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
110 lines
3.2 KiB
Plaintext
110 lines
3.2 KiB
Plaintext
#include <thrust/functional.h>
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#include <thrust/transform.h>
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#include <unittest/unittest.h>
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template <typename T>
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struct saxpy_reference
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{
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_CCCL_HOST_DEVICE saxpy_reference(const T& aa)
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: a(aa)
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{}
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_CCCL_HOST_DEVICE T operator()(const T& x, const T& y) const
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{
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return a * x + y;
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}
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T a;
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};
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template <typename Vector>
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struct TestFunctionalPlaceholdersValue
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{
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void operator()(const size_t)
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{
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const size_t n = 10000;
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using T = typename Vector::value_type;
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T a(13);
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Vector x = unittest::random_integers<T>(n);
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Vector y = unittest::random_integers<T>(n);
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Vector result(n), reference(n);
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thrust::transform(x.begin(), x.end(), y.begin(), reference.begin(), saxpy_reference<T>(a));
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using namespace thrust::placeholders;
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thrust::transform(x.begin(), x.end(), y.begin(), result.begin(), a * _1 + _2);
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ASSERT_ALMOST_EQUAL(reference, result);
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}
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};
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VectorUnitTest<TestFunctionalPlaceholdersValue, ThirtyTwoBitTypes, thrust::device_vector, thrust::device_allocator>
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TestFunctionalPlaceholdersValueDevice;
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VectorUnitTest<TestFunctionalPlaceholdersValue, ThirtyTwoBitTypes, thrust::host_vector, std::allocator>
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TestFunctionalPlaceholdersValueHost;
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template <typename Vector>
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struct TestFunctionalPlaceholdersTransformIterator
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{
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void operator()(const size_t)
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{
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const size_t n = 10000;
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using T = typename Vector::value_type;
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T a(13);
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Vector x = unittest::random_integers<T>(n);
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Vector y = unittest::random_integers<T>(n);
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Vector result(n), reference(n);
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thrust::transform(x.begin(), x.end(), y.begin(), reference.begin(), saxpy_reference<T>(a));
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using namespace thrust::placeholders;
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thrust::transform(
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thrust::make_transform_iterator(x.begin(), a * _1),
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thrust::make_transform_iterator(x.end(), a * _1),
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y.begin(),
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result.begin(),
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_1 + _2);
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ASSERT_ALMOST_EQUAL(reference, result);
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}
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};
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VectorUnitTest<TestFunctionalPlaceholdersTransformIterator,
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ThirtyTwoBitTypes,
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thrust::device_vector,
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thrust::device_allocator>
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TestFunctionalPlaceholdersTransformIteratorInstanceDevice;
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VectorUnitTest<TestFunctionalPlaceholdersTransformIterator, ThirtyTwoBitTypes, thrust::host_vector, std::allocator>
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TestFunctionalPlaceholdersTransformIteratorInstanceHost;
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void TestFunctionalPlaceholdersArgumentValueCategories()
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{
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using namespace thrust::placeholders;
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auto expr = _1 * _1 + _2 * _2;
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int a = 2;
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int b = 3;
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ASSERT_EQUAL(expr(2, 3), 13); // pass pr-value
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ASSERT_EQUAL(expr(a, b), 13); // pass l-value
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ASSERT_EQUAL(expr(::cuda::std::move(a), ::cuda::std::move(b)), 13); // pass x-value
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}
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DECLARE_UNITTEST(TestFunctionalPlaceholdersArgumentValueCategories);
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void TestFunctionalPlaceholdersSemiRegular()
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{
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using namespace thrust::placeholders;
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using Expr = decltype(_1 * _1 + _2 * _2);
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Expr expr; // default-constructible
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ASSERT_EQUAL(expr(2, 3), 13);
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Expr expr2 = expr; // copy-constructible
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ASSERT_EQUAL(expr2(2, 3), 13);
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Expr expr3;
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expr3 = expr; // copy-assignable
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ASSERT_EQUAL(expr3(2, 3), 13);
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static_assert(::cuda::std::semiregular<Expr>);
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}
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DECLARE_UNITTEST(TestFunctionalPlaceholdersSemiRegular);
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