Blame view
tools/cub-1.8.0/cub/device/dispatch/dispatch_reduce.cuh
41.7 KB
8dcb6dfcb
first commit
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 |
/******************************************************************************
* 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
* cub::DeviceReduce provides device-wide, parallel operations for computing a reduction across a sequence of data items residing within device-accessible memory.
*/
#pragma once
#include <stdio.h>
#include <iterator>
#include "../../agent/agent_reduce.cuh"
#include "../../iterator/arg_index_input_iterator.cuh"
#include "../../thread/thread_operators.cuh"
#include "../../grid/grid_even_share.cuh"
#include "../../iterator/arg_index_input_iterator.cuh"
#include "../../util_debug.cuh"
#include "../../util_device.cuh"
#include "../../util_namespace.cuh"
/// Optional outer namespace(s)
CUB_NS_PREFIX
/// CUB namespace
namespace cub {
/******************************************************************************
* Kernel entry points
*****************************************************************************/
/**
* Reduce region kernel entry point (multi-block). Computes privatized reductions, one per thread block.
*/
template <
typename ChainedPolicyT, ///< Chained tuning policy
typename InputIteratorT, ///< Random-access input iterator type for reading input items \iterator
typename OutputIteratorT, ///< Output iterator type for recording the reduced aggregate \iterator
typename OffsetT, ///< Signed integer type for global offsets
typename ReductionOpT> ///< Binary reduction functor type having member <tt>T operator()(const T &a, const T &b)</tt>
__launch_bounds__ (int(ChainedPolicyT::ActivePolicy::ReducePolicy::BLOCK_THREADS))
__global__ void DeviceReduceKernel(
InputIteratorT d_in, ///< [in] Pointer to the input sequence of data items
OutputIteratorT d_out, ///< [out] Pointer to the output aggregate
OffsetT num_items, ///< [in] Total number of input data items
GridEvenShare<OffsetT> even_share, ///< [in] Even-share descriptor for mapping an equal number of tiles onto each thread block
ReductionOpT reduction_op) ///< [in] Binary reduction functor
{
// The output value type
typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE), // OutputT = (if output iterator's value type is void) ?
typename std::iterator_traits<InputIteratorT>::value_type, // ... then the input iterator's value type,
typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT; // ... else the output iterator's value type
// Thread block type for reducing input tiles
typedef AgentReduce<
typename ChainedPolicyT::ActivePolicy::ReducePolicy,
InputIteratorT,
OutputIteratorT,
OffsetT,
ReductionOpT>
AgentReduceT;
// Shared memory storage
__shared__ typename AgentReduceT::TempStorage temp_storage;
// Consume input tiles
OutputT block_aggregate = AgentReduceT(temp_storage, d_in, reduction_op).ConsumeTiles(even_share);
// Output result
if (threadIdx.x == 0)
d_out[blockIdx.x] = block_aggregate;
}
/**
* Reduce a single tile kernel entry point (single-block). Can be used to aggregate privatized thread block reductions from a previous multi-block reduction pass.
*/
template <
typename ChainedPolicyT, ///< Chained tuning policy
typename InputIteratorT, ///< Random-access input iterator type for reading input items \iterator
typename OutputIteratorT, ///< Output iterator type for recording the reduced aggregate \iterator
typename OffsetT, ///< Signed integer type for global offsets
typename ReductionOpT, ///< Binary reduction functor type having member <tt>T operator()(const T &a, const T &b)</tt>
typename OuputT> ///< Data element type that is convertible to the \p value type of \p OutputIteratorT
__launch_bounds__ (int(ChainedPolicyT::ActivePolicy::SingleTilePolicy::BLOCK_THREADS), 1)
__global__ void DeviceReduceSingleTileKernel(
InputIteratorT d_in, ///< [in] Pointer to the input sequence of data items
OutputIteratorT d_out, ///< [out] Pointer to the output aggregate
OffsetT num_items, ///< [in] Total number of input data items
ReductionOpT reduction_op, ///< [in] Binary reduction functor
OuputT init) ///< [in] The initial value of the reduction
{
// Thread block type for reducing input tiles
typedef AgentReduce<
typename ChainedPolicyT::ActivePolicy::SingleTilePolicy,
InputIteratorT,
OutputIteratorT,
OffsetT,
ReductionOpT>
AgentReduceT;
// Shared memory storage
__shared__ typename AgentReduceT::TempStorage temp_storage;
// Check if empty problem
if (num_items == 0)
{
if (threadIdx.x == 0)
*d_out = init;
return;
}
// Consume input tiles
OuputT block_aggregate = AgentReduceT(temp_storage, d_in, reduction_op).ConsumeRange(
OffsetT(0),
num_items);
// Output result
if (threadIdx.x == 0)
*d_out = reduction_op(init, block_aggregate);
}
/// Normalize input iterator to segment offset
template <typename T, typename OffsetT, typename IteratorT>
__device__ __forceinline__
void NormalizeReductionOutput(
T &/*val*/,
OffsetT /*base_offset*/,
IteratorT /*itr*/)
{}
/// Normalize input iterator to segment offset (specialized for arg-index)
template <typename KeyValuePairT, typename OffsetT, typename WrappedIteratorT, typename OutputValueT>
__device__ __forceinline__
void NormalizeReductionOutput(
KeyValuePairT &val,
OffsetT base_offset,
ArgIndexInputIterator<WrappedIteratorT, OffsetT, OutputValueT> /*itr*/)
{
val.key -= base_offset;
}
/**
* Segmented reduction (one block per segment)
*/
template <
typename ChainedPolicyT, ///< Chained tuning policy
typename InputIteratorT, ///< Random-access input iterator type for reading input items \iterator
typename OutputIteratorT, ///< Output iterator type for recording the reduced aggregate \iterator
typename OffsetIteratorT, ///< Random-access input iterator type for reading segment offsets \iterator
typename OffsetT, ///< Signed integer type for global offsets
typename ReductionOpT, ///< Binary reduction functor type having member <tt>T operator()(const T &a, const T &b)</tt>
typename OutputT> ///< Data element type that is convertible to the \p value type of \p OutputIteratorT
__launch_bounds__ (int(ChainedPolicyT::ActivePolicy::ReducePolicy::BLOCK_THREADS))
__global__ void DeviceSegmentedReduceKernel(
InputIteratorT d_in, ///< [in] Pointer to the input sequence of data items
OutputIteratorT d_out, ///< [out] Pointer to the output aggregate
OffsetIteratorT d_begin_offsets, ///< [in] Pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
OffsetIteratorT d_end_offsets, ///< [in] Pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>. If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
int /*num_segments*/, ///< [in] The number of segments that comprise the sorting data
ReductionOpT reduction_op, ///< [in] Binary reduction functor
OutputT init) ///< [in] The initial value of the reduction
{
// Thread block type for reducing input tiles
typedef AgentReduce<
typename ChainedPolicyT::ActivePolicy::ReducePolicy,
InputIteratorT,
OutputIteratorT,
OffsetT,
ReductionOpT>
AgentReduceT;
// Shared memory storage
__shared__ typename AgentReduceT::TempStorage temp_storage;
OffsetT segment_begin = d_begin_offsets[blockIdx.x];
OffsetT segment_end = d_end_offsets[blockIdx.x];
// Check if empty problem
if (segment_begin == segment_end)
{
if (threadIdx.x == 0)
d_out[blockIdx.x] = init;
return;
}
// Consume input tiles
OutputT block_aggregate = AgentReduceT(temp_storage, d_in, reduction_op).ConsumeRange(
segment_begin,
segment_end);
// Normalize as needed
NormalizeReductionOutput(block_aggregate, segment_begin, d_in);
if (threadIdx.x == 0)
d_out[blockIdx.x] = reduction_op(init, block_aggregate);;
}
/******************************************************************************
* Policy
******************************************************************************/
template <
typename OuputT, ///< Data type
typename OffsetT, ///< Signed integer type for global offsets
typename ReductionOpT> ///< Binary reduction functor type having member <tt>T operator()(const T &a, const T &b)</tt>
struct DeviceReducePolicy
{
//------------------------------------------------------------------------------
// Architecture-specific tuning policies
//------------------------------------------------------------------------------
/// SM13
struct Policy130 : ChainedPolicy<130, Policy130, Policy130>
{
// ReducePolicy
typedef AgentReducePolicy<
CUB_SCALED_GRANULARITIES(128, 8, OuputT), ///< Threads per block, items per thread
2, ///< Number of items per vectorized load
BLOCK_REDUCE_RAKING, ///< Cooperative block-wide reduction algorithm to use
LOAD_DEFAULT> ///< Cache load modifier
ReducePolicy;
// SingleTilePolicy
typedef ReducePolicy SingleTilePolicy;
// SegmentedReducePolicy
typedef ReducePolicy SegmentedReducePolicy;
};
/// SM20
struct Policy200 : ChainedPolicy<200, Policy200, Policy130>
{
// ReducePolicy (GTX 580: 178.9 GB/s @ 48M 4B items, 158.1 GB/s @ 192M 1B items)
typedef AgentReducePolicy<
CUB_SCALED_GRANULARITIES(128, 8, OuputT), ///< Threads per block, items per thread
4, ///< Number of items per vectorized load
BLOCK_REDUCE_RAKING, ///< Cooperative block-wide reduction algorithm to use
LOAD_DEFAULT> ///< Cache load modifier
ReducePolicy;
// SingleTilePolicy
typedef ReducePolicy SingleTilePolicy;
// SegmentedReducePolicy
typedef ReducePolicy SegmentedReducePolicy;
};
/// SM30
struct Policy300 : ChainedPolicy<300, Policy300, Policy200>
{
// ReducePolicy (GTX670: 154.0 @ 48M 4B items)
typedef AgentReducePolicy<
CUB_SCALED_GRANULARITIES(256, 20, OuputT), ///< Threads per block, items per thread
2, ///< Number of items per vectorized load
BLOCK_REDUCE_WARP_REDUCTIONS, ///< Cooperative block-wide reduction algorithm to use
LOAD_DEFAULT> ///< Cache load modifier
ReducePolicy;
// SingleTilePolicy
typedef ReducePolicy SingleTilePolicy;
// SegmentedReducePolicy
typedef ReducePolicy SegmentedReducePolicy;
};
/// SM35
struct Policy350 : ChainedPolicy<350, Policy350, Policy300>
{
// ReducePolicy (GTX Titan: 255.1 GB/s @ 48M 4B items; 228.7 GB/s @ 192M 1B items)
typedef AgentReducePolicy<
CUB_SCALED_GRANULARITIES(256, 20, OuputT), ///< Threads per block, items per thread
4, ///< Number of items per vectorized load
BLOCK_REDUCE_WARP_REDUCTIONS, ///< Cooperative block-wide reduction algorithm to use
LOAD_LDG> ///< Cache load modifier
ReducePolicy;
// SingleTilePolicy
typedef ReducePolicy SingleTilePolicy;
// SegmentedReducePolicy
typedef ReducePolicy SegmentedReducePolicy;
};
/// SM60
struct Policy600 : ChainedPolicy<600, Policy600, Policy350>
{
// ReducePolicy (P100: 591 GB/s @ 64M 4B items; 583 GB/s @ 256M 1B items)
typedef AgentReducePolicy<
CUB_SCALED_GRANULARITIES(256, 16, OuputT), ///< Threads per block, items per thread
4, ///< Number of items per vectorized load
BLOCK_REDUCE_WARP_REDUCTIONS, ///< Cooperative block-wide reduction algorithm to use
LOAD_LDG> ///< Cache load modifier
ReducePolicy;
// SingleTilePolicy
typedef ReducePolicy SingleTilePolicy;
// SegmentedReducePolicy
typedef ReducePolicy SegmentedReducePolicy;
};
/// MaxPolicy
typedef Policy600 MaxPolicy;
};
/******************************************************************************
* Single-problem dispatch
******************************************************************************/
/**
* Utility class for dispatching the appropriately-tuned kernels for device-wide reduction
*/
template <
typename InputIteratorT, ///< Random-access input iterator type for reading input items \iterator
typename OutputIteratorT, ///< Output iterator type for recording the reduced aggregate \iterator
typename OffsetT, ///< Signed integer type for global offsets
typename ReductionOpT> ///< Binary reduction functor type having member <tt>T operator()(const T &a, const T &b)</tt>
struct DispatchReduce :
DeviceReducePolicy<
typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE), // OutputT = (if output iterator's value type is void) ?
typename std::iterator_traits<InputIteratorT>::value_type, // ... then the input iterator's value type,
typename std::iterator_traits<OutputIteratorT>::value_type>::Type, // ... else the output iterator's value type
OffsetT,
ReductionOpT>
{
//------------------------------------------------------------------------------
// Constants
//------------------------------------------------------------------------------
// Data type of output iterator
typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE), // OutputT = (if output iterator's value type is void) ?
typename std::iterator_traits<InputIteratorT>::value_type, // ... then the input iterator's value type,
typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT; // ... else the output iterator's value type
//------------------------------------------------------------------------------
// Problem state
//------------------------------------------------------------------------------
void *d_temp_storage; ///< [in] %Device-accessible allocation of temporary storage. When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
size_t &temp_storage_bytes; ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
InputIteratorT d_in; ///< [in] Pointer to the input sequence of data items
OutputIteratorT d_out; ///< [out] Pointer to the output aggregate
OffsetT num_items; ///< [in] Total number of input items (i.e., length of \p d_in)
ReductionOpT reduction_op; ///< [in] Binary reduction functor
OutputT init; ///< [in] The initial value of the reduction
cudaStream_t stream; ///< [in] CUDA stream to launch kernels within. Default is stream<sub>0</sub>.
bool debug_synchronous; ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors. Also causes launch configurations to be printed to the console. Default is \p false.
int ptx_version; ///< [in] PTX version
//------------------------------------------------------------------------------
// Constructor
//------------------------------------------------------------------------------
/// Constructor
CUB_RUNTIME_FUNCTION __forceinline__
DispatchReduce(
void* d_temp_storage,
size_t &temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
OffsetT num_items,
ReductionOpT reduction_op,
OutputT init,
cudaStream_t stream,
bool debug_synchronous,
int ptx_version)
:
d_temp_storage(d_temp_storage),
temp_storage_bytes(temp_storage_bytes),
d_in(d_in),
d_out(d_out),
num_items(num_items),
reduction_op(reduction_op),
init(init),
stream(stream),
debug_synchronous(debug_synchronous),
ptx_version(ptx_version)
{}
//------------------------------------------------------------------------------
// Small-problem (single tile) invocation
//------------------------------------------------------------------------------
/// Invoke a single block block to reduce in-core
template <
typename ActivePolicyT, ///< Umbrella policy active for the target device
typename SingleTileKernelT> ///< Function type of cub::DeviceReduceSingleTileKernel
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t InvokeSingleTile(
SingleTileKernelT single_tile_kernel) ///< [in] Kernel function pointer to parameterization of cub::DeviceReduceSingleTileKernel
{
#ifndef CUB_RUNTIME_ENABLED
(void)single_tile_kernel;
// Kernel launch not supported from this device
return CubDebug(cudaErrorNotSupported );
#else
cudaError error = cudaSuccess;
do
{
// Return if the caller is simply requesting the size of the storage allocation
if (d_temp_storage == NULL)
{
temp_storage_bytes = 1;
break;
}
// Log single_reduce_sweep_kernel configuration
if (debug_synchronous) _CubLog("Invoking DeviceReduceSingleTileKernel<<<1, %d, 0, %lld>>>(), %d items per thread
",
ActivePolicyT::SingleTilePolicy::BLOCK_THREADS,
(long long) stream,
ActivePolicyT::SingleTilePolicy::ITEMS_PER_THREAD);
// Invoke single_reduce_sweep_kernel
single_tile_kernel<<<1, ActivePolicyT::SingleTilePolicy::BLOCK_THREADS, 0, stream>>>(
d_in,
d_out,
num_items,
reduction_op,
init);
// Check for failure to launch
if (CubDebug(error = cudaPeekAtLastError())) break;
// Sync the stream if specified to flush runtime errors
if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
}
while (0);
return error;
#endif // CUB_RUNTIME_ENABLED
}
//------------------------------------------------------------------------------
// Normal problem size invocation (two-pass)
//------------------------------------------------------------------------------
/// Invoke two-passes to reduce
template <
typename ActivePolicyT, ///< Umbrella policy active for the target device
typename ReduceKernelT, ///< Function type of cub::DeviceReduceKernel
typename SingleTileKernelT> ///< Function type of cub::DeviceReduceSingleTileKernel
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t InvokePasses(
ReduceKernelT reduce_kernel, ///< [in] Kernel function pointer to parameterization of cub::DeviceReduceKernel
SingleTileKernelT single_tile_kernel) ///< [in] Kernel function pointer to parameterization of cub::DeviceReduceSingleTileKernel
{
#ifndef CUB_RUNTIME_ENABLED
(void) reduce_kernel;
(void) single_tile_kernel;
// Kernel launch not supported from this device
return CubDebug(cudaErrorNotSupported );
#else
cudaError error = cudaSuccess;
do
{
// Get device ordinal
int device_ordinal;
if (CubDebug(error = cudaGetDevice(&device_ordinal))) break;
// Get SM count
int sm_count;
if (CubDebug(error = cudaDeviceGetAttribute (&sm_count, cudaDevAttrMultiProcessorCount, device_ordinal))) break;
// Init regular kernel configuration
KernelConfig reduce_config;
if (CubDebug(error = reduce_config.Init<typename ActivePolicyT::ReducePolicy>(reduce_kernel))) break;
int reduce_device_occupancy = reduce_config.sm_occupancy * sm_count;
// Even-share work distribution
int max_blocks = reduce_device_occupancy * CUB_SUBSCRIPTION_FACTOR(ptx_version);
GridEvenShare<OffsetT> even_share;
even_share.DispatchInit(num_items, max_blocks, reduce_config.tile_size);
// Temporary storage allocation requirements
void* allocations[1];
size_t allocation_sizes[1] =
{
max_blocks * sizeof(OutputT) // bytes needed for privatized block reductions
};
// Alias the temporary allocations from the single storage blob (or compute the necessary size of the blob)
if (CubDebug(error = AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes))) break;
if (d_temp_storage == NULL)
{
// Return if the caller is simply requesting the size of the storage allocation
return cudaSuccess;
}
// Alias the allocation for the privatized per-block reductions
OutputT *d_block_reductions = (OutputT*) allocations[0];
// Get grid size for device_reduce_sweep_kernel
int reduce_grid_size = even_share.grid_size;
// Log device_reduce_sweep_kernel configuration
if (debug_synchronous) _CubLog("Invoking DeviceReduceKernel<<<%d, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy
",
reduce_grid_size,
ActivePolicyT::ReducePolicy::BLOCK_THREADS,
(long long) stream,
ActivePolicyT::ReducePolicy::ITEMS_PER_THREAD,
reduce_config.sm_occupancy);
// Invoke DeviceReduceKernel
reduce_kernel<<<reduce_grid_size, ActivePolicyT::ReducePolicy::BLOCK_THREADS, 0, stream>>>(
d_in,
d_block_reductions,
num_items,
even_share,
reduction_op);
// Check for failure to launch
if (CubDebug(error = cudaPeekAtLastError())) break;
// Sync the stream if specified to flush runtime errors
if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
// Log single_reduce_sweep_kernel configuration
if (debug_synchronous) _CubLog("Invoking DeviceReduceSingleTileKernel<<<1, %d, 0, %lld>>>(), %d items per thread
",
ActivePolicyT::SingleTilePolicy::BLOCK_THREADS,
(long long) stream,
ActivePolicyT::SingleTilePolicy::ITEMS_PER_THREAD);
// Invoke DeviceReduceSingleTileKernel
single_tile_kernel<<<1, ActivePolicyT::SingleTilePolicy::BLOCK_THREADS, 0, stream>>>(
d_block_reductions,
d_out,
reduce_grid_size,
reduction_op,
init);
// Check for failure to launch
if (CubDebug(error = cudaPeekAtLastError())) break;
// Sync the stream if specified to flush runtime errors
if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
}
while (0);
return error;
#endif // CUB_RUNTIME_ENABLED
}
//------------------------------------------------------------------------------
// Chained policy invocation
//------------------------------------------------------------------------------
/// Invocation
template <typename ActivePolicyT>
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t Invoke()
{
typedef typename ActivePolicyT::SingleTilePolicy SingleTilePolicyT;
typedef typename DispatchReduce::MaxPolicy MaxPolicyT;
// Force kernel code-generation in all compiler passes
if (num_items <= (SingleTilePolicyT::BLOCK_THREADS * SingleTilePolicyT::ITEMS_PER_THREAD))
{
// Small, single tile size
return InvokeSingleTile<ActivePolicyT>(
DeviceReduceSingleTileKernel<MaxPolicyT, InputIteratorT, OutputIteratorT, OffsetT, ReductionOpT, OutputT>);
}
else
{
// Regular size
return InvokePasses<ActivePolicyT>(
DeviceReduceKernel<typename DispatchReduce::MaxPolicy, InputIteratorT, OutputT*, OffsetT, ReductionOpT>,
DeviceReduceSingleTileKernel<MaxPolicyT, OutputT*, OutputIteratorT, OffsetT, ReductionOpT, OutputT>);
}
}
//------------------------------------------------------------------------------
// Dispatch entrypoints
//------------------------------------------------------------------------------
/**
* Internal dispatch routine for computing a device-wide reduction
*/
CUB_RUNTIME_FUNCTION __forceinline__
static cudaError_t Dispatch(
void *d_temp_storage, ///< [in] %Device-accessible allocation of temporary storage. When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
size_t &temp_storage_bytes, ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
InputIteratorT d_in, ///< [in] Pointer to the input sequence of data items
OutputIteratorT d_out, ///< [out] Pointer to the output aggregate
OffsetT num_items, ///< [in] Total number of input items (i.e., length of \p d_in)
ReductionOpT reduction_op, ///< [in] Binary reduction functor
OutputT init, ///< [in] The initial value of the reduction
cudaStream_t stream, ///< [in] <b>[optional]</b> CUDA stream to launch kernels within. Default is stream<sub>0</sub>.
bool debug_synchronous) ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors. Also causes launch configurations to be printed to the console. Default is \p false.
{
typedef typename DispatchReduce::MaxPolicy MaxPolicyT;
cudaError error = cudaSuccess;
do
{
// Get PTX version
int ptx_version;
if (CubDebug(error = PtxVersion(ptx_version))) break;
// Create dispatch functor
DispatchReduce dispatch(
d_temp_storage, temp_storage_bytes,
d_in, d_out, num_items, reduction_op, init,
stream, debug_synchronous, ptx_version);
// Dispatch to chained policy
if (CubDebug(error = MaxPolicyT::Invoke(ptx_version, dispatch))) break;
}
while (0);
return error;
}
};
/******************************************************************************
* Segmented dispatch
******************************************************************************/
/**
* Utility class for dispatching the appropriately-tuned kernels for device-wide reduction
*/
template <
typename InputIteratorT, ///< Random-access input iterator type for reading input items \iterator
typename OutputIteratorT, ///< Output iterator type for recording the reduced aggregate \iterator
typename OffsetIteratorT, ///< Random-access input iterator type for reading segment offsets \iterator
typename OffsetT, ///< Signed integer type for global offsets
typename ReductionOpT> ///< Binary reduction functor type having member <tt>T operator()(const T &a, const T &b)</tt>
struct DispatchSegmentedReduce :
DeviceReducePolicy<
typename std::iterator_traits<InputIteratorT>::value_type,
OffsetT,
ReductionOpT>
{
//------------------------------------------------------------------------------
// Constants
//------------------------------------------------------------------------------
/// The output value type
typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE), // OutputT = (if output iterator's value type is void) ?
typename std::iterator_traits<InputIteratorT>::value_type, // ... then the input iterator's value type,
typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT; // ... else the output iterator's value type
//------------------------------------------------------------------------------
// Problem state
//------------------------------------------------------------------------------
void *d_temp_storage; ///< [in] %Device-accessible allocation of temporary storage. When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
size_t &temp_storage_bytes; ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
InputIteratorT d_in; ///< [in] Pointer to the input sequence of data items
OutputIteratorT d_out; ///< [out] Pointer to the output aggregate
OffsetT num_segments; ///< [in] The number of segments that comprise the sorting data
OffsetIteratorT d_begin_offsets; ///< [in] Pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
OffsetIteratorT d_end_offsets; ///< [in] Pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>. If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
ReductionOpT reduction_op; ///< [in] Binary reduction functor
OutputT init; ///< [in] The initial value of the reduction
cudaStream_t stream; ///< [in] CUDA stream to launch kernels within. Default is stream<sub>0</sub>.
bool debug_synchronous; ///< [in] Whether or not to synchronize the stream after every kernel launch to check for errors. Also causes launch configurations to be printed to the console. Default is \p false.
int ptx_version; ///< [in] PTX version
//------------------------------------------------------------------------------
// Constructor
//------------------------------------------------------------------------------
/// Constructor
CUB_RUNTIME_FUNCTION __forceinline__
DispatchSegmentedReduce(
void* d_temp_storage,
size_t &temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
OffsetT num_segments,
OffsetIteratorT d_begin_offsets,
OffsetIteratorT d_end_offsets,
ReductionOpT reduction_op,
OutputT init,
cudaStream_t stream,
bool debug_synchronous,
int ptx_version)
:
d_temp_storage(d_temp_storage),
temp_storage_bytes(temp_storage_bytes),
d_in(d_in),
d_out(d_out),
num_segments(num_segments),
d_begin_offsets(d_begin_offsets),
d_end_offsets(d_end_offsets),
reduction_op(reduction_op),
init(init),
stream(stream),
debug_synchronous(debug_synchronous),
ptx_version(ptx_version)
{}
//------------------------------------------------------------------------------
// Chained policy invocation
//------------------------------------------------------------------------------
/// Invocation
template <
typename ActivePolicyT, ///< Umbrella policy active for the target device
typename DeviceSegmentedReduceKernelT> ///< Function type of cub::DeviceSegmentedReduceKernel
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t InvokePasses(
DeviceSegmentedReduceKernelT segmented_reduce_kernel) ///< [in] Kernel function pointer to parameterization of cub::DeviceSegmentedReduceKernel
{
#ifndef CUB_RUNTIME_ENABLED
(void)segmented_reduce_kernel;
// Kernel launch not supported from this device
return CubDebug(cudaErrorNotSupported );
#else
cudaError error = cudaSuccess;
do
{
// Return if the caller is simply requesting the size of the storage allocation
if (d_temp_storage == NULL)
{
temp_storage_bytes = 1;
return cudaSuccess;
}
// Init kernel configuration
KernelConfig segmented_reduce_config;
if (CubDebug(error = segmented_reduce_config.Init<typename ActivePolicyT::SegmentedReducePolicy>(segmented_reduce_kernel))) break;
// Log device_reduce_sweep_kernel configuration
if (debug_synchronous) _CubLog("Invoking SegmentedDeviceReduceKernel<<<%d, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy
",
num_segments,
ActivePolicyT::SegmentedReducePolicy::BLOCK_THREADS,
(long long) stream,
ActivePolicyT::SegmentedReducePolicy::ITEMS_PER_THREAD,
segmented_reduce_config.sm_occupancy);
// Invoke DeviceReduceKernel
segmented_reduce_kernel<<<num_segments, ActivePolicyT::SegmentedReducePolicy::BLOCK_THREADS, 0, stream>>>(
d_in,
d_out,
d_begin_offsets,
d_end_offsets,
num_segments,
reduction_op,
init);
// Check for failure to launch
if (CubDebug(error = cudaPeekAtLastError())) break;
// Sync the stream if specified to flush runtime errors
if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
}
while (0);
return error;
#endif // CUB_RUNTIME_ENABLED
}
/// Invocation
template <typename ActivePolicyT>
CUB_RUNTIME_FUNCTION __forceinline__
cudaError_t Invoke()
{
typedef typename DispatchSegmentedReduce::MaxPolicy MaxPolicyT;
// Force kernel code-generation in all compiler passes
return InvokePasses<ActivePolicyT>(
DeviceSegmentedReduceKernel<MaxPolicyT, InputIteratorT, OutputIteratorT, OffsetIteratorT, OffsetT, ReductionOpT, OutputT>);
}
//------------------------------------------------------------------------------
// Dispatch entrypoints
//------------------------------------------------------------------------------
/**
* Internal dispatch routine for computing a device-wide reduction
*/
CUB_RUNTIME_FUNCTION __forceinline__
static cudaError_t Dispatch(
void *d_temp_storage, ///< [in] %Device-accessible allocation of temporary storage. When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
size_t &temp_storage_bytes, ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
InputIteratorT d_in, ///< [in] Pointer to the input sequence of data items
OutputIteratorT d_out, ///< [out] Pointer to the output aggregate
int num_segments, ///< [in] The number of segments that comprise the sorting data
OffsetIteratorT d_begin_offsets, ///< [in] Pointer to the sequence of beginning offsets of length \p num_segments, such that <tt>d_begin_offsets[i]</tt> is the first element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>
OffsetIteratorT d_end_offsets, ///< [in] Pointer to the sequence of ending offsets of length \p num_segments, such that <tt>d_end_offsets[i]-1</tt> is the last element of the <em>i</em><sup>th</sup> data segment in <tt>d_keys_*</tt> and <tt>d_values_*</tt>. If <tt>d_end_offsets[i]-1</tt> <= <tt>d_begin_offsets[i]</tt>, the <em>i</em><sup>th</sup> is considered empty.
ReductionOpT reduction_op, ///< [in] Binary reduction functor
OutputT init, ///< [in] The initial value of the reduction
cudaStream_t stream, ///< [in] <b>[optional]</b> CUDA stream to launch kernels within. Default is stream<sub>0</sub>.
bool debug_synchronous) ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors. Also causes launch configurations to be printed to the console. Default is \p false.
{
typedef typename DispatchSegmentedReduce::MaxPolicy MaxPolicyT;
if (num_segments <= 0)
return cudaSuccess;
cudaError error = cudaSuccess;
do
{
// Get PTX version
int ptx_version;
if (CubDebug(error = PtxVersion(ptx_version))) break;
// Create dispatch functor
DispatchSegmentedReduce dispatch(
d_temp_storage, temp_storage_bytes,
d_in, d_out,
num_segments, d_begin_offsets, d_end_offsets,
reduction_op, init,
stream, debug_synchronous, ptx_version);
// Dispatch to chained policy
if (CubDebug(error = MaxPolicyT::Invoke(ptx_version, dispatch))) break;
}
while (0);
return error;
}
};
} // CUB namespace
CUB_NS_POSTFIX // Optional outer namespace(s)
|