cub
2.1.0
CUB 为 CUDA 编程模型的每一层提供最先进的、可重用的软件组件：
CUB 包含在 NVIDIA HPC SDK 和 CUDA 工具包中。
我们推荐 CUB 项目网站以获取更多信息和示例。
# include < cub/cub.cuh >
// Block-sorting CUDA kernel
__global__ void BlockSortKernel ( int *d_in, int *d_out)
{
using namespace cub ;
// Specialize BlockRadixSort, BlockLoad, and BlockStore for 128 threads
// owning 16 integer items each
typedef BlockRadixSort< int , 128 , 16 > BlockRadixSort;
typedef BlockLoad< int , 128 , 16 , BLOCK_LOAD_TRANSPOSE> BlockLoad;
typedef BlockStore< int , 128 , 16 , BLOCK_STORE_TRANSPOSE> BlockStore;
// Allocate shared memory
__shared__ union {
typename BlockRadixSort::TempStorage sort;
typename BlockLoad::TempStorage load;
typename BlockStore::TempStorage store;
} temp_storage;
int block_offset = blockIdx. x * ( 128 * 16 ); // OffsetT for this block's ment
// Obtain a segment of 2048 consecutive keys that are blocked across threads
int thread_keys[ 16 ];
BlockLoad (temp_storage. load ). Load (d_in + block_offset, thread_keys);
__syncthreads ();
// Collectively sort the keys
BlockRadixSort (temp_storage. sort ). Sort (thread_keys);
__syncthreads ();
// Store the sorted segment
BlockStore (temp_storage. store ). Store (d_out + block_offset, thread_keys);
}每个线程块使用cub::BlockRadixSort对其自己的输入段进行集体排序。该类根据要排序的数据类型、每个块的线程数、每个线程的键数以及隐式的目标编译体系结构进行专门化。
cub::BlockLoad和cub::BlockStore类同样是专门化的。此外，为了提供对设备内存的合并访问，这些原语被配置为使用条带访问模式（其中连续线程同时访问连续项）来访问内存，然后将键转置为跨线程的元素的阻塞排列。
一旦专门化，这些类就会公开不透明的TempStorage成员类型。线程块使用这些存储类型来静态分配线程块所需的共享内存的联合。 （或者，这些存储类型可以别名为全局内存分配）。
使用以下编译器的指定版本定期测试 CUB。不受支持的版本可能会发出弃用警告，可以通过在编译期间定义 CUB_IGNORE_DEPRECATED_COMPILER 来消除警告。
除了 GitHub 之外，CUB 还随 NVIDIA HPC SDK 和 CUDA 工具包一起分发。
有关特定版本的详细信息，请参阅变更日志。
| CUB 发布 | 包含在 |
|---|---|
| 2.0.1 | CUDA工具包12.0 |
| 2.0.0 | 待定 |
| 1.17.2 | 待定 |
| 1.17.1 | 待定 |
| 1.17.0 | 待定 |
| 1.16.0 | 待定 |
| 1.15.0 | NVIDIA HPC SDK 22.1 和 CUDA 工具包 11.6 |
| 1.14.0 | NVIDIA HPC SDK 21.9 |
| 1.13.1 | CUDA 工具包 11.5 |
| 1.13.0 | NVIDIA HPC SDK 21.7 |
| 1.12.1 | CUDA 工具包 11.4 |
| 1.12.0 | NVIDIA HPC SDK 21.3 |
| 1.11.0 | CUDA 工具包 11.3 |
| 1.10.0 | NVIDIA HPC SDK 20.9 和 CUDA 工具包 11.2 |
| 1.9.10-1 | NVIDIA HPC SDK 20.7 和 CUDA 工具包 11.1 |
| 1.9.10 | NVIDIA HPC SDK 20.5 |
| 1.9.9 | CUDA工具包11.0 |
| 1.9.8-1 | NVIDIA HPC SDK 20.3 |
| 1.9.8 | CUDA 工具包 11.0 抢先体验 |
| 1.9.8 | CUDA 11.0 抢先体验 |
| 1.8.0 | |
| 1.7.5 | 推力1.9.2 |
| 1.7.4 | 推力1.9.1-2 |
| 1.7.3 | |
| 1.7.2 | |
| 1.7.1 | |
| 1.7.0 | 推力1.9.0-5 |
| 1.6.4 | |
| 1.6.3 | |
| 1.6.2（之前为 1.5.5） | |
| 1.6.1（之前为 1.5.4） | |
| 1.6.0（之前的 1.5.3） | |
| 1.5.2 | |
| 1.5.1 | |
| 1.5.0 | |
| 1.4.1 | |
| 1.4.0 | |
| 1.3.2 | |
| 1.3.1 | |
| 1.3.0 | |
| 1.2.3 | |
| 1.2.2 | |
| 1.2.0 | |
| 1.1.1 | |
| 1.0.2 | |
| 1.0.1 | |
| 0.9.4 | |
| 0.9.2 | |
| 0.9.1 | |
| 0.9.0 |
CUB 和推力相互依赖。建议克隆 Thrust 并将 CUB 构建为 Thrust 的组件。
CUB 使用 CMake 构建系统来构建单元测试、示例和标头测试。要作为开发人员构建 CUB，应遵循以下秘诀：
# Clone Thrust and CUB from Github. CUB is located in Thrust's
# `dependencies/cub` submodule.
git clone --recursive https://github.com/NVIDIA/thrust.git
cd thrust
# Create build directory:
mkdir build
cd build
# Configure -- use one of the following:
cmake -DTHRUST_INCLUDE_CUB_CMAKE=ON .. # Command line interface.
ccmake -DTHRUST_INCLUDE_CUB_CMAKE=ON .. # ncurses GUI (Linux only)
cmake-gui # Graphical UI, set source/build directories and options in the app
# Build:
cmake --build . -j < num jobs > # invokes make (or ninja, etc)
# Run tests and examples:
ctest默认情况下，以 C++14 标准为目标，但这可以在 CMake 中更改。有关配置 CUB 构建和创建拉取请求的更多信息，请参阅 CONTRIBUTING.md。
CUB 可在“New BSD”开源许可证下使用：
Copyright (c) 2010-2011, Duane Merrill. All rights reserved.
Copyright (c) 2011-2018, NVIDIA CORPORATION. All rights reserved.
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