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
118 lines
3.0 KiB
Plaintext
118 lines
3.0 KiB
Plaintext
#include <thrust/functional.h>
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#include <thrust/iterator/retag.h>
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#include <thrust/partition.h>
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#include <unittest/unittest.h>
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template <typename T>
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struct is_even
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{
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_CCCL_HOST_DEVICE bool operator()(T x) const
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{
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return ((int) x % 2) == 0;
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}
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};
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template <typename Vector>
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void TestPartitionPointSimple()
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{
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using Iterator = typename Vector::iterator;
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Vector v{1, 1, 1, 0};
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Iterator first = v.begin();
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Iterator last = v.begin() + 4;
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Iterator ref = first + 3;
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ASSERT_EQUAL_QUIET(ref, thrust::partition_point(first, last, ::cuda::std::identity{}));
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last = v.begin() + 3;
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ref = last;
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ASSERT_EQUAL_QUIET(ref, thrust::partition_point(first, last, ::cuda::std::identity{}));
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}
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DECLARE_VECTOR_UNITTEST(TestPartitionPointSimple);
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template <class Vector>
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void TestPartitionPoint()
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{
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using T = typename Vector::value_type;
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using Iterator = typename Vector::iterator;
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const size_t n = (1 << 16) + 13;
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Vector v = unittest::random_integers<T>(n);
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Iterator ref = thrust::stable_partition(v.begin(), v.end(), is_even<T>());
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ASSERT_EQUAL(ref - v.begin(), thrust::partition_point(v.begin(), v.end(), is_even<T>()) - v.begin());
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}
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DECLARE_INTEGRAL_VECTOR_UNITTEST(TestPartitionPoint);
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template <typename ForwardIterator, typename Predicate>
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ForwardIterator partition_point(my_system& system, ForwardIterator first, ForwardIterator, Predicate)
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{
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system.validate_dispatch();
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return first;
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}
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void TestPartitionPointDispatchExplicit()
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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::partition_point(sys, vec.begin(), vec.begin(), 0);
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ASSERT_EQUAL(true, sys.is_valid());
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}
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DECLARE_UNITTEST(TestPartitionPointDispatchExplicit);
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template <typename ForwardIterator, typename Predicate>
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ForwardIterator partition_point(my_tag, ForwardIterator first, ForwardIterator, Predicate)
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{
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*first = 13;
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return first;
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}
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void TestPartitionPointDispatchImplicit()
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{
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thrust::device_vector<int> vec(1);
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thrust::partition_point(thrust::retag<my_tag>(vec.begin()), thrust::retag<my_tag>(vec.begin()), 0);
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ASSERT_EQUAL(13, vec.front());
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}
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DECLARE_UNITTEST(TestPartitionPointDispatchImplicit);
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struct test_less_than
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{
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long long expected;
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_CCCL_DEVICE bool operator()(long long y)
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{
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return y < expected;
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}
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};
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void TestPartitionPointWithBigIndexesHelper(int magnitude)
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{
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thrust::counting_iterator<long long> begin(0);
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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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test_less_than fn = {(1ll << magnitude) - 17};
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ASSERT_EQUAL(::cuda::std::distance(begin, thrust::partition_point(thrust::device, begin, end, fn)),
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(1ll << magnitude) - 17);
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}
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#ifndef THRUST_FORCE_32_BIT_OFFSET_TYPE
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void TestPartitionPointWithBigIndexes()
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{
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TestPartitionPointWithBigIndexesHelper(30);
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TestPartitionPointWithBigIndexesHelper(31);
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TestPartitionPointWithBigIndexesHelper(32);
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TestPartitionPointWithBigIndexesHelper(33);
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}
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DECLARE_UNITTEST(TestPartitionPointWithBigIndexes);
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#endif // THRUST_FORCE_32_BIT_OFFSET_TYPE
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