Load#

Class#

template<class T, unsigned int ItemsPerThread, unsigned int WarpSize = ::rocprim::device_warp_size(), warp_load_method Method = warp_load_method::warp_load_direct>
class warp_load#

The warp_load class is a warp level parallel primitive which provides methods for loading data from continuous memory into a blocked arrangement of items across a warp.

Overview

Example:

In the example a load operation is performed on a warp of 8 threads, using type int and 4 items per thread.

__global__ void example_kernel(int * input, ...)
{
    constexpr unsigned int threads_per_block = 128;
    constexpr unsigned int threads_per_warp  =   8;
    constexpr unsigned int items_per_thread  =   4;
    constexpr unsigned int warps_per_block   = threads_per_block / threads_per_warp;
    const unsigned int warp_id = hipThreadIdx_x / threads_per_warp;
    const int offset = blockIdx.x * threads_per_block * items_per_thread
        + warp_id * threads_per_warp * items_per_thread;
    int items[items_per_thread];
    rocprim::warp_load<int, items_per_thread, threads_per_warp, load_method> warp_load;
    warp_load.load(input + offset, items);
    ...
}

Template Parameters:
  • T – - the input/output type.

  • ItemsPerThread – - the number of items to be processed by each thread.

  • WarpSize – - the number of threads in the warp. It must be a divisor of the kernel block size.

  • Method – - the method to load data.

Public Types

using storage_type = storage_type_#

Struct used to allocate a temporary memory that is required for thread communication during operations provided by related parallel primitive.

Depending on the implemention the operations exposed by parallel primitive may require a temporary storage for thread communication. The storage should be allocated using keywords . It can be aliased to an externally allocated memory, or be a part of a union with other storage types to increase shared memory reusability.

Public Functions

template<class InputIterator>
__device__ inline void load(InputIterator input, T (&items)[ItemsPerThread], storage_type&)#

Loads data from continuous memory into an arrangement of items across the warp.

Overview

  • The type T must be such that an object of type InputIterator can be dereferenced and then implicitly converted to T.

Template Parameters:

InputIterator – - [inferred] an iterator type for input (can be a simple pointer.

Parameters:
  • input[in] - the input iterator to load from.

  • items[out] - array that data is loaded to.

  • storage[in] - temporary storage for inputs.

template<class InputIterator>
__device__ inline void load(InputIterator input, T (&items)[ItemsPerThread], unsigned int valid, storage_type&)#

Loads data from continuous memory into an arrangement of items across the warp.

Overview

  • The type T must be such that an object of type InputIterator can be dereferenced and then implicitly converted to T.

Template Parameters:

InputIterator – - [inferred] an iterator type for input (can be a simple pointer.

Parameters:
  • input[in] - the input iterator to load from.

  • items[out] - array that data is loaded to.

  • valid[in] - maximum range of valid numbers to load.

  • storage[in] - temporary storage for inputs.

template<class InputIterator, class Default>
__device__ inline void load(InputIterator input, T (&items)[ItemsPerThread], unsigned int valid, Default out_of_bounds, storage_type&)#

Loads data from continuous memory into an arrangement of items across the warp.

Overview

  • The type T must be such that an object of type InputIterator can be dereferenced and then implicitly converted to T.

Template Parameters:

InputIterator – - [inferred] an iterator type for input (can be a simple pointer.

Parameters:
  • input[in] - the input iterator to load from.

  • items[out] - array that data is loaded to.

  • valid[in] - maximum range of valid numbers to load.

  • out_of_bounds[in] - default value assigned to out-of-bound items.

  • storage[in] - temporary storage for inputs.

Algorithms#

enum rocprim::warp_load_method#

warp_load_method enumerates the methods available to load data from continuous memory into a blocked/striped arrangement of items across the warp

Values:

enumerator warp_load_direct#

Data from continuous memory is loaded into a blocked arrangement of items.

Performance Notes:

  • Performance decreases with increasing number of items per thread (stride between reads), because of reduced memory coalescing.

enumerator warp_load_striped#

A striped arrangement of data is read directly from memory.

enumerator warp_load_vectorize#

Data from continuous memory is loaded into a blocked arrangement of items using vectorization as an optimization.

Performance Notes:

  • Performance remains high due to increased memory coalescing, provided that vectorization requirements are fulfilled. Otherwise, performance will default to warp_load_direct.

Requirements:

  • The input offset (block_input) must be quad-item aligned.

  • The following conditions will prevent vectorization and switch to default warp_load_direct:

    • ItemsPerThread is odd.

    • The datatype T is not a primitive or a HIP vector type (e.g. int2, int4, etc.

enumerator warp_load_transpose#

A striped arrangement of data from continuous memory is locally transposed into a blocked arrangement of items.

Performance Notes:

  • Performance remains high due to increased memory coalescing, regardless of the number of items per thread.

  • Performance may be better compared to warp_load_direct and warp_load_vectorize due to reordering on local memory.

enumerator default_method#

Defaults to warp_load_direct.