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
105 lines
3.2 KiB
Plaintext
105 lines
3.2 KiB
Plaintext
#include <thrust/iterator/counting_iterator.h>
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#include <thrust/iterator/iterator_traits.h>
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#include <thrust/iterator/retag.h>
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#include <thrust/transform_reduce.h>
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#include <unittest/unittest.h>
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template <typename InputIterator, typename UnaryFunction, typename OutputType, typename BinaryFunction>
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OutputType
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transform_reduce(my_system& system, InputIterator, InputIterator, UnaryFunction, OutputType init, BinaryFunction)
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{
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system.validate_dispatch();
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return init;
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}
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void TestTransformReduceDispatchExplicit()
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{
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thrust::device_vector<int> vec(1);
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my_system sys(0);
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thrust::transform_reduce(sys, vec.begin(), vec.begin(), 0, 0, 0);
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ASSERT_EQUAL(true, sys.is_valid());
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}
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DECLARE_UNITTEST(TestTransformReduceDispatchExplicit);
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template <typename InputIterator, typename UnaryFunction, typename OutputType, typename BinaryFunction>
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OutputType transform_reduce(my_tag, InputIterator first, InputIterator, UnaryFunction, OutputType init, BinaryFunction)
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{
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*first = 13;
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return init;
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}
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void TestTransformReduceDispatchImplicit()
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{
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thrust::device_vector<int> vec(1);
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thrust::transform_reduce(thrust::retag<my_tag>(vec.begin()), thrust::retag<my_tag>(vec.begin()), 0, 0, 0);
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ASSERT_EQUAL(13, vec.front());
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}
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DECLARE_UNITTEST(TestTransformReduceDispatchImplicit);
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template <class Vector>
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void TestTransformReduceSimple()
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{
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using T = typename Vector::value_type;
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Vector data{1, -2, 3};
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T init = 10;
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T result = thrust::transform_reduce(data.begin(), data.end(), ::cuda::std::negate<T>(), init, ::cuda::std::plus<T>());
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ASSERT_EQUAL(result, 8);
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}
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DECLARE_VECTOR_UNITTEST(TestTransformReduceSimple);
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template <typename T>
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void TestTransformReduce(const size_t n)
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{
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thrust::host_vector<T> h_data = unittest::random_integers<T>(n);
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thrust::device_vector<T> d_data = h_data;
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T init = 13;
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T cpu_result =
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thrust::transform_reduce(h_data.begin(), h_data.end(), ::cuda::std::negate<T>(), init, ::cuda::std::plus<T>());
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T gpu_result =
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thrust::transform_reduce(d_data.begin(), d_data.end(), ::cuda::std::negate<T>(), init, ::cuda::std::plus<T>());
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ASSERT_ALMOST_EQUAL(cpu_result, gpu_result);
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}
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DECLARE_VARIABLE_UNITTEST(TestTransformReduce);
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template <typename T>
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void TestTransformReduceFromConst(const size_t n)
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{
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thrust::host_vector<T> h_data = unittest::random_integers<T>(n);
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thrust::device_vector<T> d_data = h_data;
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T init = 13;
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T cpu_result =
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thrust::transform_reduce(h_data.cbegin(), h_data.cend(), ::cuda::std::negate<T>(), init, ::cuda::std::plus<T>());
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T gpu_result =
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thrust::transform_reduce(d_data.cbegin(), d_data.cend(), ::cuda::std::negate<T>(), init, ::cuda::std::plus<T>());
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ASSERT_ALMOST_EQUAL(cpu_result, gpu_result);
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}
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DECLARE_VARIABLE_UNITTEST(TestTransformReduceFromConst);
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template <class Vector>
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void TestTransformReduceCountingIterator()
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{
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using T = typename Vector::value_type;
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using space = typename thrust::iterator_system<typename Vector::iterator>::type;
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thrust::counting_iterator<T, space> first(1);
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T result = thrust::transform_reduce(first, first + 3, ::cuda::std::negate<short>(), 0, ::cuda::std::plus<short>());
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ASSERT_EQUAL(result, -6);
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}
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DECLARE_INTEGRAL_VECTOR_UNITTEST(TestTransformReduceCountingIterator);
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