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
42 lines
1011 B
Plaintext
42 lines
1011 B
Plaintext
//===----------------------------------------------------------------------===//
|
|
//
|
|
// Part of CUDASTF in CUDA C++ Core Libraries,
|
|
// under the Apache License v2.0 with LLVM Exceptions.
|
|
// See https://llvm.org/LICENSE.txt for license information.
|
|
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
|
|
// SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
#include <cuda/experimental/stf.cuh>
|
|
|
|
using namespace cuda::experimental::stf;
|
|
|
|
void rec_func(exec_place places)
|
|
{
|
|
if (places.size() == 1)
|
|
{
|
|
// places->print("SINGLE");
|
|
}
|
|
else
|
|
{
|
|
// places->print("REC");
|
|
for (int i = 0; i < 2; i++)
|
|
{
|
|
// Take every other places from the grid
|
|
auto half_places = partition_cyclic(places, dim4(2), pos4(i));
|
|
rec_func(half_places);
|
|
}
|
|
}
|
|
}
|
|
|
|
int main()
|
|
{
|
|
auto places = exec_place::all_devices();
|
|
// places->print("ALL");
|
|
|
|
rec_func(places);
|
|
|
|
return 0;
|
|
}
|