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
62 lines
2.1 KiB
Plaintext
62 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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/**
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* @file
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* @brief This test makes sure we can generate a dot file with sections and stackable contexts
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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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// TODO (miscco): Make it work for windows
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#if !_CCCL_COMPILER(MSVC)
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// Test configuration constants
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constexpr size_t buffer_size = 64;
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constexpr size_t num_iterations = 3;
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stackable_ctx ctx;
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// Create logical data for the computation pipeline
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auto lA = ctx.logical_data(shape_of<slice<char>>(buffer_size));
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auto lB = ctx.logical_data(shape_of<slice<char>>(buffer_size));
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auto lC = ctx.logical_data(shape_of<slice<char>>(buffer_size));
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// Initialize all logical data in a dedicated DOT section
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auto r_init = ctx.dot_section("init");
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ctx.task(lA.write()).set_symbol("initA")->*[](cudaStream_t, auto) {};
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ctx.task(lB.write()).set_symbol("initB")->*[](cudaStream_t, auto) {};
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ctx.task(lC.write()).set_symbol("initC")->*[](cudaStream_t, auto) {};
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r_init.end();
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// Test nested DOT sections with stackable contexts
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// This creates a hierarchical structure to verify DOT graph generation
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for (size_t j = 0; j < num_iterations; j++)
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{
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auto r0 = ctx.dot_section("lvl0");
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ctx.task(lA.rw()).set_symbol("f1")->*[](cudaStream_t, auto) {};
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ctx.push();
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{
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auto r1 = ctx.dot_section("lvl1");
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ctx.task(lA.read(), lB.rw()).set_symbol("f2")->*[](cudaStream_t, auto, auto) {};
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ctx.task(lA.read(), lC.rw()).set_symbol("f2")->*[](cudaStream_t, auto, auto) {};
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ctx.task(lB.read(), lC.read(), lA.rw()).set_symbol("f3")->*[](cudaStream_t, auto, auto, auto) {};
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}
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ctx.pop();
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}
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ctx.finalize();
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#endif // !_CCCL_COMPILER(MSVC)
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}
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