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
130 lines
2.9 KiB
Plaintext
130 lines
2.9 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.cuh>
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#include <random>
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using namespace cuda::experimental::stf;
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struct body
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{
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// mass
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double mass;
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// position
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double pos[3];
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// speed
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double vel[3];
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// acceleration
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double acc[3];
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};
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int main()
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{
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constexpr double kSofteningSquared = 1e-3;
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constexpr double kG = 6.67259e-11;
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size_t BODY_CNT = 4096;
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double dt = 0.1;
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size_t NITER = 25;
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context ctx;
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std::vector<body> particles;
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particles.resize(BODY_CNT);
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// Initialize particles
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std::random_device rd;
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std::mt19937 gen(rd());
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std::uniform_real_distribution<> dis(-1.0, 1.0);
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for (auto& p : particles)
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{
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p.mass = 1.0;
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p.pos[0] = dis(gen);
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p.pos[1] = dis(gen);
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p.pos[2] = dis(gen);
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p.vel[0] = dis(gen);
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p.vel[1] = dis(gen);
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p.vel[2] = dis(gen);
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p.acc[0] = 0.0;
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p.acc[1] = 0.0;
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p.acc[2] = 0.0;
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}
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auto h_particles = ctx.logical_data(make_slice(&particles[0], BODY_CNT));
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auto fn = [dt](context ctx, logical_data<slice<body>> h_particles) {
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// Compute accelerations
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ctx.parallel_for(h_particles.shape(), h_particles.rw())->*[=] _CCCL_DEVICE __host__(size_t i, slice<body> p) {
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double acc[3];
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for (size_t k = 0; k < 3; k++)
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{
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acc[k] = p(i).acc[k];
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}
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for (size_t j = 0; j < p.extent(0); j++)
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{
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if (i != j)
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{
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double d[3];
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for (size_t k = 0; k < 3; k++)
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{
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d[k] = p(j).pos[k] - p(i).pos[k];
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}
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double dist = d[0] * d[0] + d[1] * d[1] + d[2] * d[2] + kSofteningSquared;
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double dist_inv = 1.0 / sqrt(dist);
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for (size_t k = 0; k < 3; k++)
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{
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acc[k] += d[k] * kG * p(j).mass * dist_inv * dist_inv * dist_inv;
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}
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}
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}
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for (size_t k = 0; k < 3; k++)
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{
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p(i).acc[k] = acc[k];
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}
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};
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// Update velocity and positions
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ctx.parallel_for(h_particles.shape(), h_particles.rw())->*[=] __host__ __device__(size_t i, slice<body> p) {
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for (size_t k = 0; k < 3; k++)
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{
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p(i).vel[k] += p(i).acc[k] * dt;
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}
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for (size_t k = 0; k < 3; k++)
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{
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p(i).pos[k] += p(i).vel[k] * dt;
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}
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for (size_t k = 0; k < 3; k++)
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{
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p(i).acc[k] = 0.0;
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}
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};
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};
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algorithm one_iter;
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for (size_t iter = 0; iter < NITER; iter++)
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{
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// fprintf(stderr, "ITER %ld\n", iter);
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one_iter.run_as_task(fn, ctx, h_particles.rw());
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}
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ctx.finalize();
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}
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