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
93 lines
3.0 KiB
Plaintext
93 lines
3.0 KiB
Plaintext
#include <thrust/execution_policy.h>
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#include <thrust/extrema.h>
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#include <thrust/merge.h>
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#include <thrust/sort.h>
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#include <cuda/buffer>
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#include <cuda/cccl_runtime_test_helper.cuh>
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#include <cuda/launch>
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#include <cuda/stream>
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#include <unittest/unittest.h>
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#ifdef THRUST_TEST_DEVICE_SIDE
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struct merge_kernel
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{
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template <typename ExecutionPolicy, typename Input1, typename Input2, typename Size, typename Output>
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__device__ void operator()(ExecutionPolicy exec, Input1 a, Input2 b, Size b_size, Output result) const
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{
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const auto end = thrust::merge(exec, a.begin(), a.end(), b.begin(), b.begin() + b_size, result.begin());
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TEST_ASSERT_DEVICE(end == result.end());
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}
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};
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template <typename ExecutionPolicy>
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void TestMergeDevice(ExecutionPolicy exec)
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{
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const auto device = test_runtime::current_test_device();
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cuda::stream stream{device};
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const size_t n = 10000;
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const size_t sizes[] = {0, 1, n / 2, n, n + 1, 2 * n};
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const size_t num_sizes = sizeof(sizes) / sizeof(size_t);
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const auto max_size = static_cast<size_t>(*thrust::max_element(sizes, sizes + num_sizes));
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auto h_a = test_runtime::random_integers_buffer<int, unittest::int8_t>(stream, n);
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auto h_b = test_runtime::random_integers_buffer<int, unittest::int8_t>(stream, max_size, n);
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thrust::stable_sort(h_a.begin(), h_a.end());
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thrust::stable_sort(h_b.begin(), h_b.end());
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auto d_a = cuda::make_device_buffer<int>(stream, device, h_a);
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auto d_b = cuda::make_device_buffer<int>(stream, device, h_b);
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for (size_t i = 0; i < num_sizes; i++)
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{
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const size_t size = sizes[i];
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auto h_result = test_runtime::make_host_buffer<int>(stream, n + size);
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stream.sync();
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const auto h_end = thrust::merge(h_a.begin(), h_a.end(), h_b.begin(), h_b.begin() + size, h_result.begin());
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ASSERT_EQUAL_QUIET(h_result.end(), h_end);
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auto result = cuda::make_device_buffer<int>(stream, device, h_result.size(), cuda::no_init);
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cuda::launch(stream, test_runtime::single_thread_config(), merge_kernel{}, exec, d_a, d_b, size, result);
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stream.sync();
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test_runtime::assert_equal(stream, result, h_result);
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}
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}
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void TestMergeDeviceSeq()
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{
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TestMergeDevice(thrust::seq);
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}
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DECLARE_UNITTEST(TestMergeDeviceSeq);
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void TestMergeDeviceDevice()
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{
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TestMergeDevice(thrust::device);
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}
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DECLARE_UNITTEST(TestMergeDeviceDevice);
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#endif
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void TestMergeCudaStreams()
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{
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const auto device = test_runtime::current_test_device();
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cuda::stream stream{device};
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auto a = cuda::make_device_buffer<int>(stream, device, {0, 2, 4});
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auto b = cuda::make_device_buffer<int>(stream, device, {0, 3, 3, 4});
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auto result = cuda::make_device_buffer<int>(stream, device, 7, cuda::no_init);
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const auto end =
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thrust::merge(thrust::cuda::par.on(stream.get()), a.begin(), a.end(), b.begin(), b.end(), result.begin());
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stream.sync();
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ASSERT_EQUAL_QUIET(result.end(), end);
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test_runtime::assert_equal(stream, result, {0, 0, 2, 3, 3, 4, 4});
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}
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DECLARE_UNITTEST(TestMergeCudaStreams);
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