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
1004 B
Plaintext
22 lines
1004 B
Plaintext
=============== array.normal begin ===============
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(cuda::std::array<int, 3>) ([0] = -7, [1] = 0, [2] = 42)
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=============== array.normal end ===============
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=============== array.empty begin ===============
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(cuda::std::array<int, 0>)
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=============== array.empty end ===============
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=============== array.nested begin ===============
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(cuda::std::array<cuda::std::array<int, 2>, 2>) {
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[0] = ([0] = 13, [1] = -5)
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[1] = ([0] = 0, [1] = 88)
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}
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=============== array.nested end ===============
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=============== array.alias begin ===============
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(cuda::std::array<int, 4>) ([0] = -31, [1] = 17, [2] = 8, [3] = -64)
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=============== array.alias end ===============
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=============== array.update.before begin ===============
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(cuda::std::array<int, 3>) ([0] = 6, [1] = -91, [2] = 52)
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=============== array.update.before end ===============
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=============== array.update.after begin ===============
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(cuda::std::array<int, 3>) ([0] = 3, [1] = 85, [2] = -12)
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=============== array.update.after end ===============
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