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
1.6 KiB
Plaintext
39 lines
1.6 KiB
Plaintext
// SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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// SPDX-License-Identifier: BSD-3-Clause
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#include <thrust/count.h>
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#include <thrust/device_vector.h>
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#include <thrust/transform.h>
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#include <nvbench_helper.cuh>
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template <typename T>
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static void negate_if(nvbench::state& state, nvbench::type_list<T>)
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{
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const auto n = static_cast<std::size_t>(state.get_int64("Elements"));
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const auto entropy = str_to_entropy(state.get_string("Entropy"));
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const auto val = lerp_min_max<T>(entropy_to_probability(entropy));
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auto transform_op = ::cuda::std::negate<T>{};
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auto select_op = less_then_t<T>{val};
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thrust::device_vector<T> input = generate(n);
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thrust::device_vector<T> output(n, thrust::no_init);
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const auto selected_elements = thrust::count_if(input.cbegin(), input.cend(), select_op);
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state.add_element_count(n);
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state.add_global_memory_reads<T>(n);
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state.add_global_memory_writes<T>(selected_elements);
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caching_allocator_t alloc; // transform_if shouldn't allocate, but let's be consistent
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state.exec(
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nvbench::exec_tag::gpu | nvbench::exec_tag::no_batch | nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
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thrust::transform_if(policy(alloc, launch), input.begin(), input.end(), output.begin(), transform_op, select_op);
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});
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}
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NVBENCH_BENCH_TYPES(negate_if, NVBENCH_TYPE_AXES(integral_types))
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// .set_name("negate_if")
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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_string_axis("Entropy", {"1.000", "0.544", "0.000"});
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