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
39 lines
1.8 KiB
Plaintext
39 lines
1.8 KiB
Plaintext
// SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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#include <thrust/device_vector.h>
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#include <thrust/equal.h>
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#include <thrust/execution_policy.h>
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#include "nvbench_helper.cuh"
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template <typename T>
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static void benchmark(nvbench::state& state, nvbench::type_list<T>)
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{
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const auto elements = static_cast<std::size_t>(state.get_int64("Elements"));
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thrust::device_vector<T> a(elements, T{1});
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thrust::device_vector<T> b(elements, T{1});
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const auto common_prefix = state.get_float64("CommonPrefixRatio");
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const auto same_elements =
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std::min(static_cast<std::size_t>(static_cast<double>(elements) * common_prefix), elements);
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caching_allocator_t alloc;
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thrust::fill(policy(alloc), b.begin() + same_elements, b.end(), T{2});
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state.add_element_count(elements);
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state.add_global_memory_reads<T>(2 * std::max(same_elements, std::size_t(1))); // using `same_elements` instead
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// of `elements` corresponds to the
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// actual elements read in an early
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// exit
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state.exec(nvbench::exec_tag::gpu | nvbench::exec_tag::no_batch | nvbench::exec_tag::sync,
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[&](nvbench::launch& launch) {
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do_not_optimize(thrust::equal(policy(alloc, launch), a.begin(), a.end(), b.begin()));
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});
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}
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NVBENCH_BENCH_TYPES(benchmark, NVBENCH_TYPE_AXES(integral_types))
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.set_name("base")
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.set_type_axes_names({"T{ct}"})
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.add_int64_power_of_two_axis("Elements", nvbench::range(16, 28, 4))
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.add_float64_axis("CommonPrefixRatio", std::vector{1.0, 0.5, 0.0});
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