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
79 lines
2.4 KiB
Plaintext
79 lines
2.4 KiB
Plaintext
// SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
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// SPDX-License-Identifier: BSD-3-Clause
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#include <cub/device/device_merge.cuh>
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#include <thrust/detail/raw_pointer_cast.h>
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#include <cuda/std/utility>
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#include <cstdint>
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#include <nvbench_helper.cuh>
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#include "merge_common.cuh"
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// %RANGE% TUNE_TRANSPOSE trp 0:1:1
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// %RANGE% TUNE_LOAD ld 0:3:1
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// %RANGE% TUNE_ITEMS_PER_THREAD ipt 7:24:1
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// %RANGE% TUNE_THREADS_PER_BLOCK_POW2 tpb 6:10:1
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template <typename KeyT>
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void keys(nvbench::state& state, nvbench::type_list<KeyT>)
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{
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using offset_t = int64_t;
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using compare_op_t = less_t;
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// Retrieve axis parameters
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const auto elements = static_cast<std::size_t>(state.get_int64("Elements{io}"));
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const bit_entropy entropy = str_to_entropy(state.get_string("Entropy"));
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const auto num_items_lhs = elements / 2;
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const auto num_items_rhs = elements - num_items_lhs;
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auto [keys_lhs, keys_rhs] = generate_lhs_rhs<KeyT>(num_items_lhs, num_items_rhs, entropy);
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thrust::device_vector<KeyT> keys_out(elements, thrust::no_init);
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KeyT* d_keys_lhs = thrust::raw_pointer_cast(keys_lhs.data());
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KeyT* d_keys_rhs = thrust::raw_pointer_cast(keys_rhs.data());
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KeyT* d_keys_out = thrust::raw_pointer_cast(keys_out.data());
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// Enable throughput calculations and add "Size" column to results.
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state.add_element_count(elements);
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state.add_global_memory_reads<KeyT>(elements);
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state.add_global_memory_writes<KeyT>(elements);
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caching_allocator_t alloc;
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state.exec(nvbench::exec_tag::gpu | nvbench::exec_tag::no_batch, [&](nvbench::launch& launch) {
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auto env = cub_bench_env(
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alloc,
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launch
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#if !TUNE_BASE
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,
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cuda::execution::tune(bench_policy_selector<key_t>{})
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#endif // !TUNE_BASE
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);
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_CCCL_TRY_CUDA_API(
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cub::DeviceMerge::MergeKeys,
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"MergePairs failed",
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d_keys_lhs,
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static_cast<offset_t>(num_items_lhs),
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d_keys_rhs,
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static_cast<offset_t>(num_items_rhs),
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d_keys_out,
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compare_op_t{},
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env);
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});
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}
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#ifdef TUNE_KeyT
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using key_types = nvbench::type_list<TUNE_KeyT>;
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#else // !defined(TUNE_KeyT)
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using key_types = fundamental_types;
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#endif // TUNE_KeyT
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NVBENCH_BENCH_TYPES(keys, NVBENCH_TYPE_AXES(key_types))
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.set_name("base")
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.set_type_axes_names({"KeyT{ct}"})
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.add_int64_power_of_two_axis("Elements{io}", nvbench::range(16, 28, 4))
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.add_string_axis("Entropy", {"1.000", "0.201"});
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