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
68 lines
1.9 KiB
Plaintext
68 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/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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using scalar_t = slice_stream_interface<int, 1>;
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template <typename T>
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__global__ void set_value(T* addr, T val)
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{
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*addr = val;
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}
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template <typename T>
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__global__ void check_value_and_reset(T* addr, T val, T reset_val)
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{
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assert(*addr == val);
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*addr = reset_val;
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}
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template <typename T>
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__global__ void add_val(T* inout_addr, T val)
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{
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*inout_addr += val;
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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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const int N = 4;
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auto var_handle = ctx.logical_data(shape_of<slice<int>>(1));
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var_handle.set_symbol("var");
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auto redux_op = std::make_shared<slice_reduction_op_sum<int>>();
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const int niters = 4;
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for (int iter = 0; iter < niters; iter++)
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{
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// We add i (total = N(N-1)/2 + initial_value)
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for (int i = 0; i < N; i++)
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{
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ctx.task(var_handle.relaxed(redux_op))->*[=](cudaStream_t stream, auto d_var) {
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add_val<<<1, 1, 0, stream>>>(d_var.data_handle(), i);
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cuda_safe_call(cudaGetLastError());
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};
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}
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// Check that we have the expected value, and reset it so that we can perform another reduction
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ctx.task(var_handle.rw())->*[=](cudaStream_t stream, auto d_var) {
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int expected = (N * (N - 1)) / 2;
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check_value_and_reset<<<1, 1, 0, stream>>>(d_var.data_handle(), expected, 0);
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};
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}
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ctx.finalize();
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}
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