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
60 lines
2.1 KiB
Plaintext
60 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/stream_ctx.cuh>
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#include <iostream>
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using namespace cuda::experimental::stf;
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int main()
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{
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stream_ctx ctx;
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// Contiguous 1D
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double* X = new double[1024];
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auto handle_X = ctx.logical_data(make_slice(X, 1024));
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// Contiguous 2D
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double* X2 = new double[1024 * 1024];
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auto handle_X2 = ctx.logical_data(make_slice(X2, std::tuple{1024, 1024}, 1024));
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// Contiguous 3D
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double* X4 = new double[128 * 128 * 128];
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auto handle_X4 = ctx.logical_data(make_slice(X4, std::tuple{128, 128, 128}, 128, 128 * 128));
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// Discontiguous 2D
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double* X3 = new double[128 * 8];
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auto handle_X3 = ctx.logical_data(make_slice(X3, std::tuple{64, 8}, 128));
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// Discontiguous 3D
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double* X5 = new double[32 * 4 * 4];
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auto handle_X5 = ctx.logical_data(make_slice(X5, std::tuple{16, 4, 4}, 32, 32 * 4));
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double* X6 = new double[32 * 4 * 4];
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auto handle_X6 = ctx.logical_data(make_slice(X6, std::tuple{32, 2, 4}, 32, 32 * 4));
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double* X7 = new double[128 * 128 * 128];
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cuda_safe_call(cudaHostRegister(X7, 128 * 128 * 128, cudaHostRegisterPortable));
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auto handle_X7 = ctx.logical_data(make_slice(X7, std::tuple{128, 128, 128}, 128, 128 * 128));
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// Detect that this was already pinned
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double* X9 = new double[1024];
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cuda_safe_call(cudaHostRegister(X9, 1024, cudaHostRegisterPortable));
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auto handle_X9 = ctx.logical_data(make_slice(X9, 1024));
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// Detect that this was already pinned
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double* X8 = new double[4 * 4 * 4];
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cuda_safe_call(cudaHostRegister(X8, 4 * 4 * 4, cudaHostRegisterPortable));
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auto handle_X8 = ctx.logical_data(make_slice(X8, std::tuple{1, 4, 4}, 4, 4 * 4));
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ctx.finalize();
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}
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