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<hr>
<h3>About CUB</h3>

Current release: v1.6.4 (12/06/2016)

We recommend the [CUB Project Website](http://nvlabs.github.com/cub) and the [cub-users discussion forum](http://groups.google.com/group/cub-users) for further information and examples.

CUB provides state-of-the-art, reusable software components for every layer 
of the CUDA programming model:
- [<b><em>Device-wide primitives</em></b>] (https://nvlabs.github.com/cub/group___device_module.html) 
  - Sort, prefix scan, reduction, histogram, etc.  
  - Compatible with CUDA dynamic parallelism
- [<b><em>Block-wide "collective" primitives</em></b>] (https://nvlabs.github.com/cub/group___block_module.html)
  - I/O, sort, prefix scan, reduction, histogram, etc.  
  - Compatible with arbitrary thread block sizes and types 
- [<b><em>Warp-wide "collective" primitives</em></b>] (https://nvlabs.github.com/cub/group___warp_module.html)
  - Warp-wide prefix scan, reduction, etc.
  - Safe and architecture-specific
- [<b><em>Thread and resource utilities</em></b>](https://nvlabs.github.com/cub/group___thread_module.html)
  - PTX intrinsics, device reflection, texture-caching iterators, caching memory allocators, etc. 

![Orientation of collective primitives within the CUDA software stack](http://nvlabs.github.com/cub/cub_overview.png)

<br><hr>
<h3>A Simple Example</h3>

```C++
#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);
}
```

Each thread block uses cub::BlockRadixSort to collectively sort 
its own input segment.  The class is specialized by the 
data type being sorted, by the number of threads per block, by the number of 
keys per thread, and implicitly by the targeted compilation architecture.  

The cub::BlockLoad and cub::BlockStore classes are similarly specialized.    
Furthermore, to provide coalesced accesses to device memory, these primitives are 
configured to access memory using a striped access pattern (where consecutive threads 
simultaneously access consecutive items) and then <em>transpose</em> the keys into 
a [<em>blocked arrangement</em>](index.html#sec4sec3) of elements across threads. 

Once specialized, these classes expose opaque \p TempStorage member types.  
The thread block uses these storage types to statically allocate the union of 
shared memory needed by the thread block.  (Alternatively these storage types 
could be aliased to global memory allocations).

<br><hr>
<h3>Stable Releases</h3>