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
17 lines
629 B
Plaintext
17 lines
629 B
Plaintext
// SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
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// SPDX-License-Identifier: BSD-3
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#include <nvbench_helper.cuh>
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// %RANGE% TUNE_ITEMS_PER_VEC_LOAD_POW2 ipv 1:2:1
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// %RANGE% TUNE_S_THREADS_PER_WARP stpw 1:32:1
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// %RANGE% TUNE_M_THREADS_PER_WARP mtpw 1:32:1
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// %RANGE% TUNE_L_NOMINAL_4B_THREADS_PER_BLOCK ltpb 128:1024:32
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// %RANGE% TUNE_S_NOMINAL_4B_ITEMS_PER_THREAD sipt 1:32:1
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// %RANGE% TUNE_M_NOMINAL_4B_ITEMS_PER_THREAD mipt 1:32:1
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// %RANGE% TUNE_L_NOMINAL_4B_ITEMS_PER_THREAD lipt 7:24:1
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using value_types = all_types;
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using op_t = ::cuda::std::plus<>;
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#include "base.cuh"
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