Alien-XGBoost
view release on metacpan or search on metacpan
xgboost/cub/cub/block/block_load.cuh view on Meta::CPAN
/******************************************************************************
* Copyright (c) 2011, Duane Merrill. All rights reserved.
* Copyright (c) 2011-2016, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
******************************************************************************/
/**
* \file
* Operations for reading linear tiles of data into the CUDA thread block.
*/
#pragma once
#include <iterator>
#include "block_exchange.cuh"
#include "../iterator/cache_modified_input_iterator.cuh"
#include "../util_ptx.cuh"
#include "../util_macro.cuh"
#include "../util_type.cuh"
#include "../util_namespace.cuh"
/// Optional outer namespace(s)
CUB_NS_PREFIX
/// CUB namespace
namespace cub {
/**
* \addtogroup UtilIo
* @{
*/
/******************************************************************//**
* \name Blocked arrangement I/O (direct)
*********************************************************************/
//@{
/**
* \brief Load a linear segment of items into a blocked arrangement across the thread block.
*
* \blocked
*
* \tparam T <b>[inferred]</b> The data type to load.
* \tparam ITEMS_PER_THREAD <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
* \tparam InputIteratorT <b>[inferred]</b> The random-access iterator type for input \iterator.
*/
template <
typename InputT,
int ITEMS_PER_THREAD,
typename InputIteratorT>
__device__ __forceinline__ void LoadDirectBlocked(
int linear_tid, ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
{
InputIteratorT thread_itr = block_itr + (linear_tid * ITEMS_PER_THREAD);
// Load directly in thread-blocked order
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
{
items[ITEM] = thread_itr[ITEM];
}
}
/**
* \brief Load a linear segment of items into a blocked arrangement across the thread block, guarded by range.
*
* \blocked
*
* \tparam T <b>[inferred]</b> The data type to load.
* \tparam ITEMS_PER_THREAD <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
* \tparam InputIteratorT <b>[inferred]</b> The random-access iterator type for input \iterator.
*/
template <
typename InputT,
int ITEMS_PER_THREAD,
typename InputIteratorT>
__device__ __forceinline__ void LoadDirectBlocked(
int linear_tid, ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
int valid_items) ///< [in] Number of valid items to load
{
InputIteratorT thread_itr = block_itr + (linear_tid * ITEMS_PER_THREAD);
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
{
if ((linear_tid * ITEMS_PER_THREAD) + ITEM < valid_items)
{
items[ITEM] = thread_itr[ITEM];
}
}
}
/**
* \brief Load a linear segment of items into a blocked arrangement across the thread block, guarded by range, with a fall-back assignment of out-of-bound elements..
*
* \blocked
*
* \tparam T <b>[inferred]</b> The data type to load.
* \tparam ITEMS_PER_THREAD <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
* \tparam InputIteratorT <b>[inferred]</b> The random-access iterator type for input \iterator.
*/
template <
typename InputT,
typename DefaultT,
int ITEMS_PER_THREAD,
typename InputIteratorT>
__device__ __forceinline__ void LoadDirectBlocked(
int linear_tid, ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
int valid_items, ///< [in] Number of valid items to load
DefaultT oob_default) ///< [in] Default value to assign out-of-bound items
{
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
items[ITEM] = oob_default;
LoadDirectBlocked(linear_tid, block_itr, items, valid_items);
}
#ifndef DOXYGEN_SHOULD_SKIP_THIS // Do not document
/**
* Internal implementation for load vectorization
*/
template <
CacheLoadModifier MODIFIER,
typename T,
int ITEMS_PER_THREAD>
__device__ __forceinline__ void InternalLoadDirectBlockedVectorized(
int linear_tid, ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
T *block_ptr, ///< [in] Input pointer for loading from
T (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
{
// Biggest memory access word that T is a whole multiple of
typedef typename UnitWord<T>::DeviceWord DeviceWord;
enum
{
TOTAL_WORDS = sizeof(items) / sizeof(DeviceWord),
VECTOR_SIZE = (TOTAL_WORDS % 4 == 0) ?
4 :
(TOTAL_WORDS % 2 == 0) ?
2 :
1,
VECTORS_PER_THREAD = TOTAL_WORDS / VECTOR_SIZE,
};
// Vector type
typedef typename CubVector<DeviceWord, VECTOR_SIZE>::Type Vector;
// Vector items
Vector vec_items[VECTORS_PER_THREAD];
// Aliased input ptr
Vector* vec_ptr = reinterpret_cast<Vector*>(block_ptr) + (linear_tid * VECTORS_PER_THREAD);
// Load directly in thread-blocked order
#pragma unroll
for (int ITEM = 0; ITEM < VECTORS_PER_THREAD; ITEM++)
{
vec_items[ITEM] = ThreadLoad<MODIFIER>(vec_ptr + ITEM);
}
// Copy
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
{
items[ITEM] = *(reinterpret_cast<T*>(vec_items) + ITEM);
}
}
#endif // DOXYGEN_SHOULD_SKIP_THIS
/**
* \brief Load a linear segment of items into a blocked arrangement across the thread block.
*
* \blocked
*
* The input offset (\p block_ptr + \p block_offset) must be quad-item aligned
*
* The following conditions will prevent vectorization and loading will fall back to cub::BLOCK_LOAD_DIRECT:
* - \p ITEMS_PER_THREAD is odd
* - The data type \p T is not a built-in primitive or CUDA vector type (e.g., \p short, \p int2, \p double, \p float2, etc.)
*
* \tparam T <b>[inferred]</b> The data type to load.
* \tparam ITEMS_PER_THREAD <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
*/
template <
typename T,
int ITEMS_PER_THREAD>
__device__ __forceinline__ void LoadDirectBlockedVectorized(
int linear_tid, ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
T *block_ptr, ///< [in] Input pointer for loading from
T (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
{
InternalLoadDirectBlockedVectorized<LOAD_DEFAULT>(linear_tid, block_ptr, items);
}
//@} end member group
/******************************************************************//**
* \name Striped arrangement I/O (direct)
*********************************************************************/
//@{
/**
* \brief Load a linear segment of items into a striped arrangement across the thread block.
*
* \striped
*
* \tparam BLOCK_THREADS The thread block size in threads
* \tparam T <b>[inferred]</b> The data type to load.
* \tparam ITEMS_PER_THREAD <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
* \tparam InputIteratorT <b>[inferred]</b> The random-access iterator type for input \iterator.
*/
template <
int BLOCK_THREADS,
typename InputT,
int ITEMS_PER_THREAD,
typename InputIteratorT>
__device__ __forceinline__ void LoadDirectStriped(
int linear_tid, ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
{
InputIteratorT thread_itr = block_itr + linear_tid;
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
{
items[ITEM] = thread_itr[ITEM * BLOCK_THREADS];
}
}
/**
* \brief Load a linear segment of items into a striped arrangement across the thread block, guarded by range
*
* \striped
*
* \tparam BLOCK_THREADS The thread block size in threads
* \tparam T <b>[inferred]</b> The data type to load.
* \tparam ITEMS_PER_THREAD <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
* \tparam InputIteratorT <b>[inferred]</b> The random-access iterator type for input \iterator.
*/
template <
int BLOCK_THREADS,
typename InputT,
int ITEMS_PER_THREAD,
typename InputIteratorT>
__device__ __forceinline__ void LoadDirectStriped(
int linear_tid, ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
int valid_items) ///< [in] Number of valid items to load
xgboost/cub/cub/block/block_load.cuh view on Meta::CPAN
InputIteratorT thread_itr = block_itr + warp_offset + tid ;
// Load directly in warp-striped order
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
{
if (warp_offset + tid + (ITEM * CUB_PTX_WARP_THREADS) < valid_items)
{
items[ITEM] = thread_itr[(ITEM * CUB_PTX_WARP_THREADS)];
}
}
}
/**
* \brief Load a linear segment of items into a warp-striped arrangement across the thread block, guarded by range, with a fall-back assignment of out-of-bound elements.
*
* \warpstriped
*
* \par Usage Considerations
* The number of threads in the thread block must be a multiple of the architecture's warp size.
*
* \tparam T <b>[inferred]</b> The data type to load.
* \tparam ITEMS_PER_THREAD <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
* \tparam InputIteratorT <b>[inferred]</b> The random-access iterator type for input \iterator.
*/
template <
typename InputT,
typename DefaultT,
int ITEMS_PER_THREAD,
typename InputIteratorT>
__device__ __forceinline__ void LoadDirectWarpStriped(
int linear_tid, ///< [in] A suitable 1D thread-identifier for the calling thread (e.g., <tt>(threadIdx.y * blockDim.x) + linear_tid</tt> for 2D thread blocks)
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
int valid_items, ///< [in] Number of valid items to load
DefaultT oob_default) ///< [in] Default value to assign out-of-bound items
{
// Load directly in warp-striped order
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
items[ITEM] = oob_default;
LoadDirectWarpStriped(linear_tid, block_itr, items, valid_items);
}
//@} end member group
/** @} */ // end group UtilIo
//-----------------------------------------------------------------------------
// Generic BlockLoad abstraction
//-----------------------------------------------------------------------------
/**
* \brief cub::BlockLoadAlgorithm enumerates alternative algorithms for cub::BlockLoad to read a linear segment of data from memory into a blocked arrangement across a CUDA thread block.
*/
/**
* \brief cub::BlockLoadAlgorithm enumerates alternative algorithms for cub::BlockLoad to read a linear segment of data from memory into a blocked arrangement across a CUDA thread block.
*/
enum BlockLoadAlgorithm
{
/**
* \par Overview
*
* A [<em>blocked arrangement</em>](index.html#sec5sec3) of data is read
* directly from memory.
*
* \par Performance Considerations
* - The utilization of memory transactions (coalescing) decreases as the
* access stride between threads increases (i.e., the number items per thread).
*/
BLOCK_LOAD_DIRECT,
/**
* \par Overview
*
* A [<em>blocked arrangement</em>](index.html#sec5sec3) of data is read
* from memory using CUDA's built-in vectorized loads as a coalescing optimization.
* For example, <tt>ld.global.v4.s32</tt> instructions will be generated
* when \p T = \p int and \p ITEMS_PER_THREAD % 4 == 0.
*
* \par Performance Considerations
* - The utilization of memory transactions (coalescing) remains high until the the
* access stride between threads (i.e., the number items per thread) exceeds the
* maximum vector load width (typically 4 items or 64B, whichever is lower).
* - The following conditions will prevent vectorization and loading will fall back to cub::BLOCK_LOAD_DIRECT:
* - \p ITEMS_PER_THREAD is odd
* - The \p InputIteratorTis not a simple pointer type
* - The block input offset is not quadword-aligned
* - The data type \p T is not a built-in primitive or CUDA vector type (e.g., \p short, \p int2, \p double, \p float2, etc.)
*/
BLOCK_LOAD_VECTORIZE,
/**
* \par Overview
*
* A [<em>striped arrangement</em>](index.html#sec5sec3) of data is read
* efficiently from memory and then locally transposed into a
* [<em>blocked arrangement</em>](index.html#sec5sec3).
*
* \par Performance Considerations
* - The utilization of memory transactions (coalescing) remains high regardless
* of items loaded per thread.
* - The local reordering incurs slightly longer latencies and throughput than the
* direct cub::BLOCK_LOAD_DIRECT and cub::BLOCK_LOAD_VECTORIZE alternatives.
*/
BLOCK_LOAD_TRANSPOSE,
/**
* \par Overview
*
* A [<em>warp-striped arrangement</em>](index.html#sec5sec3) of data is
* read efficiently from memory and then locally transposed into a
* [<em>blocked arrangement</em>](index.html#sec5sec3).
*
* \par Usage Considerations
* - BLOCK_THREADS must be a multiple of WARP_THREADS
*
* \par Performance Considerations
* - The utilization of memory transactions (coalescing) remains high regardless
* of items loaded per thread.
* - The local reordering incurs slightly larger latencies than the
* direct cub::BLOCK_LOAD_DIRECT and cub::BLOCK_LOAD_VECTORIZE alternatives.
* - Provisions more shared storage, but incurs smaller latencies than the
* BLOCK_LOAD_WARP_TRANSPOSE_TIMESLICED alternative.
*/
BLOCK_LOAD_WARP_TRANSPOSE,
/**
* \par Overview
*
* Like \p BLOCK_LOAD_WARP_TRANSPOSE, a [<em>warp-striped arrangement</em>](index.html#sec5sec3)
* of data is read directly from memory and then is locally transposed into a
* [<em>blocked arrangement</em>](index.html#sec5sec3). To reduce the shared memory
* requirement, only one warp's worth of shared memory is provisioned and is
* subsequently time-sliced among warps.
*
* \par Usage Considerations
* - BLOCK_THREADS must be a multiple of WARP_THREADS
*
* \par Performance Considerations
* - The utilization of memory transactions (coalescing) remains high regardless
* of items loaded per thread.
* - Provisions less shared memory temporary storage, but incurs larger
* latencies than the BLOCK_LOAD_WARP_TRANSPOSE alternative.
*/
BLOCK_LOAD_WARP_TRANSPOSE_TIMESLICED,
};
/**
* \brief The BlockLoad class provides [<em>collective</em>](index.html#sec0) data movement methods for loading a linear segment of items from memory into a [<em>blocked arrangement</em>](index.html#sec5sec3) across a CUDA thread block. .
* \tparam BLOCK_DIM_X The thread block length in threads along the X dimension
* \tparam ITEMS_PER_THREAD The number of consecutive items partitioned onto each thread.
* \tparam ALGORITHM <b>[optional]</b> cub::BlockLoadAlgorithm tuning policy. default: cub::BLOCK_LOAD_DIRECT.
* \tparam WARP_TIME_SLICING <b>[optional]</b> 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). (defa...
* \tparam BLOCK_DIM_Y <b>[optional]</b> The thread block length in threads along the Y dimension (default: 1)
* \tparam BLOCK_DIM_Z <b>[optional]</b> The thread block length in threads along the Z dimension (default: 1)
* \tparam PTX_ARCH <b>[optional]</b> \ptxversion
*
* \par 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:
* -# <b>cub::BLOCK_LOAD_DIRECT</b>. A [<em>blocked arrangement</em>](index.html#sec5sec3)
* of data is read directly from memory. [More...](\ref cub::BlockLoadAlgorithm)
* -# <b>cub::BLOCK_LOAD_VECTORIZE</b>. A [<em>blocked arrangement</em>](index.html#sec5sec3)
* of data is read directly from memory using CUDA's built-in vectorized loads as a
* coalescing optimization. [More...](\ref cub::BlockLoadAlgorithm)
* -# <b>cub::BLOCK_LOAD_TRANSPOSE</b>. A [<em>striped arrangement</em>](index.html#sec5sec3)
* of data is read directly from memory and is then locally transposed into a
* [<em>blocked arrangement</em>](index.html#sec5sec3). [More...](\ref cub::BlockLoadAlgorithm)
* -# <b>cub::BLOCK_LOAD_WARP_TRANSPOSE</b>. A [<em>warp-striped arrangement</em>](index.html#sec5sec3)
* of data is read directly from memory and is then locally transposed into a
* [<em>blocked arrangement</em>](index.html#sec5sec3). [More...](\ref cub::BlockLoadAlgorithm)
* -# <b>cub::BLOCK_LOAD_WARP_TRANSPOSE_TIMESLICED,</b>. A [<em>warp-striped arrangement</em>](index.html#sec5sec3)
* of data is read directly from memory and is then locally transposed into a
* [<em>blocked arrangement</em>](index.html#sec5sec3) one warp at a time. [More...](\ref cub::BlockLoadAlgorithm)
* - \rowmajor
*
* \par A Simple Example
* \blockcollective{BlockLoad}
* \par
* 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 \p BLOCK_LOAD_WARP_TRANSPOSE,
* meaning memory references are efficiently coalesced using a warp-striped access
* pattern (after which items are locally reordered among threads).
* \par
* \code
* #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
* typedef cub::BlockLoad<int, 128, 4, BLOCK_LOAD_WARP_TRANSPOSE> BlockLoad;
*
* // 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);
*
* \endcode
* \par
* Suppose the input \p d_data is <tt>0, 1, 2, 3, 4, 5, ...</tt>.
* The set of \p thread_data across the block of threads in those threads will be
* <tt>{ [0,1,2,3], [4,5,6,7], ..., [508,509,510,511] }</tt>.
*
*/
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 PTX_ARCH = CUB_PTX_ARCH>
class BlockLoad
{
private:
/******************************************************************************
* Constants and typed definitions
******************************************************************************/
/// Constants
enum
{
/// The thread block size in threads
BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
};
/******************************************************************************
* Algorithmic variants
******************************************************************************/
/// Load helper
template <BlockLoadAlgorithm _POLICY, int DUMMY>
struct LoadInternal;
/**
* BLOCK_LOAD_DIRECT specialization of load helper
*/
template <int DUMMY>
struct LoadInternal<BLOCK_LOAD_DIRECT, DUMMY>
{
/// Shared memory storage layout type
typedef NullType TempStorage;
/// Linear thread-id
int linear_tid;
/// Constructor
__device__ __forceinline__ LoadInternal(
TempStorage &/*temp_storage*/,
int linear_tid)
:
linear_tid(linear_tid)
{}
/// Load a linear segment of items from memory
template <typename InputIteratorT>
__device__ __forceinline__ void Load(
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
{
LoadDirectBlocked(linear_tid, block_itr, items);
}
/// Load a linear segment of items from memory, guarded by range
template <typename InputIteratorT>
__device__ __forceinline__ void Load(
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
int valid_items) ///< [in] Number of valid items to load
{
LoadDirectBlocked(linear_tid, block_itr, items, valid_items);
}
/// Load a linear segment of items from memory, guarded by range, with a fall-back assignment of out-of-bound elements
template <typename InputIteratorT, typename DefaultT>
__device__ __forceinline__ void Load(
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
int valid_items, ///< [in] Number of valid items to load
DefaultT oob_default) ///< [in] Default value to assign out-of-bound items
{
LoadDirectBlocked(linear_tid, block_itr, items, valid_items, oob_default);
}
};
/**
* BLOCK_LOAD_VECTORIZE specialization of load helper
*/
template <int DUMMY>
struct LoadInternal<BLOCK_LOAD_VECTORIZE, DUMMY>
{
/// Shared memory storage layout type
typedef NullType TempStorage;
/// Linear thread-id
int linear_tid;
/// Constructor
__device__ __forceinline__ LoadInternal(
TempStorage &/*temp_storage*/,
int linear_tid)
:
linear_tid(linear_tid)
{}
/// Load a linear segment of items from memory, specialized for native pointer types (attempts vectorization)
template <typename InputIteratorT>
__device__ __forceinline__ void Load(
InputT *block_ptr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
{
InternalLoadDirectBlockedVectorized<LOAD_DEFAULT>(linear_tid, block_ptr, items);
}
/// Load a linear segment of items from memory, specialized for native pointer types (attempts vectorization)
template <typename InputIteratorT>
__device__ __forceinline__ void Load(
const InputT *block_ptr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
{
InternalLoadDirectBlockedVectorized<LOAD_DEFAULT>(linear_tid, block_ptr, items);
}
/// Load a linear segment of items from memory, specialized for native pointer types (attempts vectorization)
template <
CacheLoadModifier MODIFIER,
typename ValueType,
typename OffsetT>
__device__ __forceinline__ void Load(
CacheModifiedInputIterator<MODIFIER, ValueType, OffsetT> block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
{
InternalLoadDirectBlockedVectorized<MODIFIER>(linear_tid, block_itr.ptr, items);
}
/// Load a linear segment of items from memory, specialized for opaque input iterators (skips vectorization)
template <typename _InputIteratorT>
__device__ __forceinline__ void Load(
_InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
{
LoadDirectBlocked(linear_tid, block_itr, items);
}
/// Load a linear segment of items from memory, guarded by range (skips vectorization)
template <typename InputIteratorT>
__device__ __forceinline__ void Load(
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
int valid_items) ///< [in] Number of valid items to load
{
LoadDirectBlocked(linear_tid, block_itr, items, valid_items);
}
/// Load a linear segment of items from memory, guarded by range, with a fall-back assignment of out-of-bound elements (skips vectorization)
template <typename InputIteratorT, typename DefaultT>
__device__ __forceinline__ void Load(
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
int valid_items, ///< [in] Number of valid items to load
DefaultT oob_default) ///< [in] Default value to assign out-of-bound items
{
LoadDirectBlocked(linear_tid, block_itr, items, valid_items, oob_default);
}
};
/**
* BLOCK_LOAD_TRANSPOSE specialization of load helper
*/
template <int DUMMY>
struct LoadInternal<BLOCK_LOAD_TRANSPOSE, DUMMY>
{
// BlockExchange utility type for keys
typedef BlockExchange<InputT, BLOCK_DIM_X, ITEMS_PER_THREAD, false, BLOCK_DIM_Y, BLOCK_DIM_Z, PTX_ARCH> BlockExchange;
/// Shared memory storage layout type
struct _TempStorage : BlockExchange::TempStorage
{
/// Temporary storage for partially-full block guard
volatile int valid_items;
};
/// Alias wrapper allowing storage to be unioned
struct TempStorage : Uninitialized<_TempStorage> {};
/// Thread reference to shared storage
_TempStorage &temp_storage;
/// Linear thread-id
int linear_tid;
/// Constructor
__device__ __forceinline__ LoadInternal(
TempStorage &temp_storage,
int linear_tid)
:
temp_storage(temp_storage.Alias()),
linear_tid(linear_tid)
{}
/// Load a linear segment of items from memory
template <typename InputIteratorT>
__device__ __forceinline__ void Load(
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load{
{
LoadDirectStriped<BLOCK_THREADS>(linear_tid, block_itr, items);
BlockExchange(temp_storage).StripedToBlocked(items, items);
}
/// Load a linear segment of items from memory, guarded by range
template <typename InputIteratorT>
__device__ __forceinline__ void Load(
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
int valid_items) ///< [in] Number of valid items to load
{
if (linear_tid == 0)
temp_storage.valid_items = valid_items; // Move through volatile smem as a workaround to prevent RF spilling on subsequent loads
CTA_SYNC();
LoadDirectStriped<BLOCK_THREADS>(linear_tid, block_itr, items, temp_storage.valid_items);
BlockExchange(temp_storage).StripedToBlocked(items, items);
}
/// Load a linear segment of items from memory, guarded by range, with a fall-back assignment of out-of-bound elements
template <typename InputIteratorT, typename DefaultT>
__device__ __forceinline__ void Load(
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
int valid_items, ///< [in] Number of valid items to load
DefaultT oob_default) ///< [in] Default value to assign out-of-bound items
{
if (linear_tid == 0)
temp_storage.valid_items = valid_items; // Move through volatile smem as a workaround to prevent RF spilling on subsequent loads
CTA_SYNC();
LoadDirectStriped<BLOCK_THREADS>(linear_tid, block_itr, items, temp_storage.valid_items, oob_default);
BlockExchange(temp_storage).StripedToBlocked(items, items);
}
};
/**
* BLOCK_LOAD_WARP_TRANSPOSE specialization of load helper
*/
template <int DUMMY>
struct LoadInternal<BLOCK_LOAD_WARP_TRANSPOSE, DUMMY>
{
enum
{
WARP_THREADS = CUB_WARP_THREADS(PTX_ARCH)
};
// Assert BLOCK_THREADS must be a multiple of WARP_THREADS
CUB_STATIC_ASSERT((BLOCK_THREADS % WARP_THREADS == 0), "BLOCK_THREADS must be a multiple of WARP_THREADS");
// BlockExchange utility type for keys
typedef BlockExchange<InputT, BLOCK_DIM_X, ITEMS_PER_THREAD, false, BLOCK_DIM_Y, BLOCK_DIM_Z, PTX_ARCH> BlockExchange;
/// Shared memory storage layout type
struct _TempStorage : BlockExchange::TempStorage
{
/// Temporary storage for partially-full block guard
volatile int valid_items;
};
/// Alias wrapper allowing storage to be unioned
struct TempStorage : Uninitialized<_TempStorage> {};
/// Thread reference to shared storage
_TempStorage &temp_storage;
/// Linear thread-id
int linear_tid;
/// Constructor
__device__ __forceinline__ LoadInternal(
TempStorage &temp_storage,
int linear_tid)
:
temp_storage(temp_storage.Alias()),
linear_tid(linear_tid)
{}
/// Load a linear segment of items from memory
template <typename InputIteratorT>
__device__ __forceinline__ void Load(
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load{
{
LoadDirectWarpStriped(linear_tid, block_itr, items);
BlockExchange(temp_storage).WarpStripedToBlocked(items, items);
}
/// Load a linear segment of items from memory, guarded by range
template <typename InputIteratorT>
__device__ __forceinline__ void Load(
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
int valid_items) ///< [in] Number of valid items to load
{
if (linear_tid == 0)
temp_storage.valid_items = valid_items; // Move through volatile smem as a workaround to prevent RF spilling on subsequent loads
CTA_SYNC();
LoadDirectWarpStriped(linear_tid, block_itr, items, temp_storage.valid_items);
BlockExchange(temp_storage).WarpStripedToBlocked(items, items);
}
/// Load a linear segment of items from memory, guarded by range, with a fall-back assignment of out-of-bound elements
template <typename InputIteratorT, typename DefaultT>
__device__ __forceinline__ void Load(
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
int valid_items, ///< [in] Number of valid items to load
DefaultT oob_default) ///< [in] Default value to assign out-of-bound items
{
if (linear_tid == 0)
temp_storage.valid_items = valid_items; // Move through volatile smem as a workaround to prevent RF spilling on subsequent loads
CTA_SYNC();
LoadDirectWarpStriped(linear_tid, block_itr, items, temp_storage.valid_items, oob_default);
BlockExchange(temp_storage).WarpStripedToBlocked(items, items);
}
};
/**
* BLOCK_LOAD_WARP_TRANSPOSE_TIMESLICED specialization of load helper
*/
template <int DUMMY>
struct LoadInternal<BLOCK_LOAD_WARP_TRANSPOSE_TIMESLICED, DUMMY>
{
enum
{
WARP_THREADS = CUB_WARP_THREADS(PTX_ARCH)
};
// Assert BLOCK_THREADS must be a multiple of WARP_THREADS
CUB_STATIC_ASSERT((BLOCK_THREADS % WARP_THREADS == 0), "BLOCK_THREADS must be a multiple of WARP_THREADS");
// BlockExchange utility type for keys
typedef BlockExchange<InputT, BLOCK_DIM_X, ITEMS_PER_THREAD, true, BLOCK_DIM_Y, BLOCK_DIM_Z, PTX_ARCH> BlockExchange;
/// Shared memory storage layout type
struct _TempStorage : BlockExchange::TempStorage
{
/// Temporary storage for partially-full block guard
volatile int valid_items;
};
/// Alias wrapper allowing storage to be unioned
struct TempStorage : Uninitialized<_TempStorage> {};
/// Thread reference to shared storage
_TempStorage &temp_storage;
/// Linear thread-id
int linear_tid;
/// Constructor
__device__ __forceinline__ LoadInternal(
TempStorage &temp_storage,
int linear_tid)
:
temp_storage(temp_storage.Alias()),
linear_tid(linear_tid)
{}
/// Load a linear segment of items from memory
template <typename InputIteratorT>
__device__ __forceinline__ void Load(
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load{
{
LoadDirectWarpStriped(linear_tid, block_itr, items);
BlockExchange(temp_storage).WarpStripedToBlocked(items, items);
}
/// Load a linear segment of items from memory, guarded by range
template <typename InputIteratorT>
__device__ __forceinline__ void Load(
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
int valid_items) ///< [in] Number of valid items to load
{
if (linear_tid == 0)
temp_storage.valid_items = valid_items; // Move through volatile smem as a workaround to prevent RF spilling on subsequent loads
CTA_SYNC();
LoadDirectWarpStriped(linear_tid, block_itr, items, temp_storage.valid_items);
BlockExchange(temp_storage).WarpStripedToBlocked(items, items);
}
/// Load a linear segment of items from memory, guarded by range, with a fall-back assignment of out-of-bound elements
template <typename InputIteratorT, typename DefaultT>
__device__ __forceinline__ void Load(
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
int valid_items, ///< [in] Number of valid items to load
DefaultT oob_default) ///< [in] Default value to assign out-of-bound items
{
if (linear_tid == 0)
temp_storage.valid_items = valid_items; // Move through volatile smem as a workaround to prevent RF spilling on subsequent loads
CTA_SYNC();
LoadDirectWarpStriped(linear_tid, block_itr, items, temp_storage.valid_items, oob_default);
BlockExchange(temp_storage).WarpStripedToBlocked(items, items);
}
};
/******************************************************************************
* Type definitions
******************************************************************************/
/// Internal load implementation to use
typedef LoadInternal<ALGORITHM, 0> InternalLoad;
/// Shared memory storage layout type
typedef typename InternalLoad::TempStorage _TempStorage;
/******************************************************************************
* Utility methods
******************************************************************************/
/// Internal storage allocator
__device__ __forceinline__ _TempStorage& PrivateStorage()
{
__shared__ _TempStorage private_storage;
return private_storage;
}
/******************************************************************************
* Thread fields
******************************************************************************/
/// Thread reference to shared storage
_TempStorage &temp_storage;
/// Linear thread-id
int linear_tid;
public:
/// \smemstorage{BlockLoad}
struct TempStorage : Uninitialized<_TempStorage> {};
/******************************************************************//**
* \name Collective constructors
*********************************************************************/
//@{
/**
* \brief Collective constructor using a private static allocation of shared memory as temporary storage.
*/
__device__ __forceinline__ BlockLoad()
:
temp_storage(PrivateStorage()),
linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
{}
/**
* \brief Collective constructor using the specified memory allocation as temporary storage.
*/
__device__ __forceinline__ BlockLoad(
TempStorage &temp_storage) ///< [in] Reference to memory allocation having layout type TempStorage
:
temp_storage(temp_storage.Alias()),
linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
{}
//@} end member group
/******************************************************************//**
* \name Data movement
*********************************************************************/
//@{
/**
* \brief Load a linear segment of items from memory.
*
* \par
* - \blocked
* - \smemreuse
*
* \par 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 \p BLOCK_LOAD_WARP_TRANSPOSE,
* meaning memory references are efficiently coalesced using a warp-striped access
* pattern (after which items are locally reordered among threads).
* \par
* \code
* #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
* typedef cub::BlockLoad<int, 128, 4, BLOCK_LOAD_WARP_TRANSPOSE> BlockLoad;
*
* // 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);
*
* \endcode
* \par
* Suppose the input \p d_data is <tt>0, 1, 2, 3, 4, 5, ...</tt>.
* The set of \p thread_data across the block of threads in those threads will be
* <tt>{ [0,1,2,3], [4,5,6,7], ..., [508,509,510,511] }</tt>.
*
*/
template <typename InputIteratorT>
__device__ __forceinline__ void Load(
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
{
InternalLoad(temp_storage, linear_tid).Load(block_itr, items);
}
/**
* \brief Load a linear segment of items from memory, guarded by range.
*
* \par
* - \blocked
* - \smemreuse
*
* \par 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 \p BLOCK_LOAD_WARP_TRANSPOSE,
* meaning memory references are efficiently coalesced using a warp-striped access
* pattern (after which items are locally reordered among threads).
* \par
* \code
* #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
* typedef cub::BlockLoad<int, 128, 4, BLOCK_LOAD_WARP_TRANSPOSE> BlockLoad;
*
* // 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);
*
* \endcode
* \par
* Suppose the input \p d_data is <tt>0, 1, 2, 3, 4, 5, 6...</tt> and \p valid_items is \p 5.
* The set of \p thread_data across the block of threads in those threads will be
* <tt>{ [0,1,2,3], [4,?,?,?], ..., [?,?,?,?] }</tt>, with only the first two threads
* being unmasked to load portions of valid data (and other items remaining unassigned).
*
*/
template <typename InputIteratorT>
__device__ __forceinline__ void Load(
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
int valid_items) ///< [in] Number of valid items to load
{
InternalLoad(temp_storage, linear_tid).Load(block_itr, items, valid_items);
}
/**
* \brief Load a linear segment of items from memory, guarded by range, with a fall-back assignment of out-of-bound elements
*
* \par
* - \blocked
* - \smemreuse
*
* \par 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 \p BLOCK_LOAD_WARP_TRANSPOSE,
* meaning memory references are efficiently coalesced using a warp-striped access
* pattern (after which items are locally reordered among threads).
* \par
* \code
* #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
* typedef cub::BlockLoad<int, 128, 4, BLOCK_LOAD_WARP_TRANSPOSE> BlockLoad;
*
* // 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);
*
* \endcode
* \par
* Suppose the input \p d_data is <tt>0, 1, 2, 3, 4, 5, 6...</tt>,
* \p valid_items is \p 5, and the out-of-bounds default is \p -1.
* The set of \p thread_data across the block of threads in those threads will be
* <tt>{ [0,1,2,3], [4,-1,-1,-1], ..., [-1,-1,-1,-1] }</tt>, with only the first two threads
* being unmasked to load portions of valid data (and other items are assigned \p -1)
*
*/
template <typename InputIteratorT, typename DefaultT>
__device__ __forceinline__ void Load(
InputIteratorT block_itr, ///< [in] The thread block's base input iterator for loading from
InputT (&items)[ITEMS_PER_THREAD], ///< [out] Data to load
int valid_items, ///< [in] Number of valid items to load
DefaultT oob_default) ///< [in] Default value to assign out-of-bound items
{
InternalLoad(temp_storage, linear_tid).Load(block_itr, items, valid_items, oob_default);
}
//@} end member group
};
} // CUB namespace
CUB_NS_POSTFIX // Optional outer namespace(s)
( run in 2.999 seconds using v1.01-cache-2.11-cpan-39bf76dae61 )