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
83 lines
2.0 KiB
Plaintext
83 lines
2.0 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 This example illustrates how we can annotate tasks and logical data with debugging symbol
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*
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* CUDASTF_DOT_FILE=axpy.dot build/examples/axpy-annotated
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*
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* # Generate the visualization from this dot file in PDF or PNG format
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* dot -Tpdf axpy.dot -o axpy.pdf
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* dot -Tpng axpy.dot -o axpy.png
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*
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* # Generate visualization with events (for advanced users)
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* CUDASTF_DOT_IGNORE_PREREQS=0 CUDASTF_DOT_FILE=axpy-with-events.dot build/examples/axpy-annotated
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* dot -Tpng axpy-with-events.dot -o axpy-with-events.png
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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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__global__ void axpy(double a, slice<const double> x, slice<double> y)
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{
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int tid = blockIdx.x * blockDim.x + threadIdx.x;
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int nthreads = gridDim.x * blockDim.x;
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for (int i = tid; i < x.size(); i += nthreads)
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{
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y(i) += a * x(i);
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}
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}
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double X0(int i)
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{
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return sin((double) i);
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}
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double Y0(int i)
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{
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return cos((double) i);
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}
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int main()
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{
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context ctx;
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const size_t N = 16;
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double X[N], Y[N];
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for (size_t i = 0; i < N; i++)
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{
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X[i] = X0(i);
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Y[i] = Y0(i);
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}
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double alpha = 3.14;
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auto lX = ctx.logical_data(X).set_symbol("X");
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auto lY = ctx.logical_data(Y).set_symbol("Y");
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/* Compute Y = Y + alpha X */
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ctx.task(lX.read(), lY.rw()).set_symbol("axpy")->*[&](cudaStream_t s, auto dX, auto dY) {
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axpy<<<16, 128, 0, s>>>(alpha, dX, dY);
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};
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ctx.finalize();
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for (size_t i = 0; i < N; i++)
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{
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assert(fabs(Y[i] - (Y0(i) + alpha * X0(i))) < 0.0001);
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assert(fabs(X[i] - X0(i)) < 0.0001);
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}
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}
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