begin(), h_output.begin(), ::cuda::std::plus<T>(), decomp);
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thrust::system::omp::tag omp_tag;
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reduce_intervals(omp_tag, d_input.begin(), d_output.begin(), ::cuda::std::plus<T>(), decomp);
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ASSERT_EQUAL(h_output, d_output);
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}
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};
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VariableUnitTest<TestOmpReduceIntervals, IntegralTypes> TestOmpReduceIntervalsInstance;
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