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
69 lines
1.7 KiB
Plaintext
69 lines
1.7 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 <iostream>
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using namespace cuda::experimental::stf;
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__global__ void kernel()
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{
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// No-op
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}
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int main(int argc, char** argv)
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{
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int nblocks = 4;
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size_t block_size = 1024 * 1024;
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if (argc > 1)
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{
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nblocks = atoi(argv[1]);
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}
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if (argc > 2)
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{
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block_size = atoi(argv[2]);
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}
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// At most 1 buffer is allocated at the same time
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setenv("MAX_ALLOC_CNT", "2", 1);
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graph_ctx ctx;
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::std::vector<logical_data<slice<char>>> handles(nblocks);
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std::vector<char> h_buffer(nblocks * block_size);
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for (int i = 0; i < nblocks; i++)
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{
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handles[i] = ctx.logical_data(make_slice(&h_buffer[i * block_size], block_size));
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handles[i].set_symbol("D_" + std::to_string(i));
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}
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// We only 2 buffers, we are forced to reuse the buffer from D0 for D2
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for (int i = 0; i < 3; i++)
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{
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ctx.task(handles[i % nblocks].read(), handles[(i + 1) % nblocks].rw())
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->*[&](cudaStream_t s, auto /*unused*/, auto /*unused*/) {
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kernel<<<1, 1, 0, s>>>();
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};
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}
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ctx.submit();
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if (argc > 3)
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{
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std::cout << "Generating DOT output in " << argv[3] << '\n';
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ctx.print_to_dot(argv[3]);
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}
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ctx.finalize();
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}
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