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
73 lines
1.9 KiB
Plaintext
73 lines
1.9 KiB
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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#include <thrust/device_vector.h>
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#include <thrust/execution_policy.h>
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#include <thrust/random.h>
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#include <thrust/shuffle.h>
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#include "nvbench_helper.cuh"
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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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thrust::device_vector<T> data(elements);
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state.add_element_count(elements);
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state.add_global_memory_reads<T>(elements);
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state.add_global_memory_writes<T>(elements);
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auto do_engine = [&](auto&& engine_constructor) {
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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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thrust::shuffle(policy(alloc, launch), data.begin(), data.end(), engine_constructor());
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});
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};
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const auto rng_engine = state.get_string("Engine");
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if (rng_engine == "minstd")
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{
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do_engine([] {
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return thrust::random::minstd_rand{};
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});
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}
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else if (rng_engine == "ranlux24")
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{
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do_engine([] {
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return thrust::random::ranlux24{};
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});
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}
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else if (rng_engine == "ranlux48")
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{
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do_engine([] {
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return thrust::random::ranlux48{};
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});
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}
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else if (rng_engine == "taus88")
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{
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do_engine([] {
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return thrust::random::taus88{};
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});
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}
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}
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using types =
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nvbench::type_list<int8_t,
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int16_t,
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int32_t,
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int64_t
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#if _CCCL_HAS_INT128()
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,
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int128_t
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#endif
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>;
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NVBENCH_BENCH_TYPES(basic, NVBENCH_TYPE_AXES(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_string_axis("Engine", {"minstd", "ranlux24", "ranlux48", "taus88"});
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