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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/**
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* @file
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*
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* @brief Experiment with local context nesting
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*
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*/
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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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stackable_ctx ctx;
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int array[1024];
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for (size_t i = 0; i < 1024; i++)
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{
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array[i] = 1 + i * i;
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}
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auto lA = ctx.logical_data(array).set_symbol("A");
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// repeat : {tmp = a; tmp*=2; a+=tmp}
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for (size_t iter = 0; iter < 10; iter++)
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{
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stackable_ctx::graph_scope_guard graph{ctx}; // RAII: automatic push/pop (lock_guard style)
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auto tmp = ctx.logical_data(lA.shape()).set_symbol("tmp");
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ctx.parallel_for(tmp.shape(), tmp.write(), lA.read())->*[] __device__(size_t i, auto tmp, auto a) {
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tmp(i) = a(i);
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};
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ctx.parallel_for(tmp.shape(), tmp.rw())->*[] __device__(size_t i, auto tmp) {
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tmp(i) *= 2;
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};
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ctx.parallel_for(lA.shape(), tmp.read(), lA.rw())->*[] __device__(size_t i, auto tmp, auto a) {
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a(i) += tmp(i);
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};
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// ctx.pop() is called automatically when 'graph' goes out of scope
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}
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ctx.finalize();
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// Verify the array has been updated correctly by the write-back mechanism
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// Each iteration transforms each element: a_new = a_old + 2 * a_old = 3 * a_old
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// Starting from array[i] = 1 + i*i, after 10 iterations:
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// array[i] = 3^10 * (1 + i*i)
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constexpr int pow3_10 = 59049; // 3^10
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for (size_t i = 0; i < 1024; i++)
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{
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int expected = pow3_10 * (1 + static_cast<int>(i * i));
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EXPECT(array[i] == expected);
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}
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}
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