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
179 lines
5.9 KiB
Plaintext
179 lines
5.9 KiB
Plaintext
#define CCCL_IGNORE_DEPRECATED_API
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#include <thrust/copy.h>
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#include <thrust/device_vector.h>
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#include <thrust/functional.h>
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#include <thrust/gather.h>
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#include <thrust/host_vector.h>
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#include <thrust/iterator/counting_iterator.h>
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#include <thrust/iterator/tabulate_output_iterator.h>
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#include <thrust/iterator/transform_iterator.h>
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#include <thrust/iterator/zip_iterator.h>
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#include <thrust/reduce.h>
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#include <thrust/sequence.h>
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#include <cuda/std/type_traits>
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#include <unittest/unittest.h>
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template <typename OutItT>
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struct host_write_op
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{
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OutItT out;
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template <typename IndexT, typename T>
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_CCCL_HOST void operator()(IndexT index, T val)
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{
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out[index] = val;
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}
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};
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template <typename OutItT>
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struct host_write_first_op
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{
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OutItT out;
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template <typename IndexT, typename T>
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_CCCL_HOST void operator()(IndexT index, T val)
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{
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// val is a cuda::std::tuple(value, input_index). Only write out the value part.
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out[index] = cuda::std::get<0>(val);
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}
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};
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template <typename OutItT>
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struct device_write_first_op
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{
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OutItT out;
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template <typename IndexT, typename T>
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_CCCL_DEVICE void operator()(IndexT index, T val)
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{
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// val is a cuda::std::tuple(value, input_index). Only write out the value part.
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out[index] = cuda::std::get<0>(val);
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}
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};
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struct select_op
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{
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std::size_t select_every_nth;
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template <typename T, typename IndexT>
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_CCCL_HOST_DEVICE bool operator()(cuda::std::tuple<T, IndexT> key_index_pair)
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{
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// Select every n-th item
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return (cuda::std::get<1>(key_index_pair) % select_every_nth == 0);
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}
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};
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struct index_to_gather_index_op
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{
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std::size_t gather_stride;
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template <typename IndexT>
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_CCCL_HOST_DEVICE IndexT operator()(IndexT index)
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{
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// Gather the i-th output item from input[i*3]
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return index * static_cast<IndexT>(gather_stride);
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}
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};
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// ensure that we properly support thrust::tabulate_output_iterator from cuda::std
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void TestTabulateOutputIteratorTraits()
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{
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using base_it = thrust::host_vector<int>::iterator;
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using Op = host_write_op<base_it>;
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using it = thrust::tabulate_output_iterator<Op>;
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using traits = cuda::std::iterator_traits<it>;
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using category = thrust::detail::iterator_category_with_system_and_traversal<::cuda::std::random_access_iterator_tag,
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thrust::any_system_tag,
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thrust::random_access_traversal_tag>;
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static_assert(cuda::std::is_same_v<traits::difference_type, ptrdiff_t>);
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static_assert(cuda::std::is_same_v<traits::value_type, void>);
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static_assert(cuda::std::is_same_v<traits::pointer, void>);
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static_assert(cuda::std::is_same_v<traits::reference, thrust::detail::tabulate_output_iterator_proxy<Op, ptrdiff_t>>);
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static_assert(cuda::std::is_same_v<traits::iterator_category, category>);
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static_assert(cuda::std::is_same_v<thrust::iterator_traversal_t<it>, thrust::random_access_traversal_tag>);
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static_assert(cuda::std::__has_random_access_traversal<it>);
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// FIXME(bgruber): all up to and including random access should be true
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static_assert(!cuda::std::output_iterator<it, int>);
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static_assert(!cuda::std::input_iterator<it>);
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static_assert(!cuda::std::forward_iterator<it>);
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static_assert(!cuda::std::bidirectional_iterator<it>);
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static_assert(!cuda::std::random_access_iterator<it>);
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static_assert(!cuda::std::contiguous_iterator<it>);
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}
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DECLARE_UNITTEST(TestTabulateOutputIteratorTraits);
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template <class Vector>
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void TestTabulateOutputIterator()
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{
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using T = typename Vector::value_type;
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using it_t = typename Vector::iterator;
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using space = typename thrust::iterator_system<typename Vector::iterator>::type;
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static constexpr std::size_t num_items = 240;
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Vector input(num_items);
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Vector output(num_items, T{42});
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// Use operator type that supports the targeted system
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using op_t = typename ::cuda::std::conditional<(::cuda::std::is_same<space, thrust::host_system_tag>::value),
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host_write_first_op<it_t>,
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device_write_first_op<it_t>>::type;
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// Construct tabulate_output_iterator
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op_t op{output.begin()};
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auto tabulate_out_it = thrust::make_tabulate_output_iterator(op);
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// Prepare input
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thrust::sequence(input.begin(), input.end(), 1);
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auto iota_it = thrust::make_counting_iterator(0);
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auto zipped_in = thrust::make_zip_iterator(input.begin(), iota_it);
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// Run copy_if using tabulate_output_iterator as the output iterator
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static constexpr std::size_t select_every_nth = 3;
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auto selected_it_end =
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thrust::copy_if(zipped_in, zipped_in + num_items, tabulate_out_it, select_op{select_every_nth});
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const auto num_selected = static_cast<std::size_t>(::cuda::std::distance(tabulate_out_it, selected_it_end));
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// Prepare expected data
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Vector expected_output(num_items, T{42});
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const std::size_t expected_num_selected = (num_items + select_every_nth - 1) / select_every_nth;
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auto gather_index_it =
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thrust::make_transform_iterator(thrust::make_counting_iterator(0), index_to_gather_index_op{select_every_nth});
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thrust::gather(gather_index_it, gather_index_it + expected_num_selected, input.cbegin(), expected_output.begin());
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ASSERT_EQUAL(expected_num_selected, num_selected);
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ASSERT_EQUAL(output, expected_output);
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}
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DECLARE_VECTOR_UNITTEST(TestTabulateOutputIterator);
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void TestTabulateOutputIterator()
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{
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using vector_t = thrust::host_vector<int>;
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using vec_it_t = typename vector_t::iterator;
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using op_t = host_write_op<vec_it_t>;
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vector_t out(4, 42);
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thrust::tabulate_output_iterator<op_t> tabulate_out_it{op_t{out.begin()}};
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tabulate_out_it[1] = 2;
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vector_t ref{42, 2, 42, 42};
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ASSERT_EQUAL(out, ref);
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tabulate_out_it[3] = 0;
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ref = {42, 2, 42, 0};
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ASSERT_EQUAL(out, ref);
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tabulate_out_it[1] = 4;
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ref = {42, 4, 42, 0};
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ASSERT_EQUAL(out, ref);
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}
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DECLARE_UNITTEST(TestTabulateOutputIterator);
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