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/******************************************************************************
* Copyright (c) 2011, Duane Merrill. All rights reserved.
* Copyright (c) 2011-2018, 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 writing linear segments of data from the CUDA thread block
*/
#pragma once
#include <iterator>
#include "block_exchange.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
* @{
*/
/******************************************************************//**
*
ame Blocked arrangement I/O (direct)
*********************************************************************/
//@{
/**
* \brief Store a blocked arrangement of items across a thread block into a linear segment of items.
*
* \blocked
*
* \tparam T <b>[inferred]</b> The data type to store.
* \tparam ITEMS_PER_THREAD <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
* \tparam OutputIteratorT <b>[inferred]</b> The random-access iterator type for output \iterator.
*/
template <
typename T,
int ITEMS_PER_THREAD,
typename OutputIteratorT>
__device__ __forceinline__ void StoreDirectBlocked(
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)
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
{
OutputIteratorT thread_itr = block_itr + (linear_tid * ITEMS_PER_THREAD);
// Store directly in thread-blocked order
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
{
thread_itr[ITEM] = items[ITEM];
}
}
/**
* \brief Store a blocked arrangement of items across a thread block into a linear segment of items, guarded by range
*
* \blocked
*
* \tparam T <b>[inferred]</b> The data type to store.
* \tparam ITEMS_PER_THREAD <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
* \tparam OutputIteratorT <b>[inferred]</b> The random-access iterator type for output \iterator.
*/
template <
typename T,
int ITEMS_PER_THREAD,
typename OutputIteratorT>
__device__ __forceinline__ void StoreDirectBlocked(
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)
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD], ///< [in] Data to store
int valid_items) ///< [in] Number of valid items to write
{
OutputIteratorT thread_itr = block_itr + (linear_tid * ITEMS_PER_THREAD);
// Store directly in thread-blocked order
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
{
if (ITEM + (linear_tid * ITEMS_PER_THREAD) < valid_items)
{
thread_itr[ITEM] = items[ITEM];
}
}
}
/**
* \brief Store a blocked arrangement of items across a thread block into a linear segment of items.
*
* \blocked
*
* The output offset (\p block_ptr + \p block_offset) must be quad-item aligned,
* which is the default starting offset returned by \p cudaMalloc()
*
* \par
* The following conditions will prevent vectorization and storing will fall back to cub::BLOCK_STORE_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 store.
* \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 StoreDirectBlockedVectorized(
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 storing from
T (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
{
enum
{
// Maximum CUDA vector size is 4 elements
MAX_VEC_SIZE = CUB_MIN(4, ITEMS_PER_THREAD),
// Vector size must be a power of two and an even divisor of the items per thread
VEC_SIZE = ((((MAX_VEC_SIZE - 1) & MAX_VEC_SIZE) == 0) && ((ITEMS_PER_THREAD % MAX_VEC_SIZE) == 0)) ?
MAX_VEC_SIZE :
1,
VECTORS_PER_THREAD = ITEMS_PER_THREAD / VEC_SIZE,
};
// Vector type
typedef typename CubVector<T, VEC_SIZE>::Type Vector;
// Alias global pointer
Vector *block_ptr_vectors = reinterpret_cast<Vector*>(const_cast<T*>(block_ptr));
// Alias pointers (use "raw" array here which should get optimized away to prevent conservative PTXAS lmem spilling)
Vector raw_vector[VECTORS_PER_THREAD];
T *raw_items = reinterpret_cast<T*>(raw_vector);
// Copy
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
{
raw_items[ITEM] = items[ITEM];
}
// Direct-store using vector types
StoreDirectBlocked(linear_tid, block_ptr_vectors, raw_vector);
}
//@} end member group
/******************************************************************//**
*
ame Striped arrangement I/O (direct)
*********************************************************************/
//@{
/**
* \brief Store a striped arrangement of data across the thread block into a linear segment of items.
*
* \striped
*
* \tparam BLOCK_THREADS The thread block size in threads
* \tparam T <b>[inferred]</b> The data type to store.
* \tparam ITEMS_PER_THREAD <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
* \tparam OutputIteratorT <b>[inferred]</b> The random-access iterator type for output \iterator.
*/
template <
int BLOCK_THREADS,
typename T,
int ITEMS_PER_THREAD,
typename OutputIteratorT>
__device__ __forceinline__ void StoreDirectStriped(
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)
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
{
OutputIteratorT thread_itr = block_itr + linear_tid;
// Store directly in striped order
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
{
thread_itr[(ITEM * BLOCK_THREADS)] = items[ITEM];
}
}
/**
* \brief Store a striped arrangement of data across the thread block into a linear segment of items, guarded by range
*
* \striped
*
* \tparam BLOCK_THREADS The thread block size in threads
* \tparam T <b>[inferred]</b> The data type to store.
* \tparam ITEMS_PER_THREAD <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
* \tparam OutputIteratorT <b>[inferred]</b> The random-access iterator type for output \iterator.
*/
template <
int BLOCK_THREADS,
typename T,
int ITEMS_PER_THREAD,
typename OutputIteratorT>
__device__ __forceinline__ void StoreDirectStriped(
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)
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD], ///< [in] Data to store
int valid_items) ///< [in] Number of valid items to write
{
OutputIteratorT thread_itr = block_itr + linear_tid;
// Store directly in striped order
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
{
if ((ITEM * BLOCK_THREADS) + linear_tid < valid_items)
{
thread_itr[(ITEM * BLOCK_THREADS)] = items[ITEM];
}
}
}
//@} end member group
/******************************************************************//**
*
ame Warp-striped arrangement I/O (direct)
*********************************************************************/
//@{
/**
* \brief Store a warp-striped arrangement of data across the thread block into a linear segment of items.
*
* \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 store.
* \tparam ITEMS_PER_THREAD <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
* \tparam OutputIteratorT <b>[inferred]</b> The random-access iterator type for output \iterator.
*/
template <
typename T,
int ITEMS_PER_THREAD,
typename OutputIteratorT>
__device__ __forceinline__ void StoreDirectWarpStriped(
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)
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD]) ///< [out] Data to load
{
int tid = linear_tid & (CUB_PTX_WARP_THREADS - 1);
int wid = linear_tid >> CUB_PTX_LOG_WARP_THREADS;
int warp_offset = wid * CUB_PTX_WARP_THREADS * ITEMS_PER_THREAD;
OutputIteratorT thread_itr = block_itr + warp_offset + tid;
// Store directly in warp-striped order
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
{
thread_itr[(ITEM * CUB_PTX_WARP_THREADS)] = items[ITEM];
}
}
/**
* \brief Store a warp-striped arrangement of data across the thread block into a linear segment of items, guarded by range
*
* \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 store.
* \tparam ITEMS_PER_THREAD <b>[inferred]</b> The number of consecutive items partitioned onto each thread.
* \tparam OutputIteratorT <b>[inferred]</b> The random-access iterator type for output \iterator.
*/
template <
typename T,
int ITEMS_PER_THREAD,
typename OutputIteratorT>
__device__ __forceinline__ void StoreDirectWarpStriped(
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)
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD], ///< [in] Data to store
int valid_items) ///< [in] Number of valid items to write
{
int tid = linear_tid & (CUB_PTX_WARP_THREADS - 1);
int wid = linear_tid >> CUB_PTX_LOG_WARP_THREADS;
int warp_offset = wid * CUB_PTX_WARP_THREADS * ITEMS_PER_THREAD;
OutputIteratorT thread_itr = block_itr + warp_offset + tid;
// Store 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)
{
thread_itr[(ITEM * CUB_PTX_WARP_THREADS)] = items[ITEM];
}
}
}
//@} end member group
/** @} */ // end group UtilIo
//-----------------------------------------------------------------------------
// Generic BlockStore abstraction
//-----------------------------------------------------------------------------
/**
* \brief cub::BlockStoreAlgorithm enumerates alternative algorithms for cub::BlockStore to write a blocked arrangement of items across a CUDA thread block to a linear segment of memory.
*/
enum BlockStoreAlgorithm
{
/**
* \par Overview
*
* A [<em>blocked arrangement</em>](index.html#sec5sec3) of data is written
* directly to 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_STORE_DIRECT,
/**
* \par Overview
*
* A [<em>blocked arrangement</em>](index.html#sec5sec3) of data is written directly
* to memory using CUDA's built-in vectorized stores as a coalescing optimization.
* For example, <tt>st.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 store width (typically 4 items or 64B, whichever is lower).
* - The following conditions will prevent vectorization and writing will fall back to cub::BLOCK_STORE_DIRECT:
* - \p ITEMS_PER_THREAD is odd
* - The \p OutputIteratorT is not a simple pointer type
* - The block output 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_STORE_VECTORIZE,
/**
* \par Overview
* A [<em>blocked arrangement</em>](index.html#sec5sec3) is locally
* transposed and then efficiently written to memory as a [<em>striped arrangement</em>](index.html#sec5sec3).
*
* \par Performance Considerations
* - The utilization of memory transactions (coalescing) remains high regardless
* of items written per thread.
* - The local reordering incurs slightly longer latencies and throughput than the
* direct cub::BLOCK_STORE_DIRECT and cub::BLOCK_STORE_VECTORIZE alternatives.
*/
BLOCK_STORE_TRANSPOSE,
/**
* \par Overview
* A [<em>blocked arrangement</em>](index.html#sec5sec3) is locally
* transposed and then efficiently written to memory as a
* [<em>warp-striped 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 written per thread.
* - The local reordering incurs slightly longer latencies and throughput than the
* direct cub::BLOCK_STORE_DIRECT and cub::BLOCK_STORE_VECTORIZE alternatives.
*/
BLOCK_STORE_WARP_TRANSPOSE,
/**
* \par Overview
* A [<em>blocked arrangement</em>](index.html#sec5sec3) is locally
* transposed and then efficiently written to memory as a
* [<em>warp-striped 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 written per thread.
* - Provisions less shared memory temporary storage, but incurs larger
* latencies than the BLOCK_STORE_WARP_TRANSPOSE alternative.
*/
BLOCK_STORE_WARP_TRANSPOSE_TIMESLICED,
};
/**
* \brief The BlockStore class provides [<em>collective</em>](index.html#sec0) data movement methods for writing a [<em>blocked arrangement</em>](index.html#sec5sec3) of items partitioned across a CUDA thread block to a linear segment of memory. 
* \ingroup BlockModule
* \ingroup UtilIo
*
* \tparam T The type of data to be written.
* \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::BlockStoreAlgorithm tuning policy enumeration. default: cub::BLOCK_STORE_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). (default: false)
* \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 BlockStore class provides a single data movement abstraction that can be specialized
* to implement different cub::BlockStoreAlgorithm strategies. This facilitates different
* performance policies for different architectures, data types, granularity sizes, etc.
* - BlockStore can be optionally specialized by different data movement strategies:
* -# <b>cub::BLOCK_STORE_DIRECT</b>. A [<em>blocked arrangement</em>](index.html#sec5sec3) of data is written
* directly to memory. [More...](\ref cub::BlockStoreAlgorithm)
* -# <b>cub::BLOCK_STORE_VECTORIZE</b>. A [<em>blocked arrangement</em>](index.html#sec5sec3)
* of data is written directly to memory using CUDA's built-in vectorized stores as a
* coalescing optimization. [More...](\ref cub::BlockStoreAlgorithm)
* -# <b>cub::BLOCK_STORE_TRANSPOSE</b>. A [<em>blocked arrangement</em>](index.html#sec5sec3)
* is locally transposed into a [<em>striped arrangement</em>](index.html#sec5sec3) which is
* then written to memory. [More...](\ref cub::BlockStoreAlgorithm)
* -# <b>cub::BLOCK_STORE_WARP_TRANSPOSE</b>. A [<em>blocked arrangement</em>](index.html#sec5sec3)
* is locally transposed into a [<em>warp-striped arrangement</em>](index.html#sec5sec3) which is
* then written to memory. [More...](\ref cub::BlockStoreAlgorithm)
* - \rowmajor
*
* \par A Simple Example
* \blockcollective{BlockStore}
* \par
* The code snippet below illustrates the storing of a "blocked" arrangement
* of 512 integers across 128 threads (where each thread owns 4 consecutive items)
* into a linear segment of memory. The store is specialized for \p BLOCK_STORE_WARP_TRANSPOSE,
* meaning items are locally reordered among threads so that memory references will be
* efficiently coalesced using a warp-striped access pattern.
* \par
* \code
* #include <cub/cub.cuh> // or equivalently <cub/block/block_store.cuh>
*
* __global__ void ExampleKernel(int *d_data, ...)
* {
* // Specialize BlockStore for a 1D block of 128 threads owning 4 integer items each
* typedef cub::BlockStore<int, 128, 4, BLOCK_STORE_WARP_TRANSPOSE> BlockStore;
*
* // Allocate shared memory for BlockStore
* __shared__ typename BlockStore::TempStorage temp_storage;
*
* // Obtain a segment of consecutive items that are blocked across threads
* int thread_data[4];
* ...
*
* // Store items to linear memory
* int thread_data[4];
* BlockStore(temp_storage).Store(d_data, thread_data);
*
* \endcode
* \par
* Suppose the set of \p thread_data across the block of threads is
* <tt>{ [0,1,2,3], [4,5,6,7], ..., [508,509,510,511] }</tt>.
* The output \p d_data will be <tt>0, 1, 2, 3, 4, 5, ...</tt>.
*
*/
template <
typename T,
int BLOCK_DIM_X,
int ITEMS_PER_THREAD,
BlockStoreAlgorithm ALGORITHM = BLOCK_STORE_DIRECT,
int BLOCK_DIM_Y = 1,
int BLOCK_DIM_Z = 1,
int PTX_ARCH = CUB_PTX_ARCH>
class BlockStore
{
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
******************************************************************************/
/// Store helper
template <BlockStoreAlgorithm _POLICY, int DUMMY>
struct StoreInternal;
/**
* BLOCK_STORE_DIRECT specialization of store helper
*/
template <int DUMMY>
struct StoreInternal<BLOCK_STORE_DIRECT, DUMMY>
{
/// Shared memory storage layout type
typedef NullType TempStorage;
/// Linear thread-id
int linear_tid;
/// Constructor
__device__ __forceinline__ StoreInternal(
TempStorage &/*temp_storage*/,
int linear_tid)
:
linear_tid(linear_tid)
{}
/// Store items into a linear segment of memory
template <typename OutputIteratorT>
__device__ __forceinline__ void Store(
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
{
StoreDirectBlocked(linear_tid, block_itr, items);
}
/// Store items into a linear segment of memory, guarded by range
template <typename OutputIteratorT>
__device__ __forceinline__ void Store(
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD], ///< [in] Data to store
int valid_items) ///< [in] Number of valid items to write
{
StoreDirectBlocked(linear_tid, block_itr, items, valid_items);
}
};
/**
* BLOCK_STORE_VECTORIZE specialization of store helper
*/
template <int DUMMY>
struct StoreInternal<BLOCK_STORE_VECTORIZE, DUMMY>
{
/// Shared memory storage layout type
typedef NullType TempStorage;
/// Linear thread-id
int linear_tid;
/// Constructor
__device__ __forceinline__ StoreInternal(
TempStorage &/*temp_storage*/,
int linear_tid)
:
linear_tid(linear_tid)
{}
/// Store items into a linear segment of memory, specialized for native pointer types (attempts vectorization)
__device__ __forceinline__ void Store(
T *block_ptr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
{
StoreDirectBlockedVectorized(linear_tid, block_ptr, items);
}
/// Store items into a linear segment of memory, specialized for opaque input iterators (skips vectorization)
template <typename OutputIteratorT>
__device__ __forceinline__ void Store(
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
{
StoreDirectBlocked(linear_tid, block_itr, items);
}
/// Store items into a linear segment of memory, guarded by range
template <typename OutputIteratorT>
__device__ __forceinline__ void Store(
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD], ///< [in] Data to store
int valid_items) ///< [in] Number of valid items to write
{
StoreDirectBlocked(linear_tid, block_itr, items, valid_items);
}
};
/**
* BLOCK_STORE_TRANSPOSE specialization of store helper
*/
template <int DUMMY>
struct StoreInternal<BLOCK_STORE_TRANSPOSE, DUMMY>
{
// BlockExchange utility type for keys
typedef BlockExchange<T, 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__ StoreInternal(
TempStorage &temp_storage,
int linear_tid)
:
temp_storage(temp_storage.Alias()),
linear_tid(linear_tid)
{}
/// Store items into a linear segment of memory
template <typename OutputIteratorT>
__device__ __forceinline__ void Store(
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
{
BlockExchange(temp_storage).BlockedToStriped(items);
StoreDirectStriped<BLOCK_THREADS>(linear_tid, block_itr, items);
}
/// Store items into a linear segment of memory, guarded by range
template <typename OutputIteratorT>
__device__ __forceinline__ void Store(
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD], ///< [in] Data to store
int valid_items) ///< [in] Number of valid items to write
{
BlockExchange(temp_storage).BlockedToStriped(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();
StoreDirectStriped<BLOCK_THREADS>(linear_tid, block_itr, items, temp_storage.valid_items);
}
};
/**
* BLOCK_STORE_WARP_TRANSPOSE specialization of store helper
*/
template <int DUMMY>
struct StoreInternal<BLOCK_STORE_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<T, 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__ StoreInternal(
TempStorage &temp_storage,
int linear_tid)
:
temp_storage(temp_storage.Alias()),
linear_tid(linear_tid)
{}
/// Store items into a linear segment of memory
template <typename OutputIteratorT>
__device__ __forceinline__ void Store(
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
{
BlockExchange(temp_storage).BlockedToWarpStriped(items);
StoreDirectWarpStriped(linear_tid, block_itr, items);
}
/// Store items into a linear segment of memory, guarded by range
template <typename OutputIteratorT>
__device__ __forceinline__ void Store(
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD], ///< [in] Data to store
int valid_items) ///< [in] Number of valid items to write
{
BlockExchange(temp_storage).BlockedToWarpStriped(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();
StoreDirectWarpStriped(linear_tid, block_itr, items, temp_storage.valid_items);
}
};
/**
* BLOCK_STORE_WARP_TRANSPOSE_TIMESLICED specialization of store helper
*/
template <int DUMMY>
struct StoreInternal<BLOCK_STORE_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<T, 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__ StoreInternal(
TempStorage &temp_storage,
int linear_tid)
:
temp_storage(temp_storage.Alias()),
linear_tid(linear_tid)
{}
/// Store items into a linear segment of memory
template <typename OutputIteratorT>
__device__ __forceinline__ void Store(
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
{
BlockExchange(temp_storage).BlockedToWarpStriped(items);
StoreDirectWarpStriped(linear_tid, block_itr, items);
}
/// Store items into a linear segment of memory, guarded by range
template <typename OutputIteratorT>
__device__ __forceinline__ void Store(
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD], ///< [in] Data to store
int valid_items) ///< [in] Number of valid items to write
{
BlockExchange(temp_storage).BlockedToWarpStriped(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();
StoreDirectWarpStriped(linear_tid, block_itr, items, temp_storage.valid_items);
}
};
/******************************************************************************
* Type definitions
******************************************************************************/
/// Internal load implementation to use
typedef StoreInternal<ALGORITHM, 0> InternalStore;
/// Shared memory storage layout type
typedef typename InternalStore::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{BlockStore}
struct TempStorage : Uninitialized<_TempStorage> {};
/******************************************************************//**
*
ame Collective constructors
*********************************************************************/
//@{
/**
* \brief Collective constructor using a private static allocation of shared memory as temporary storage.
*/
__device__ __forceinline__ BlockStore()
:
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__ BlockStore(
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
/******************************************************************//**
*
ame Data movement
*********************************************************************/
//@{
/**
* \brief Store items into a linear segment of memory.
*
* \par
* - \blocked
* - \smemreuse
*
* \par Snippet
* The code snippet below illustrates the storing of a "blocked" arrangement
* of 512 integers across 128 threads (where each thread owns 4 consecutive items)
* into a linear segment of memory. The store is specialized for \p BLOCK_STORE_WARP_TRANSPOSE,
* meaning items are locally reordered among threads so that memory references will be
* efficiently coalesced using a warp-striped access pattern.
* \par
* \code
* #include <cub/cub.cuh> // or equivalently <cub/block/block_store.cuh>
*
* __global__ void ExampleKernel(int *d_data, ...)
* {
* // Specialize BlockStore for a 1D block of 128 threads owning 4 integer items each
* typedef cub::BlockStore<int, 128, 4, BLOCK_STORE_WARP_TRANSPOSE> BlockStore;
*
* // Allocate shared memory for BlockStore
* __shared__ typename BlockStore::TempStorage temp_storage;
*
* // Obtain a segment of consecutive items that are blocked across threads
* int thread_data[4];
* ...
*
* // Store items to linear memory
* int thread_data[4];
* BlockStore(temp_storage).Store(d_data, thread_data);
*
* \endcode
* \par
* Suppose the set of \p thread_data across the block of threads is
* <tt>{ [0,1,2,3], [4,5,6,7], ..., [508,509,510,511] }</tt>.
* The output \p d_data will be <tt>0, 1, 2, 3, 4, 5, ...</tt>.
*
*/
template <typename OutputIteratorT>
__device__ __forceinline__ void Store(
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD]) ///< [in] Data to store
{
InternalStore(temp_storage, linear_tid).Store(block_itr, items);
}
/**
* \brief Store items into a linear segment of memory, guarded by range.
*
* \par
* - \blocked
* - \smemreuse
*
* \par Snippet
* The code snippet below illustrates the guarded storing of a "blocked" arrangement
* of 512 integers across 128 threads (where each thread owns 4 consecutive items)
* into a linear segment of memory. The store is specialized for \p BLOCK_STORE_WARP_TRANSPOSE,
* meaning items are locally reordered among threads so that memory references will be
* efficiently coalesced using a warp-striped access pattern.
* \par
* \code
* #include <cub/cub.cuh> // or equivalently <cub/block/block_store.cuh>
*
* __global__ void ExampleKernel(int *d_data, int valid_items, ...)
* {
* // Specialize BlockStore for a 1D block of 128 threads owning 4 integer items each
* typedef cub::BlockStore<int, 128, 4, BLOCK_STORE_WARP_TRANSPOSE> BlockStore;
*
* // Allocate shared memory for BlockStore
* __shared__ typename BlockStore::TempStorage temp_storage;
*
* // Obtain a segment of consecutive items that are blocked across threads
* int thread_data[4];
* ...
*
* // Store items to linear memory
* int thread_data[4];
* BlockStore(temp_storage).Store(d_data, thread_data, valid_items);
*
* \endcode
* \par
* Suppose the set of \p thread_data across the block of threads is
* <tt>{ [0,1,2,3], [4,5,6,7], ..., [508,509,510,511] }</tt> and \p valid_items is \p 5.
* The output \p d_data will be <tt>0, 1, 2, 3, 4, ?, ?, ?, ...</tt>, with
* only the first two threads being unmasked to store portions of valid data.
*
*/
template <typename OutputIteratorT>
__device__ __forceinline__ void Store(
OutputIteratorT block_itr, ///< [in] The thread block's base output iterator for storing to
T (&items)[ITEMS_PER_THREAD], ///< [in] Data to store
int valid_items) ///< [in] Number of valid items to write
{
InternalStore(temp_storage, linear_tid).Store(block_itr, items, valid_items);
}
};
} // CUB namespace
CUB_NS_POSTFIX // Optional outer namespace(s)
|