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
70 lines
1.9 KiB
Plaintext
70 lines
1.9 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/graph/graph_ctx.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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template <typename S>
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__global__ void inc_kernel(S sA)
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{
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sA(threadIdx.x)++;
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}
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template <typename S1, typename S2>
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__global__ void copy_kernel(S1 sA, S2 sB)
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{
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sB(threadIdx.x) = sA(threadIdx.x);
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}
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template <typename Ctx>
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void run()
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{
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int A[10] = {0};
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int B[10] = {0};
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Ctx ctx;
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auto lA = ctx.logical_data(A);
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auto lB = ctx.logical_data(B);
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for (size_t k = 0; k < 10; k++)
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{
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// fprintf(stderr, "iter %zu - 0 : ctx.hash %zu\n", k, ctx.hash());
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ctx.task(lA.rw())->*[](cudaStream_t stream, auto sA) {
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inc_kernel<<<1, 10, 0, stream>>>(sA);
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};
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// fprintf(stderr, "iter %zu - 1 : ctx.hash %zu\n", k, ctx.hash());
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ctx.task(lA.read(), lB.rw())->*[](cudaStream_t stream, auto sA, auto sB) {
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copy_kernel<<<1, 10, 0, stream>>>(sA, sB);
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};
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// fprintf(stderr, "iter %zu - 2 : ctx.hash %zu\n", k, ctx.hash());
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ctx.task(lB.rw())->*[](cudaStream_t stream, auto sB) {
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inc_kernel<<<1, 10, 0, stream>>>(sB);
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};
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// fprintf(stderr, "iter %zu - 3 : ctx.hash %zu\n", k, ctx.hash());
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}
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ctx.host_launch(lA.read(), lB.read())->*[&](auto /*unused*/, auto /*unused*/) {
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// fprintf(stderr, "HOST END : ctx.hash %zu\n", ctx.hash());
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};
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ctx.finalize();
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}
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int main()
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{
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run<stream_ctx>();
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run<graph_ctx>();
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}
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