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
156 lines
4.2 KiB
Plaintext
156 lines
4.2 KiB
Plaintext
#include <thrust/device_free.h>
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#include <thrust/device_malloc.h>
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#include <thrust/device_vector.h>
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#include <thrust/functional.h>
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#include <thrust/inner_product.h>
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#include <thrust/iterator/retag.h>
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#include <unittest/unittest.h>
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template <class Vector>
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void TestInnerProductSimple()
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{
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using T = typename Vector::value_type;
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Vector v1{1, -2, 3};
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Vector v2{-4, 5, 6};
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T init = 3;
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T result = thrust::inner_product(v1.begin(), v1.end(), v2.begin(), init);
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ASSERT_EQUAL(result, 7);
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}
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DECLARE_VECTOR_UNITTEST(TestInnerProductSimple);
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template <typename InputIterator1, typename InputIterator2, typename OutputType>
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int inner_product(my_system& system, InputIterator1, InputIterator1, InputIterator2, OutputType)
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{
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system.validate_dispatch();
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return 13;
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}
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void TestInnerProductDispatchExplicit()
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{
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thrust::device_vector<int> vec;
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my_system sys(0);
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thrust::inner_product(sys, vec.begin(), vec.end(), vec.begin(), 0);
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ASSERT_EQUAL(true, sys.is_valid());
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}
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DECLARE_UNITTEST(TestInnerProductDispatchExplicit);
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template <typename InputIterator1, typename InputIterator2, typename OutputType>
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int inner_product(my_tag, InputIterator1, InputIterator1, InputIterator2, OutputType)
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{
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return 13;
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}
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void TestInnerProductDispatchImplicit()
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{
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thrust::device_vector<int> vec;
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int result = thrust::inner_product(
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thrust::retag<my_tag>(vec.begin()), thrust::retag<my_tag>(vec.end()), thrust::retag<my_tag>(vec.begin()), 0);
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ASSERT_EQUAL(13, result);
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}
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DECLARE_UNITTEST(TestInnerProductDispatchImplicit);
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template <class Vector>
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void TestInnerProductWithOperator()
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{
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using T = typename Vector::value_type;
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Vector v1{1, -2, 3};
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Vector v2{-1, 3, 6};
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// compute (v1 - v2) and perform a multiplies reduction
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T init = 3;
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T result = thrust::inner_product(
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v1.begin(), v1.end(), v2.begin(), init, ::cuda::std::multiplies<T>(), ::cuda::std::minus<T>());
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ASSERT_EQUAL(result, 90);
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}
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DECLARE_VECTOR_UNITTEST(TestInnerProductWithOperator);
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template <typename T>
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struct TestInnerProduct
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{
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void operator()(const size_t n)
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{
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thrust::host_vector<T> h_v1 = unittest::random_integers<T>(n);
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thrust::host_vector<T> h_v2 = unittest::random_integers<T>(n);
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thrust::device_vector<T> d_v1 = h_v1;
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thrust::device_vector<T> d_v2 = h_v2;
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T init = 13;
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T expected = thrust::inner_product(h_v1.begin(), h_v1.end(), h_v2.begin(), init);
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T result = thrust::inner_product(d_v1.begin(), d_v1.end(), d_v2.begin(), init);
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ASSERT_EQUAL(expected, result);
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}
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};
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VariableUnitTest<TestInnerProduct, IntegralTypes> TestInnerProductInstance;
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struct only_set_when_both_expected
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{
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long long expected;
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bool* flag;
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_CCCL_DEVICE long long operator()(long long x, long long y)
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{
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if (x == expected && y == expected)
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{
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*flag = true;
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}
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return x == y;
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}
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};
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void TestInnerProductWithBigIndexesHelper(int magnitude)
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{
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thrust::counting_iterator<long long> begin(1);
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thrust::counting_iterator<long long> end = begin + (1ll << magnitude);
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ASSERT_EQUAL(::cuda::std::distance(begin, end), 1ll << magnitude);
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thrust::device_ptr<bool> has_executed = thrust::device_malloc<bool>(1);
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*has_executed = false;
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only_set_when_both_expected fn = {(1ll << magnitude) - 1, thrust::raw_pointer_cast(has_executed)};
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ASSERT_EQUAL(thrust::inner_product(thrust::device, begin, end, begin, 0ll, ::cuda::std::plus<long long>(), fn),
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(1ll << magnitude));
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bool has_executed_h = *has_executed;
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thrust::device_free(has_executed);
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ASSERT_EQUAL(has_executed_h, true);
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}
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void TestInnerProductWithBigIndexes()
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{
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TestInnerProductWithBigIndexesHelper(30);
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#ifndef THRUST_FORCE_32_BIT_OFFSET_TYPE
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TestInnerProductWithBigIndexesHelper(31);
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TestInnerProductWithBigIndexesHelper(32);
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TestInnerProductWithBigIndexesHelper(33);
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#endif
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}
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DECLARE_UNITTEST(TestInnerProductWithBigIndexes);
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void TestInnerProductPlaceholders()
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{ // Regression test for NVIDIA/thrust#1178
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using namespace thrust::placeholders;
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thrust::device_vector<float> v1(100, 1.f);
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thrust::device_vector<float> v2(100, 1.f);
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auto result =
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thrust::inner_product(v1.begin(), v1.end(), v2.begin(), 0.0f, ::cuda::std::plus<float>{}, _1 * _2 + 1.0f);
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ASSERT_ALMOST_EQUAL(result, 200.f);
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}
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DECLARE_UNITTEST(TestInnerProductPlaceholders);
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