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
22 lines
737 B
Plaintext
22 lines
737 B
Plaintext
// The purpose of this test is to verify that CUB can use NVTX without any additional dependencies. It is built as part
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// of the unit tests, but can also be built standalone:
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// Compile (from current directory):
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// nvcc test_nvtx_standalone.cu -I../../cub -I../../thrust -I../../libcudacxx/include -o nvtx_standalone
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// Profile & view:
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// (nsys profile -o nvtx_standalone.nsys-rep -f true ./nvtx_standalone || true) && nsys-ui nvtx_standalone.nsys-rep
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#include <cub/device/device_for.cuh>
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#include <cuda/iterator>
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#include <cuda/std/functional>
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int main()
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{
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_CCCL_NVTX_RANGE_SCOPE("main");
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cuda::counting_iterator<int> it{0};
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cub::DeviceFor::ForEach(it, it + 16, ::cuda::std::negate<int>{});
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cudaDeviceSynchronize();
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}
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