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
119 lines
2.9 KiB
Plaintext
119 lines
2.9 KiB
Plaintext
#include <thrust/execution_policy.h>
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#include <thrust/reduce.h>
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#include <cuda/iterator>
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#include <unittest/unittest.h>
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template <typename ExecutionPolicy, typename Iterator, typename T, typename Iterator2>
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__global__ void reduce_kernel(ExecutionPolicy exec, Iterator first, Iterator last, T init, Iterator2 result)
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{
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*result = thrust::reduce(exec, first, last, init);
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}
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#ifdef THRUST_TEST_DEVICE_SIDE
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template <typename T, typename ExecutionPolicy>
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void TestReduceDevice(ExecutionPolicy exec, const size_t n)
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{
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thrust::host_vector<T> h_data = unittest::random_integers<T>(n);
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thrust::device_vector<T> d_data = h_data;
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thrust::device_vector<T> d_result(1);
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T init = 13;
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T h_result = thrust::reduce(h_data.begin(), h_data.end(), init);
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reduce_kernel<<<1, 1>>>(exec, d_data.begin(), d_data.end(), init, d_result.begin());
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cudaError_t const err = cudaDeviceSynchronize();
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ASSERT_EQUAL(cudaSuccess, err);
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ASSERT_EQUAL(h_result, d_result[0]);
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}
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template <typename T>
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struct TestReduceDeviceSeq
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{
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void operator()(const size_t n)
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{
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TestReduceDevice<T>(thrust::seq, n);
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}
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};
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VariableUnitTest<TestReduceDeviceSeq, IntegralTypes> TestReduceDeviceSeqInstance;
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template <typename T>
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struct TestReduceDeviceDevice
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{
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void operator()(const size_t n)
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{
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TestReduceDevice<T>(thrust::device, n);
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}
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};
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VariableUnitTest<TestReduceDeviceDevice, IntegralTypes> TestReduceDeviceDeviceInstance;
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template <typename T>
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struct TestReduceDeviceNoSync
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{
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void operator()(const size_t n)
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{
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TestReduceDevice<T>(thrust::cuda::par_nosync, n);
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}
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};
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VariableUnitTest<TestReduceDeviceNoSync, IntegralTypes> TestReduceDeviceNoSyncInstance;
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#endif
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template <typename ExecutionPolicy>
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void TestReduceCudaStreams(ExecutionPolicy policy)
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{
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using Vector = thrust::device_vector<int>;
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Vector v(3);
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v[0] = 1;
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v[1] = -2;
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v[2] = 3;
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cudaStream_t s;
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cudaStreamCreate(&s);
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auto streampolicy = policy.on(s);
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// no initializer
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ASSERT_EQUAL(thrust::reduce(streampolicy, v.begin(), v.end()), 2);
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// with initializer
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ASSERT_EQUAL(thrust::reduce(streampolicy, v.begin(), v.end(), 10), 12);
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cudaStreamDestroy(s);
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}
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void TestReduceCudaStreamsSync()
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{
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TestReduceCudaStreams(thrust::cuda::par);
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}
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DECLARE_UNITTEST(TestReduceCudaStreamsSync);
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void TestReduceCudaStreamsNoSync()
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{
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TestReduceCudaStreams(thrust::cuda::par_nosync);
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}
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DECLARE_UNITTEST(TestReduceCudaStreamsNoSync);
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#if defined(THRUST_RDC_ENABLED)
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void TestReduceLargeInput()
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{
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using T = unsigned long long;
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using OffsetT = std::size_t;
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const OffsetT num_items = 1ull << 32;
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cuda::constant_iterator<T> d_data(T{1});
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thrust::device_vector<T> d_result(1);
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reduce_kernel<<<1, 1>>>(thrust::device, d_data, d_data + num_items, T{}, d_result.begin());
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cudaError_t const err = cudaDeviceSynchronize();
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ASSERT_EQUAL(cudaSuccess, err);
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ASSERT_EQUAL(num_items, d_result[0]);
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}
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DECLARE_UNITTEST(TestReduceLargeInput);
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#endif
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