cub::BlockLoad

Defined in /home/runner/work/cccl/cccl/cub/cub/block/block_load.cuh

template<typename InputT, int BLOCK_DIM_X, int ITEMS_PER_THREAD, BlockLoadAlgorithm ALGORITHM = BLOCK_LOAD_DIRECT, int BLOCK_DIM_Y = 1, int BLOCK_DIM_Z = 1, int LEGACY_PTX_ARCH = 0> class BlockLoad

The BlockLoad class provides collective data movement methods for loading a linear segment of items from memory into a blocked arrangement across a CUDA thread block.

Overview

The BlockLoad class provides a single data movement abstraction that can be specialized to implement different cub::BlockLoadAlgorithm strategies. This facilitates different performance policies for different architectures, data types, granularity sizes, etc.
BlockLoad can be optionally specialized by different data movement strategies:
1. cub::BLOCK_LOAD_DIRECT: A blocked arrangement of data is read directly from memory.
2. cub::BLOCK_LOAD_STRIPED: A striped arrangement of data is read directly from memory.
3. cub::BLOCK_LOAD_VECTORIZE: A blocked arrangement of data is read directly from memory using CUDA’s built-in vectorized loads as a coalescing optimization.
4. cub::BLOCK_LOAD_TRANSPOSE: A striped arrangement of data is read directly from memory and is then locally transposed into a blocked arrangement.
5. cub::BLOCK_LOAD_WARP_TRANSPOSE: A warp-striped arrangement of data is read directly from memory and is then locally transposed into a blocked arrangement.
6. cub::BLOCK_LOAD_WARP_TRANSPOSE_TIMESLICED: A warp-striped arrangement of data is read directly from memory and is then locally transposed into a blocked arrangement one warp at a time.
For multi-dimensional blocks, threads are linearly ranked in row-major order.

A Simple Example

Every thread in the block uses the BlockLoad class by first specializing the BlockLoad type, then instantiating an instance with parameters for communication, and finally invoking one or more collective member functions.

The code snippet below illustrates the loading of a linear segment of 512 integers into a “blocked” arrangement across 128 threads where each thread owns 4 consecutive items. The load is specialized for BLOCK_LOAD_WARP_TRANSPOSE, meaning memory references are efficiently coalesced using a warp-striped access pattern (after which items are locally reordered among threads).

#include <cub/cub.cuh>   // or equivalently <cub/block/block_load.cuh>

__global__ void ExampleKernel(int *d_data, ...)
{
    // Specialize BlockLoad for a 1D block of 128 threads owning 4 integer items each
    using BlockLoad = cub::BlockLoad<int, 128, 4, BLOCK_LOAD_WARP_TRANSPOSE>;

    // Allocate shared memory for BlockLoad
    __shared__ typename BlockLoad::TempStorage temp_storage;

    // Load a segment of consecutive items that are blocked across threads
    int thread_data[4];
    BlockLoad(temp_storage).Load(d_data, thread_data);

Suppose the input d_data is 0, 1, 2, 3, 4, 5, .... The set of thread_data across the block of threads in those threads will be { [0,1,2,3], [4,5,6,7], ..., [508,509,510,511] }.

Re-using dynamically allocating shared memory

The block/example_block_reduce_dyn_smem.cu example illustrates usage of dynamically shared memory with BlockReduce and how to re-purpose the same memory region. This example can be easily adapted to the storage required by BlockLoad.

Template Parameters

InputT – The data type to read into (which must be convertible from the input iterator’s value type).
BLOCK_DIM_X – The thread block length in threads along the X dimension
ITEMS_PER_THREAD – The number of consecutive items partitioned onto each thread.
ALGORITHM – [optional] cub::BlockLoadAlgorithm tuning policy. default: cub::BLOCK_LOAD_DIRECT.
WARP_TIME_SLICING – [optional] Whether or not only one warp’s worth of shared memory should be allocated and time-sliced among block-warps during any load-related data transpositions (versus each warp having its own storage). (default: false)
BLOCK_DIM_Y – [optional] The thread block length in threads along the Y dimension (default: 1)
BLOCK_DIM_Z – [optional] The thread block length in threads along the Z dimension (default: 1)
LEGACY_PTX_ARCH – [optional] Unused.

Collective constructors

inline BlockLoad(): Collective constructor using a private static allocation of shared memory as temporary storage.

inline BlockLoad(TempStorage &temp_storage)

Collective constructor using the specified memory allocation as temporary storage.

Parameters: temp_storage – [in] Reference to memory allocation having layout type TempStorage

Data movement

template<typename InputIteratorT> inline void Load(InputIteratorT block_itr, InputT (&items)[ITEMS_PER_THREAD])

Load a linear segment of items from memory.

Assumes a blocked arrangement of (block-threads * items-per-thread) items across the thread block, where thread_i owns the i^th range of items-per-thread contiguous items. For multi-dimensional thread blocks, a row-major thread ordering is assumed.
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 the loading of a linear segment of 512 integers into a “blocked” arrangement across 128 threads where each thread owns 4 consecutive items. The load is specialized for BLOCK_LOAD_WARP_TRANSPOSE, meaning memory references are efficiently coalesced using a warp-striped access pattern (after which items are locally reordered among threads).

#include <cub/cub.cuh>   // or equivalently <cub/block/block_load.cuh>

__global__ void ExampleKernel(int *d_data, ...)
{
    // Specialize BlockLoad for a 1D block of 128 threads owning 4 integer items each
    using BlockLoad = cub::BlockLoad<int, 128, 4, BLOCK_LOAD_WARP_TRANSPOSE>;

    // Allocate shared memory for BlockLoad
    __shared__ typename BlockLoad::TempStorage temp_storage;

    // Load a segment of consecutive items that are blocked across threads
    int thread_data[4];
    BlockLoad(temp_storage).Load(d_data, thread_data);

Suppose the input d_data is 0, 1, 2, 3, 4, 5, .... The set of thread_data across the block of threads in those threads will be { [0,1,2,3], [4,5,6,7], ..., [508,509,510,511] }.

Parameters

block_itr – [in] The thread block’s base input iterator for loading from
items – [out] Data to load

template<typename InputIteratorT> inline void Load(InputIteratorT block_itr, InputT (&items)[ITEMS_PER_THREAD], int valid_items)

Load a linear segment of items from memory, guarded by range.

Assumes a blocked arrangement of (block-threads * items-per-thread) items across the thread block, where thread_i owns the i^th range of items-per-thread contiguous items. For multi-dimensional thread blocks, a row-major thread ordering is assumed.
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 the guarded loading of a linear segment of 512 integers into a “blocked” arrangement across 128 threads where each thread owns 4 consecutive items. The load is specialized for BLOCK_LOAD_WARP_TRANSPOSE, meaning memory references are efficiently coalesced using a warp-striped access pattern (after which items are locally reordered among threads).

#include <cub/cub.cuh>   // or equivalently <cub/block/block_load.cuh>

__global__ void ExampleKernel(int *d_data, int valid_items, ...)
{
    // Specialize BlockLoad for a 1D block of 128 threads owning 4 integer items each
    using BlockLoad = cub::BlockLoad<int, 128, 4, BLOCK_LOAD_WARP_TRANSPOSE>;

    // Allocate shared memory for BlockLoad
    __shared__ typename BlockLoad::TempStorage temp_storage;

    // Load a segment of consecutive items that are blocked across threads
    int thread_data[4];
    BlockLoad(temp_storage).Load(d_data, thread_data, valid_items);

Suppose the input d_data is 0, 1, 2, 3, 4, 5, 6... and valid_items is 5. The set of thread_data across the block of threads in those threads will be { [0,1,2,3], [4,?,?,?], ..., [?,?,?,?] }, with only the first two threads being unmasked to load portions of valid data (and other items remaining unassigned).

Parameters

block_itr – [in] The thread block’s base input iterator for loading from
items – [out] Data to load
valid_items – [in] Number of valid items to load

template<typename InputIteratorT, typename DefaultT> inline void Load(InputIteratorT block_itr, InputT (&items)[ITEMS_PER_THREAD], int valid_items, DefaultT oob_default)

Load a linear segment of items from memory, guarded by range, with a fall-back assignment of out-of-bound elements

Assumes a blocked arrangement of (block-threads * items-per-thread) items across the thread block, where thread_i owns the i^th range of items-per-thread contiguous items. For multi-dimensional thread blocks, a row-major thread ordering is assumed.
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 the guarded loading of a linear segment of 512 integers into a “blocked” arrangement across 128 threads where each thread owns 4 consecutive items. The load is specialized for BLOCK_LOAD_WARP_TRANSPOSE, meaning memory references are efficiently coalesced using a warp-striped access pattern (after which items are locally reordered among threads).

#include <cub/cub.cuh>   // or equivalently <cub/block/block_load.cuh>

__global__ void ExampleKernel(int *d_data, int valid_items, ...)
{
    // Specialize BlockLoad for a 1D block of 128 threads owning 4 integer items each
    using BlockLoad = cub::BlockLoad<int, 128, 4, BLOCK_LOAD_WARP_TRANSPOSE>;

    // Allocate shared memory for BlockLoad
    __shared__ typename BlockLoad::TempStorage temp_storage;

    // Load a segment of consecutive items that are blocked across threads
    int thread_data[4];
    BlockLoad(temp_storage).Load(d_data, thread_data, valid_items, -1);

Suppose the input d_data is 0, 1, 2, 3, 4, 5, 6... valid_items is 5, and the out-of-bounds default is -1. The set of thread_data across the block of threads in those threads will be { [0,1,2,3], [4,-1,-1,-1], ..., [-1,-1,-1,-1] }, with only the first two threads being unmasked to load portions of valid data (and other items are assigned -1)

Parameters

block_itr – [in] The thread block’s base input iterator for loading from
items – [out] Data to load
valid_items – [in] Number of valid items to load
oob_default – [in] Default value to assign out-of-bound items

struct TempStorage : public Uninitialized<_TempStorage>: The operations exposed by BlockLoad 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.