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
40 lines
1.0 KiB
C++
40 lines
1.0 KiB
C++
//===----------------------------------------------------------------------===//
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//
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// Part of CUDA Experimental 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) 2024 NVIDIA CORPORATION & AFFILIATES.
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//
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//===----------------------------------------------------------------------===//
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#include "errors.h"
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#include <stdexcept>
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void check(nvrtcResult result)
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{
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if (result != NVRTC_SUCCESS)
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{
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throw std::runtime_error(std::string("NVRTC error: ") + nvrtcGetErrorString(result));
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}
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}
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void check(CUresult result)
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{
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if (result != CUDA_SUCCESS)
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{
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const char* str = nullptr;
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cuGetErrorString(result, &str);
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throw std::runtime_error(std::string("CUDA error: ") + str);
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}
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}
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void check(nvJitLinkResult result)
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{
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if (result != NVJITLINK_SUCCESS)
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{
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throw std::runtime_error(std::string("nvJitLink error: ") + std::to_string(result));
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}
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}
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