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cub::WarpReduce< T, LOGICAL_WARP_THREADS, PTX_ARCH > Class Template Reference

Detailed description

template< typename T, int LOGICAL_WARP_THREADS = CUB_PTX_WARP_THREADS, int PTX_ARCH = CUB_PTX_ARCH>
class cub::WarpReduce< T, LOGICAL_WARP_THREADS, PTX_ARCH >

The WarpReduce class provides collective methods for computing a parallel reduction of items partitioned across a CUDA thread warp.

warp_reduce_logo.png
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Template Parameters
TThe reduction input/output element type
LOGICAL_WARP_THREADS[optional] The number of threads per "logical" warp (may be less than the number of hardware warp threads). Default is the warp size of the targeted CUDA compute-capability (e.g., 32 threads for SM20).
PTX_ARCH[optional] The PTX compute capability for which to to specialize this collective, formatted as per the CUDA_ARCH macro (e.g., 350 for sm_35). Useful for determining the collective's storage requirements for a given device from the host. (Default: the value of CUDA_ARCH during the current compiler pass)
Overview
  • A reduction (or fold) uses a binary combining operator to compute a single aggregate from a list of input elements.
  • Supports "logical" warps smaller than the physical warp size (e.g., logical warps of 8 threads)
  • The number of entrant threads must be an multiple of LOGICAL_WARP_THREADS
Performance Considerations
  • Uses special instructions when applicable (e.g., warp SHFL instructions)
  • Uses synchronization-free communication between warp lanes when applicable
  • Incurs zero bank conflicts for most types
  • Computation is slightly more efficient (i.e., having lower instruction overhead) for:
    • Summation (vs. generic reduction)
    • The architecture's warp size is a whole multiple of LOGICAL_WARP_THREADS
Simple Examples
Every thread in the warp uses the WarpReduce class by first specializing the WarpReduce type, then instantiating an instance with parameters for communication, and finally invoking or more collective member functions.
The code snippet below illustrates four concurrent warp sum reductions within a block of 128 threads (one per each of the 32-thread warps).
#include <cub/cub.cuh>
__global__ void ExampleKernel(...)
{
// Specialize WarpReduce for type int
// Allocate WarpReduce shared memory for 4 warps
__shared__ typename WarpReduce::TempStorage temp_storage[4];
// Obtain one input item per thread
int thread_data = ...
// Return the warp-wide sums to each lane0 (threads 0, 32, 64, and 96)
int warp_id = threadIdx.x / 32;
int aggregate = WarpReduce(temp_storage[warp_id]).Sum(thread_data);
Suppose the set of input thread_data across the block of threads is {0, 1, 2, 3, ..., 127}. The corresponding output aggregate in threads 0, 32, 64, and 96 will 496, 1520, 2544, and 3568, respectively (and is undefined in other threads).
The code snippet below illustrates a single warp sum reduction within a block of 128 threads.
#include <cub/cub.cuh>
__global__ void ExampleKernel(...)
{
// Specialize WarpReduce for type int
// Allocate WarpReduce shared memory for one warp
__shared__ typename WarpReduce::TempStorage temp_storage;
...
// Only the first warp performs a reduction
if (threadIdx.x < 32)
{
// Obtain one input item per thread
int thread_data = ...
// Return the warp-wide sum to lane0
int aggregate = WarpReduce(temp_storage).Sum(thread_data);
Suppose the set of input thread_data across the warp of threads is {0, 1, 2, 3, ..., 31}. The corresponding output aggregate in thread0 will be 496 (and is undefined in other threads).

Classes

struct  TempStorage
 The operations exposed by WarpReduce require a temporary memory allocation of this nested type for thread communication. This opaque storage can be allocated directly using the __shared__ keyword. Alternatively, it can be aliased to externally allocated memory (shared or global) or union'd with other storage allocation types to facilitate memory reuse. More...
 

Public Methods

Collective constructors
__device__ __forceinline__ WarpReduce (TempStorage &temp_storage)
 Collective constructor using the specified memory allocation as temporary storage. Logical warp and lane identifiers are constructed from threadIdx.x. More...
 
Summation reductions
__device__ __forceinline__ T Sum (T input)
 Computes a warp-wide sum in the calling warp. The output is valid in warp lane0. More...
 
__device__ __forceinline__ T Sum (T input, int valid_items)
 Computes a partially-full warp-wide sum in the calling warp. The output is valid in warp lane0. More...
 
template<typename FlagT >
__device__ __forceinline__ T HeadSegmentedSum (T input, FlagT head_flag)
 Computes a segmented sum in the calling warp where segments are defined by head-flags. The sum of each segment is returned to the first lane in that segment (which always includes lane0). More...
 
template<typename FlagT >
__device__ __forceinline__ T TailSegmentedSum (T input, FlagT tail_flag)
 Computes a segmented sum in the calling warp where segments are defined by tail-flags. The sum of each segment is returned to the first lane in that segment (which always includes lane0). More...
 
Generic reductions
template<typename ReductionOp >
__device__ __forceinline__ T Reduce (T input, ReductionOp reduction_op)
 Computes a warp-wide reduction in the calling warp using the specified binary reduction functor. The output is valid in warp lane0. More...
 
template<typename ReductionOp >
__device__ __forceinline__ T Reduce (T input, ReductionOp reduction_op, int valid_items)
 Computes a partially-full warp-wide reduction in the calling warp using the specified binary reduction functor. The output is valid in warp lane0. More...
 
template<typename ReductionOp , typename FlagT >
__device__ __forceinline__ T HeadSegmentedReduce (T input, FlagT head_flag, ReductionOp reduction_op)
 Computes a segmented reduction in the calling warp where segments are defined by head-flags. The reduction of each segment is returned to the first lane in that segment (which always includes lane0). More...
 
template<typename ReductionOp , typename FlagT >
__device__ __forceinline__ T TailSegmentedReduce (T input, FlagT tail_flag, ReductionOp reduction_op)
 Computes a segmented reduction in the calling warp where segments are defined by tail-flags. The reduction of each segment is returned to the first lane in that segment (which always includes lane0). More...
 

Constructor & Destructor Documentation

template<typename T , int LOGICAL_WARP_THREADS = CUB_PTX_WARP_THREADS, int PTX_ARCH = CUB_PTX_ARCH>
__device__ __forceinline__ cub::WarpReduce< T, LOGICAL_WARP_THREADS, PTX_ARCH >::WarpReduce ( TempStorage temp_storage)
inline

Collective constructor using the specified memory allocation as temporary storage. Logical warp and lane identifiers are constructed from threadIdx.x.

Parameters
[in]temp_storageReference to memory allocation having layout type TempStorage

Member Function Documentation

template<typename T , int LOGICAL_WARP_THREADS = CUB_PTX_WARP_THREADS, int PTX_ARCH = CUB_PTX_ARCH>
__device__ __forceinline__ T cub::WarpReduce< T, LOGICAL_WARP_THREADS, PTX_ARCH >::Sum ( input)
inline

Computes a warp-wide sum in the calling warp. The output is valid in warp lane0.

A subsequent __syncthreads() threadblock barrier should be invoked after calling this method if the collective's temporary storage (e.g., temp_storage) is to be reused or repurposed.

Snippet
The code snippet below illustrates four concurrent warp sum reductions within a block of 128 threads (one per each of the 32-thread warps).
#include <cub/cub.cuh>
__global__ void ExampleKernel(...)
{
// Specialize WarpReduce for type int
// Allocate WarpReduce shared memory for 4 warps
__shared__ typename WarpReduce::TempStorage temp_storage[4];
// Obtain one input item per thread
int thread_data = ...
// Return the warp-wide sums to each lane0
int warp_id = threadIdx.x / 32;
int aggregate = WarpReduce(temp_storage[warp_id]).Sum(thread_data);
Suppose the set of input thread_data across the block of threads is {0, 1, 2, 3, ..., 127}. The corresponding output aggregate in threads 0, 32, 64, and 96 will 496, 1520, 2544, and 3568, respectively (and is undefined in other threads).
Parameters
[in]inputCalling thread's input
template<typename T , int LOGICAL_WARP_THREADS = CUB_PTX_WARP_THREADS, int PTX_ARCH = CUB_PTX_ARCH>
__device__ __forceinline__ T cub::WarpReduce< T, LOGICAL_WARP_THREADS, PTX_ARCH >::Sum ( input,
int  valid_items 
)
inline

Computes a partially-full warp-wide sum in the calling warp. The output is valid in warp lane0.

All threads across the calling warp must agree on the same value for valid_items. Otherwise the result is undefined.

A subsequent __syncthreads() threadblock barrier should be invoked after calling this method if the collective's temporary storage (e.g., temp_storage) is to be reused or repurposed.

Snippet
The code snippet below illustrates a sum reduction within a single, partially-full block of 32 threads (one warp).
#include <cub/cub.cuh>
__global__ void ExampleKernel(int *d_data, int valid_items)
{
// Specialize WarpReduce for type int
// Allocate WarpReduce shared memory for one warp
__shared__ typename WarpReduce::TempStorage temp_storage;
// Obtain one input item per thread if in range
int thread_data;
if (threadIdx.x < valid_items)
thread_data = d_data[threadIdx.x];
// Return the warp-wide sums to each lane0
int aggregate = WarpReduce(temp_storage).Sum(
thread_data, valid_items);
Suppose the input d_data is {0, 1, 2, 3, 4, ... and valid_items is 4. The corresponding output aggregate in thread0 is 6 (and is undefined in other threads).
Parameters
[in]inputCalling thread's input
[in]valid_itemsTotal number of valid items in the calling thread's logical warp (may be less than LOGICAL_WARP_THREADS)
template<typename T , int LOGICAL_WARP_THREADS = CUB_PTX_WARP_THREADS, int PTX_ARCH = CUB_PTX_ARCH>
template<typename FlagT >
__device__ __forceinline__ T cub::WarpReduce< T, LOGICAL_WARP_THREADS, PTX_ARCH >::HeadSegmentedSum ( input,
FlagT  head_flag 
)
inline

Computes a segmented sum in the calling warp where segments are defined by head-flags. The sum of each segment is returned to the first lane in that segment (which always includes lane0).

A subsequent __syncthreads() threadblock barrier should be invoked after calling this method if the collective's temporary storage (e.g., temp_storage) is to be reused or repurposed.

Snippet
The code snippet below illustrates a head-segmented warp sum reduction within a block of 32 threads (one warp).
#include <cub/cub.cuh>
__global__ void ExampleKernel(...)
{
// Specialize WarpReduce for type int
// Allocate WarpReduce shared memory for one warp
__shared__ typename WarpReduce::TempStorage temp_storage;
// Obtain one input item and flag per thread
int thread_data = ...
int head_flag = ...
// Return the warp-wide sums to each lane0
int aggregate = WarpReduce(temp_storage).HeadSegmentedSum(
thread_data, head_flag);
Suppose the set of input thread_data and head_flag across the block of threads is {0, 1, 2, 3, ..., 31 and is {1, 0, 0, 0, 1, 0, 0, 0, ..., 1, 0, 0, 0, respectively. The corresponding output aggregate in threads 0, 4, 8, etc. will be 6, 22, 38, etc. (and is undefined in other threads).
Template Parameters
ReductionOp[inferred] Binary reduction operator type having member T operator()(const T &a, const T &b)
Parameters
[in]inputCalling thread's input
[in]head_flagHead flag denoting whether or not input is the start of a new segment
template<typename T , int LOGICAL_WARP_THREADS = CUB_PTX_WARP_THREADS, int PTX_ARCH = CUB_PTX_ARCH>
template<typename FlagT >
__device__ __forceinline__ T cub::WarpReduce< T, LOGICAL_WARP_THREADS, PTX_ARCH >::TailSegmentedSum ( input,
FlagT  tail_flag 
)
inline

Computes a segmented sum in the calling warp where segments are defined by tail-flags. The sum of each segment is returned to the first lane in that segment (which always includes lane0).

A subsequent __syncthreads() threadblock barrier should be invoked after calling this method if the collective's temporary storage (e.g., temp_storage) is to be reused or repurposed.

Snippet
The code snippet below illustrates a tail-segmented warp sum reduction within a block of 32 threads (one warp).
#include <cub/cub.cuh>
__global__ void ExampleKernel(...)
{
// Specialize WarpReduce for type int
// Allocate WarpReduce shared memory for one warp
__shared__ typename WarpReduce::TempStorage temp_storage;
// Obtain one input item and flag per thread
int thread_data = ...
int tail_flag = ...
// Return the warp-wide sums to each lane0
int aggregate = WarpReduce(temp_storage).TailSegmentedSum(
thread_data, tail_flag);
Suppose the set of input thread_data and tail_flag across the block of threads is {0, 1, 2, 3, ..., 31 and is {0, 0, 0, 1, 0, 0, 0, 1, ..., 0, 0, 0, 1, respectively. The corresponding output aggregate in threads 0, 4, 8, etc. will be 6, 22, 38, etc. (and is undefined in other threads).
Template Parameters
ReductionOp[inferred] Binary reduction operator type having member T operator()(const T &a, const T &b)
Parameters
[in]inputCalling thread's input
[in]tail_flagHead flag denoting whether or not input is the start of a new segment
template<typename T , int LOGICAL_WARP_THREADS = CUB_PTX_WARP_THREADS, int PTX_ARCH = CUB_PTX_ARCH>
template<typename ReductionOp >
__device__ __forceinline__ T cub::WarpReduce< T, LOGICAL_WARP_THREADS, PTX_ARCH >::Reduce ( input,
ReductionOp  reduction_op 
)
inline

Computes a warp-wide reduction in the calling warp using the specified binary reduction functor. The output is valid in warp lane0.

Supports non-commutative reduction operators

A subsequent __syncthreads() threadblock barrier should be invoked after calling this method if the collective's temporary storage (e.g., temp_storage) is to be reused or repurposed.

Snippet
The code snippet below illustrates four concurrent warp max reductions within a block of 128 threads (one per each of the 32-thread warps).
#include <cub/cub.cuh>
__global__ void ExampleKernel(...)
{
// Specialize WarpReduce for type int
// Allocate WarpReduce shared memory for 4 warps
__shared__ typename WarpReduce::TempStorage temp_storage[4];
// Obtain one input item per thread
int thread_data = ...
// Return the warp-wide reductions to each lane0
int warp_id = threadIdx.x / 32;
int aggregate = WarpReduce(temp_storage[warp_id]).Reduce(
thread_data, cub::Max());
Suppose the set of input thread_data across the block of threads is {0, 1, 2, 3, ..., 127}. The corresponding output aggregate in threads 0, 32, 64, and 96 will 31, 63, 95, and 127, respectively (and is undefined in other threads).
Template Parameters
ReductionOp[inferred] Binary reduction operator type having member T operator()(const T &a, const T &b)
Parameters
[in]inputCalling thread's input
[in]reduction_opBinary reduction operator
template<typename T , int LOGICAL_WARP_THREADS = CUB_PTX_WARP_THREADS, int PTX_ARCH = CUB_PTX_ARCH>
template<typename ReductionOp >
__device__ __forceinline__ T cub::WarpReduce< T, LOGICAL_WARP_THREADS, PTX_ARCH >::Reduce ( input,
ReductionOp  reduction_op,
int  valid_items 
)
inline

Computes a partially-full warp-wide reduction in the calling warp using the specified binary reduction functor. The output is valid in warp lane0.

All threads across the calling warp must agree on the same value for valid_items. Otherwise the result is undefined.

Supports non-commutative reduction operators

A subsequent __syncthreads() threadblock barrier should be invoked after calling this method if the collective's temporary storage (e.g., temp_storage) is to be reused or repurposed.

Snippet
The code snippet below illustrates a max reduction within a single, partially-full block of 32 threads (one warp).
#include <cub/cub.cuh>
__global__ void ExampleKernel(int *d_data, int valid_items)
{
// Specialize WarpReduce for type int
// Allocate WarpReduce shared memory for one warp
__shared__ typename WarpReduce::TempStorage temp_storage;
// Obtain one input item per thread if in range
int thread_data;
if (threadIdx.x < valid_items)
thread_data = d_data[threadIdx.x];
// Return the warp-wide reductions to each lane0
int aggregate = WarpReduce(temp_storage).Reduce(
thread_data, cub::Max(), valid_items);
Suppose the input d_data is {0, 1, 2, 3, 4, ... and valid_items is 4. The corresponding output aggregate in thread0 is 3 (and is undefined in other threads).
Template Parameters
ReductionOp[inferred] Binary reduction operator type having member T operator()(const T &a, const T &b)
Parameters
[in]inputCalling thread's input
[in]reduction_opBinary reduction operator
[in]valid_itemsTotal number of valid items in the calling thread's logical warp (may be less than LOGICAL_WARP_THREADS)
template<typename T , int LOGICAL_WARP_THREADS = CUB_PTX_WARP_THREADS, int PTX_ARCH = CUB_PTX_ARCH>
template<typename ReductionOp , typename FlagT >
__device__ __forceinline__ T cub::WarpReduce< T, LOGICAL_WARP_THREADS, PTX_ARCH >::HeadSegmentedReduce ( input,
FlagT  head_flag,
ReductionOp  reduction_op 
)
inline

Computes a segmented reduction in the calling warp where segments are defined by head-flags. The reduction of each segment is returned to the first lane in that segment (which always includes lane0).

Supports non-commutative reduction operators

A subsequent __syncthreads() threadblock barrier should be invoked after calling this method if the collective's temporary storage (e.g., temp_storage) is to be reused or repurposed.

Snippet
The code snippet below illustrates a head-segmented warp max reduction within a block of 32 threads (one warp).
#include <cub/cub.cuh>
__global__ void ExampleKernel(...)
{
// Specialize WarpReduce for type int
// Allocate WarpReduce shared memory for one warp
__shared__ typename WarpReduce::TempStorage temp_storage;
// Obtain one input item and flag per thread
int thread_data = ...
int head_flag = ...
// Return the warp-wide reductions to each lane0
int aggregate = WarpReduce(temp_storage).HeadSegmentedReduce(
thread_data, head_flag, cub::Max());
Suppose the set of input thread_data and head_flag across the block of threads is {0, 1, 2, 3, ..., 31 and is {1, 0, 0, 0, 1, 0, 0, 0, ..., 1, 0, 0, 0, respectively. The corresponding output aggregate in threads 0, 4, 8, etc. will be 3, 7, 11, etc. (and is undefined in other threads).
Template Parameters
ReductionOp[inferred] Binary reduction operator type having member T operator()(const T &a, const T &b)
Parameters
[in]inputCalling thread's input
[in]head_flagHead flag denoting whether or not input is the start of a new segment
[in]reduction_opReduction operator
template<typename T , int LOGICAL_WARP_THREADS = CUB_PTX_WARP_THREADS, int PTX_ARCH = CUB_PTX_ARCH>
template<typename ReductionOp , typename FlagT >
__device__ __forceinline__ T cub::WarpReduce< T, LOGICAL_WARP_THREADS, PTX_ARCH >::TailSegmentedReduce ( input,
FlagT  tail_flag,
ReductionOp  reduction_op 
)
inline

Computes a segmented reduction in the calling warp where segments are defined by tail-flags. The reduction of each segment is returned to the first lane in that segment (which always includes lane0).

Supports non-commutative reduction operators

A subsequent __syncthreads() threadblock barrier should be invoked after calling this method if the collective's temporary storage (e.g., temp_storage) is to be reused or repurposed.

Snippet
The code snippet below illustrates a tail-segmented warp max reduction within a block of 32 threads (one warp).
#include <cub/cub.cuh>
__global__ void ExampleKernel(...)
{
// Specialize WarpReduce for type int
// Allocate WarpReduce shared memory for one warp
__shared__ typename WarpReduce::TempStorage temp_storage;
// Obtain one input item and flag per thread
int thread_data = ...
int tail_flag = ...
// Return the warp-wide reductions to each lane0
int aggregate = WarpReduce(temp_storage).TailSegmentedReduce(
thread_data, tail_flag, cub::Max());
Suppose the set of input thread_data and tail_flag across the block of threads is {0, 1, 2, 3, ..., 31 and is {0, 0, 0, 1, 0, 0, 0, 1, ..., 0, 0, 0, 1, respectively. The corresponding output aggregate in threads 0, 4, 8, etc. will be 3, 7, 11, etc. (and is undefined in other threads).
Template Parameters
ReductionOp[inferred] Binary reduction operator type having member T operator()(const T &a, const T &b)
Parameters
[in]inputCalling thread's input
[in]tail_flagTail flag denoting whether or not input is the end of the current segment
[in]reduction_opReduction operator

The documentation for this class was generated from the following file: