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
61 lines
1.9 KiB
Plaintext
61 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/allocators/adapters.cuh>
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#include <cuda/experimental/stf.cuh>
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using namespace cuda::experimental::stf;
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int main()
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{
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double* d_ptrA;
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const size_t N = 128 * 1024;
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const size_t NITER = 10;
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// User allocated memory
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cuda_safe_call(cudaMalloc(&d_ptrA, N * sizeof(double)));
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async_resources_handle handle;
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cudaStream_t stream;
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cuda_safe_call(cudaStreamCreate(&stream));
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for (size_t i = 0; i < NITER; i++)
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{
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graph_ctx ctx(stream, handle);
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// The uncached allocator of the context will be using cudaMallocAsync(...,
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// stream) to avoid creating memory nodes in the graph (because they are
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// costly and caching the graph also keeps memory allocated)
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auto wrapper = stream_adapter(ctx, stream);
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ctx.set_allocator(block_allocator<buddy_allocator>(ctx, wrapper.allocator()));
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auto A = ctx.logical_data(make_slice(d_ptrA, N), data_place::current_device());
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for (size_t k = 0; k < 4; k++)
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{
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auto tmp = ctx.logical_data(A.shape());
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auto tmp2 = ctx.logical_data(A.shape());
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// Test device and managed memory
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ctx.parallel_for(A.shape(), A.read(), tmp.write(), tmp2.write(data_place::managed()))
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->*[] __device__(size_t i, auto a, auto tmp, auto tmp2) {
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tmp(i) = a(i);
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tmp2(i) = a(i);
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};
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}
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ctx.finalize();
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wrapper.clear();
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}
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cuda_safe_call(cudaStreamSynchronize(stream));
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}
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