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
86 lines
3.3 KiB
Plaintext
86 lines
3.3 KiB
Plaintext
//===----------------------------------------------------------------------===//
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//
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// Part of libcu++, the C++ Standard Library for your entire system,
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// under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
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//
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//===----------------------------------------------------------------------===//
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#include <thrust/device_vector.h>
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#include <thrust/fill.h>
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#include <cuda/functional>
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#include <cuda/std/execution>
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#include <cuda/stream>
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#include "nvbench_helper.cuh"
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// All-zero is a valid heap; setting one element to 1 forces a violation at
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// that child index since its parent is still 0.
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template <typename T>
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static void prepare_input(thrust::device_vector<T>& d, std::size_t violation_point)
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{
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thrust::fill(d.begin(), d.end(), T{0});
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if (violation_point >= 1 && violation_point < d.size())
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{
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d[violation_point] = T{1};
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}
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}
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template <typename T>
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static void basic(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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const auto violation_frac = state.get_float64("ViolationAt");
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const auto violation_point = cuda::std::clamp<std::size_t>(
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static_cast<std::size_t>(static_cast<double>(elements) * violation_frac), std::size_t{0}, elements - 1);
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thrust::device_vector<T> dinput(elements, thrust::no_init);
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prepare_input(dinput, violation_point);
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state.add_global_memory_reads<T>(2 * violation_point);
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state.add_global_memory_writes<size_t>(1);
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caching_allocator_t alloc{};
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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(cuda::std::is_heap(cuda_policy(alloc, launch), dinput.begin(), dinput.end()));
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});
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}
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NVBENCH_BENCH_TYPES(basic, NVBENCH_TYPE_AXES(fundamental_types))
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.set_name("base")
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.add_int64_power_of_two_axis("Elements", nvbench::range(16, 28, 4))
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.add_float64_axis("ViolationAt", std::vector{1.0, 0.5, 0.01});
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template <typename T>
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static void with_predicate(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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const auto violation_frac = state.get_float64("ViolationAt");
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const auto violation_point = cuda::std::clamp<std::size_t>(
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static_cast<std::size_t>(static_cast<double>(elements) * violation_frac), std::size_t{0}, elements - 1);
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thrust::device_vector<T> dinput(elements, thrust::no_init);
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prepare_input(dinput, violation_point);
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state.add_global_memory_reads<T>(2 * violation_point);
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state.add_global_memory_writes<size_t>(1);
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caching_allocator_t alloc{};
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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(
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cuda::std::is_heap(cuda_policy(alloc, launch), dinput.begin(), dinput.end(), cuda::std::less<>{}));
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});
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}
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NVBENCH_BENCH_TYPES(with_predicate, NVBENCH_TYPE_AXES(fundamental_types))
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.set_name("with_predicate")
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.add_int64_power_of_two_axis("Elements", nvbench::range(16, 28, 4))
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.add_float64_axis("ViolationAt", std::vector{1.0, 0.5, 0.01});
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