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
18 lines
506 B
Plaintext
18 lines
506 B
Plaintext
#include <cub/device/device_for.cuh> // internal include of NVTX
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#include <thrust/iterator/counting_iterator.h>
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#include <cuda/iterator>
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#include <cuda/std/functional>
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#include <nvtx3/nvtx3.hpp> // user-side include of NVTX, retrieved elsewhere
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int main()
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{
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nvtx3::scoped_range range("user-range"); // user-side use of unversioned NVTX API
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cuda::counting_iterator<int> it{0};
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cub::DeviceFor::ForEach(it, it + 16, ::cuda::std::negate<int>{}); // internal use of NVTX
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cudaDeviceSynchronize();
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}
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