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
79 lines
2.1 KiB
Plaintext
79 lines
2.1 KiB
Plaintext
//===----------------------------------------------------------------------===//
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//
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// Part of CUDASTF in CUDA C++ Core Libraries,
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// under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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// SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
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//
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//===----------------------------------------------------------------------===//
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#include <cuda/experimental/__stf/stream/interfaces/slice_reduction_ops.cuh>
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#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
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using namespace cuda::experimental::stf;
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__global__ void add(int* ptr, int value)
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{
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*ptr = *ptr + value;
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}
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__global__ void check_val(const int* ptr, int expected)
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{
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assert(*ptr == expected);
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}
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/*
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* This test ensures that we can reconstruct a piece of data where there are
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* multiple "shared" instances, and "redux" instances
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*/
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int main()
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{
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stream_ctx ctx;
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int ndevs;
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cuda_safe_call(cudaGetDeviceCount(&ndevs));
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if (ndevs < 2)
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{
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fprintf(stderr, "Skipping test: need at least 2 devices.\n");
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return 0;
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}
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auto redux_op = std::make_shared<slice_reduction_op_sum<int>>();
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int a = 17;
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// init op (17)
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auto handle = ctx.logical_data(make_slice(&a, 1));
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// RW dev0 (18)
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ctx.task(exec_place::device(0), handle.rw())->*[](auto stream, auto s) {
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add<<<1, 1, 0, stream>>>(s.data_handle(), 1);
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};
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// READ dev1 (18)
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ctx.task(exec_place::device(1), handle.read())->*[](auto stream, auto s) {
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check_val<<<1, 1, 0, stream>>>(s.data_handle(), 18);
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};
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// REDUX dev1 (18 + 42)
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ctx.task(exec_place::device(1), handle.relaxed(redux_op))->*[](auto stream, auto s) {
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add<<<1, 1, 0, stream>>>(s.data_handle(), 42);
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};
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// READ dev0 (18 + 42)
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ctx.task(exec_place::device(0), handle.read())->*[](auto stream, auto s) {
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check_val<<<1, 1, 0, stream>>>(s.data_handle(), 18 + 42);
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};
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// READ
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ctx.task(exec_place::host(), handle.read())->*[](auto stream, auto s) {
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cuda_safe_call(cudaStreamSynchronize(stream));
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EXPECT(s(0) == 18 + 42);
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// printf("VALUE %d expected %d\n", s(0), 18 + 42);
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};
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ctx.finalize();
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}
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