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
15 lines
622 B
Plaintext
15 lines
622 B
Plaintext
// SPDX-FileCopyrightText: Copyright (c) 2011-2023, NVIDIA CORPORATION. All rights reserved.
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// SPDX-License-Identifier: BSD-3
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// This benchmark uses a custom reduction operation, max_t, which is not known to CUB, so no operator specific
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// optimizations (e.g. using redux or DPX instructions) are performed. This benchmark covers the unoptimized code path.
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// Because CUB cannot detect this operator, we cannot add any tunings based on the results of this benchmark. Its main
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// use is to detect regressions.
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#include <nvbench_helper.cuh>
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using value_types = all_types;
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using op_t = max_t;
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#include "base.cuh"
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