Files
project_6/cccl_upstream/thrust/benchmarks/bench/replace_if/basic.cu
EngineX CI 56fd68e7dd [INFRA] Import NVIDIA/CCCL upstream as optimization reference library
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
2026-07-30 09:35:51 +00:00

44 lines
1.3 KiB
Plaintext

// SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
#include <thrust/device_vector.h>
#include <thrust/replace.h>
#include <cuda/memory_pool>
#include <cuda/stream>
#include "nvbench_helper.cuh"
struct equal_to_42
{
template <class T>
__device__ constexpr bool operator()(const T& val) const noexcept
{
return val == static_cast<T>(42);
}
};
template <typename T>
static void basic(nvbench::state& state, nvbench::type_list<T>)
{
const auto elements = static_cast<std::size_t>(state.get_int64("Elements"));
thrust::device_vector<T> in = generate(elements, bit_entropy::_1_000, T{0}, T{42});
state.add_element_count(elements);
state.add_global_memory_reads<T>(elements);
state.add_global_memory_writes<T>(elements);
caching_allocator_t alloc{};
state.exec(nvbench::exec_tag::gpu | nvbench::exec_tag::no_batch | nvbench::exec_tag::sync,
[&](nvbench::launch& launch) {
thrust::replace_if(policy(alloc, launch), in.begin(), in.end(), equal_to_42{}, 1337);
});
}
NVBENCH_BENCH_TYPES(basic, NVBENCH_TYPE_AXES(fundamental_types))
.set_name("base")
.set_type_axes_names({"T{ct}"})
.add_int64_power_of_two_axis("Elements", nvbench::range(16, 28, 4));