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
23 lines
902 B
Plaintext
23 lines
902 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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// Tuning parameters found for signed integer types apply equally for unsigned integer types
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#include <nvbench_helper.cuh>
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// This benchmark tunes the old, non-lookahead scan implementation. Using it for benchmarking, will pick the lookahead
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// implementation on SM100+, but it's better to use the sum.lookahead.cu benchmark instead, which uses a single OffsetT.
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// %RANGE% TUNE_ITEMS ipt 7:24:1
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// %RANGE% TUNE_THREADS tpb 128:1024:32
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// %RANGE% TUNE_MAGIC_NS ns 0:2048:4
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// %RANGE% TUNE_DELAY_CONSTRUCTOR_ID dcid 0:7:1
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// %RANGE% TUNE_L2_WRITE_LATENCY_NS l2w 0:1200:5
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// %RANGE% TUNE_TRANSPOSE trp 0:1:1
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// %RANGE% TUNE_LOAD ld 0:1:1
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#define USES_LOOKAHEAD() 0
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using op_t = ::cuda::std::plus<>;
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using scan_offset_types = offset_types;
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#include "base.cuh"
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