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/*
* This is an extension of the original CUSP csr_vector.h SPMV implementation.
* It is based on the Code and incorporates changes as to cope with the details
* of the StoRM code.
* As this is mostly copy & paste, the original license still applies.
*/
/*
* Copyright 2008-2009 NVIDIA Corporation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#pragma once
#include <limits>
#include <cstdint>
#include <algorithm>
#include <math_functions.h>
#include <cusp/detail/device/spmv/csr_vector.h>
#include "storm-cudaplugin-config.h"
namespace cusp
{
namespace detail
{
namespace device
{
//////////////////////////////////////////////////////////////////////////////
// CSR SpMV kernels based on a vector model (one warp per row)
//////////////////////////////////////////////////////////////////////////////
//
// spmv_csr_vector_device
// Each row of the CSR matrix is assigned to a warp. The warp computes
// y[i] = A[i,:] * x, i.e. the dot product of the i-th row of A with
// the x vector, in parallel. This division of work implies that
// the CSR index and data arrays (Aj and Ax) are accessed in a contiguous
// manner (but generally not aligned). On GT200 these accesses are
// coalesced, unlike kernels based on the one-row-per-thread division of
// work. Since an entire 32-thread warp is assigned to each row, many
// threads will remain idle when their row contains a small number
// of elements. This code relies on implicit synchronization among
// threads in a warp.
//
// spmv_csr_vector_tex_device
// Same as spmv_csr_vector_tex_device, except that the texture cache is
// used for accessing the x vector.
//
// Note: THREADS_PER_VECTOR must be one of [2,4,8,16,32]
template <unsigned int VECTORS_PER_BLOCK, unsigned int THREADS_PER_VECTOR, bool UseCache>
__launch_bounds__(VECTORS_PER_BLOCK * THREADS_PER_VECTOR,1)
__global__ void
storm_cuda_opt_spmv_csr_vector_kernel_float(const uint_fast64_t num_rows, const uint_fast64_t * matrixRowIndices, const float * matrixColumnIndicesAndValues, const float * x, float * y)
{
__shared__ volatile float sdata[VECTORS_PER_BLOCK * THREADS_PER_VECTOR + THREADS_PER_VECTOR / 2]; // padded to avoid reduction conditionals
__shared__ volatile uint_fast64_t ptrs[VECTORS_PER_BLOCK][2];
const uint_fast64_t THREADS_PER_BLOCK = VECTORS_PER_BLOCK * THREADS_PER_VECTOR;
const uint_fast64_t thread_id = THREADS_PER_BLOCK * blockIdx.x + threadIdx.x; // global thread index
const uint_fast64_t thread_lane = threadIdx.x & (THREADS_PER_VECTOR - 1); // thread index within the vector
const uint_fast64_t vector_id = thread_id / THREADS_PER_VECTOR; // global vector index
const uint_fast64_t vector_lane = threadIdx.x / THREADS_PER_VECTOR; // vector index within the block
const uint_fast64_t num_vectors = VECTORS_PER_BLOCK * gridDim.x; // total number of active vectors
for(uint_fast64_t row = vector_id; row < num_rows; row += num_vectors)
{
// use two threads to fetch Ap[row] and Ap[row+1]
// this is considerably faster than the straightforward version
if(thread_lane < 2)
ptrs[vector_lane][thread_lane] = matrixRowIndices[row + thread_lane];
const uint_fast64_t row_start = ptrs[vector_lane][0]; //same as: row_start = Ap[row];
const uint_fast64_t row_end = ptrs[vector_lane][1]; //same as: row_end = Ap[row+1];
// initialize local sum
float sum = 0;
if (THREADS_PER_VECTOR == 32 && row_end - row_start > 32)
{
// ensure aligned memory access to Aj and Ax
uint_fast64_t jj = row_start - (row_start & (THREADS_PER_VECTOR - 1)) + thread_lane;
// accumulate local sums
if(jj >= row_start && jj < row_end) {
#ifdef STORM_CUDAPLUGIN_HAVE_64BIT_FLOAT_ALIGNMENT
sum += matrixColumnIndicesAndValues[4 * jj + 2] * fetch_x<UseCache>(*reinterpret_cast<uint_fast64_t const*>(matrixColumnIndicesAndValues + 4 * jj), x);
#else
sum += matrixColumnIndicesAndValues[3 * jj + 2] * fetch_x<UseCache>(*reinterpret_cast<uint_fast64_t const*>(matrixColumnIndicesAndValues + 3 * jj), x);
#endif
//sum += reinterpret_cast<ValueType const*>(matrixColumnIndicesAndValues)[2*jj + 1] * fetch_x<UseCache>(matrixColumnIndicesAndValues[2*jj], x);
}
// accumulate local sums
for(jj += THREADS_PER_VECTOR; jj < row_end; jj += THREADS_PER_VECTOR) {
//sum += reinterpret_cast<ValueType const*>(matrixColumnIndicesAndValues)[2*jj + 1] * fetch_x<UseCache>(matrixColumnIndicesAndValues[2*jj], x);
#ifdef STORM_CUDAPLUGIN_HAVE_64BIT_FLOAT_ALIGNMENT
sum += matrixColumnIndicesAndValues[4 * jj + 2] * fetch_x<UseCache>(*reinterpret_cast<uint_fast64_t const*>(matrixColumnIndicesAndValues + 4 * jj), x);
#else
sum += matrixColumnIndicesAndValues[3 * jj + 2] * fetch_x<UseCache>(*reinterpret_cast<uint_fast64_t const*>(matrixColumnIndicesAndValues + 3 * jj), x);
#endif
}
} else {
// accumulate local sums
for(uint_fast64_t jj = row_start + thread_lane; jj < row_end; jj += THREADS_PER_VECTOR) {
//sum += reinterpret_cast<ValueType const*>(matrixColumnIndicesAndValues)[2*jj + 1] * fetch_x<UseCache>(matrixColumnIndicesAndValues[2*jj], x);
#ifdef STORM_CUDAPLUGIN_HAVE_64BIT_FLOAT_ALIGNMENT
sum += matrixColumnIndicesAndValues[4 * jj + 2] * fetch_x<UseCache>(*reinterpret_cast<uint_fast64_t const*>(matrixColumnIndicesAndValues + 4 * jj), x);
#else
sum += matrixColumnIndicesAndValues[3 * jj + 2] * fetch_x<UseCache>(*reinterpret_cast<uint_fast64_t const*>(matrixColumnIndicesAndValues + 3 * jj), x);
#endif
}
}
// store local sum in shared memory
sdata[threadIdx.x] = sum;
// reduce local sums to row sum
if (THREADS_PER_VECTOR > 16) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 16];
if (THREADS_PER_VECTOR > 8) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 8];
if (THREADS_PER_VECTOR > 4) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 4];
if (THREADS_PER_VECTOR > 2) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 2];
if (THREADS_PER_VECTOR > 1) sdata[threadIdx.x] = sum = sum + sdata[threadIdx.x + 1];
// first thread writes the result
if (thread_lane == 0)
y[row] = sdata[threadIdx.x];
}
}
template <unsigned int ROWS_PER_BLOCK, unsigned int THREADS_PER_ROW, bool Minimize>
__launch_bounds__(ROWS_PER_BLOCK * THREADS_PER_ROW,1)
__global__ void
storm_cuda_opt_vector_reduce_kernel_float(const uint_fast64_t num_rows, const uint_fast64_t * nondeterministicChoiceIndices, float * x, const float * y, const float minMaxInitializer)
{
__shared__ volatile float sdata[ROWS_PER_BLOCK * THREADS_PER_ROW + THREADS_PER_ROW / 2]; // padded to avoid reduction conditionals
__shared__ volatile uint_fast64_t ptrs[ROWS_PER_BLOCK][2];
const uint_fast64_t THREADS_PER_BLOCK = ROWS_PER_BLOCK * THREADS_PER_ROW;
const uint_fast64_t thread_id = THREADS_PER_BLOCK * blockIdx.x + threadIdx.x; // global thread index
const uint_fast64_t thread_lane = threadIdx.x & (THREADS_PER_ROW - 1); // thread index within the vector
const uint_fast64_t vector_id = thread_id / THREADS_PER_ROW; // global vector index
const uint_fast64_t vector_lane = threadIdx.x / THREADS_PER_ROW; // vector index within the block
const uint_fast64_t num_vectors = ROWS_PER_BLOCK * gridDim.x; // total number of active vectors
for(uint_fast64_t row = vector_id; row < num_rows; row += num_vectors)
{
// use two threads to fetch Ap[row] and Ap[row+1]
// this is considerably faster than the straightforward version
if(thread_lane < 2)
ptrs[vector_lane][thread_lane] = nondeterministicChoiceIndices[row + thread_lane];
const uint_fast64_t row_start = ptrs[vector_lane][0]; //same as: row_start = Ap[row];
const uint_fast64_t row_end = ptrs[vector_lane][1]; //same as: row_end = Ap[row+1];
// initialize local Min/Max
float localMinMaxElement = minMaxInitializer;
if (THREADS_PER_ROW == 32 && row_end - row_start > 32)
{
// ensure aligned memory access to Aj and Ax
uint_fast64_t jj = row_start - (row_start & (THREADS_PER_ROW - 1)) + thread_lane;
// accumulate local sums
if(jj >= row_start && jj < row_end) {
if(Minimize) {
localMinMaxElement = min(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement > y[jj]) ? y[jj] : localMinMaxElement;
} else {
localMinMaxElement = max(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement < y[jj]) ? y[jj] : localMinMaxElement;
}
}
// accumulate local sums
for(jj += THREADS_PER_ROW; jj < row_end; jj += THREADS_PER_ROW)
if(Minimize) {
localMinMaxElement = min(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement > y[jj]) ? y[jj] : localMinMaxElement;
} else {
localMinMaxElement = max(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement < y[jj]) ? y[jj] : localMinMaxElement;
}
}
else
{
// accumulate local sums
for(uint_fast64_t jj = row_start + thread_lane; jj < row_end; jj += THREADS_PER_ROW)
if(Minimize) {
localMinMaxElement = min(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement > y[jj]) ? y[jj] : localMinMaxElement;
} else {
localMinMaxElement = max(localMinMaxElement, y[jj]);
//localMinMaxElement = (localMinMaxElement < y[jj]) ? y[jj] : localMinMaxElement;
}
}
// store local sum in shared memory
sdata[threadIdx.x] = localMinMaxElement;
// reduce local min/max to row min/max
if (Minimize) {
/*if (THREADS_PER_ROW > 16) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 16]) ? sdata[threadIdx.x + 16] : localMinMaxElement);
if (THREADS_PER_ROW > 8) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 8]) ? sdata[threadIdx.x + 8] : localMinMaxElement);
if (THREADS_PER_ROW > 4) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 4]) ? sdata[threadIdx.x + 4] : localMinMaxElement);
if (THREADS_PER_ROW > 2) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 2]) ? sdata[threadIdx.x + 2] : localMinMaxElement);
if (THREADS_PER_ROW > 1) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement > sdata[threadIdx.x + 1]) ? sdata[threadIdx.x + 1] : localMinMaxElement);*/
if (THREADS_PER_ROW > 16) sdata[threadIdx.x] = localMinMaxElement = min(localMinMaxElement, sdata[threadIdx.x + 16]);
if (THREADS_PER_ROW > 8) sdata[threadIdx.x] = localMinMaxElement = min(localMinMaxElement, sdata[threadIdx.x + 8]);
if (THREADS_PER_ROW > 4) sdata[threadIdx.x] = localMinMaxElement = min(localMinMaxElement, sdata[threadIdx.x + 4]);
if (THREADS_PER_ROW > 2) sdata[threadIdx.x] = localMinMaxElement = min(localMinMaxElement, sdata[threadIdx.x + 2]);
if (THREADS_PER_ROW > 1) sdata[threadIdx.x] = localMinMaxElement = min(localMinMaxElement, sdata[threadIdx.x + 1]);
} else {
/*if (THREADS_PER_ROW > 16) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 16]) ? sdata[threadIdx.x + 16] : localMinMaxElement);
if (THREADS_PER_ROW > 8) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 8]) ? sdata[threadIdx.x + 8] : localMinMaxElement);
if (THREADS_PER_ROW > 4) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 4]) ? sdata[threadIdx.x + 4] : localMinMaxElement);
if (THREADS_PER_ROW > 2) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 2]) ? sdata[threadIdx.x + 2] : localMinMaxElement);
if (THREADS_PER_ROW > 1) sdata[threadIdx.x] = localMinMaxElement = ((localMinMaxElement < sdata[threadIdx.x + 1]) ? sdata[threadIdx.x + 1] : localMinMaxElement);*/
if (THREADS_PER_ROW > 16) sdata[threadIdx.x] = localMinMaxElement = max(localMinMaxElement, sdata[threadIdx.x + 16]);
if (THREADS_PER_ROW > 8) sdata[threadIdx.x] = localMinMaxElement = max(localMinMaxElement, sdata[threadIdx.x + 8]);
if (THREADS_PER_ROW > 4) sdata[threadIdx.x] = localMinMaxElement = max(localMinMaxElement, sdata[threadIdx.x + 4]);
if (THREADS_PER_ROW > 2) sdata[threadIdx.x] = localMinMaxElement = max(localMinMaxElement, sdata[threadIdx.x + 2]);
if (THREADS_PER_ROW > 1) sdatHTTP/1.1 200 OK Content-Type: text/html; charset=UTF-8 Set-Cookie: i_like_gitea=0a9a46f041cd138f; Path=/; HttpOnly; SameSite=Lax Set-Cookie: _csrf=V90dnt5TNVjJ93T13WtDQHV1c_w6MTczNDk1MDE5ODcyODU0OTA2OQ; Path=/; Expires=Tue, 24 Dec 2024 10:36:38 GMT; HttpOnly; SameSite=Lax Set-Cookie: macaron_flash=; Path=/; Max-Age=0; HttpOnly; SameSite=Lax X-Frame-Options: SAMEORIGIN Date: Mon, 23 Dec 2024 10:36:38 GMT Transfer-Encoding: chunked 1fee5 sp/cln_mirror - COPYING at 1cffe6208b382817fb72ed269b03943ccd88b5d8 - cln_mirror - Gitea: Git with a cup of tea
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GNU GENERAL PUBLIC LICENSE
Version 2, June 1991
Copyright (C) 1989, 1991 Free Software Foundation, Inc.
51 Franklin Street, Fifth Floor, Boston, MA
02110-1301, USA.
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
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OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
POSSIBILITY OF SUCH DAMAGES.
END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
convey the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,
MA 02110-1301, USA.
Also add information on how to contact you by electronic and paper mail.
If the program is interactive, make it output a short notice like this
when it starts in an interactive mode:
Gnomovision version 69, Copyright (C) year name of author
Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, the commands you use may
be called something other than `show w' and `show c'; they could even be
mouse-clicks or menu items--whatever suits your program.
You should also get your employer (if you work as a programmer) or your
school, if any, to sign a "copyright disclaimer" for the program, if
necessary. Here is a sample; alter the names:
Yoyodyne, Inc., hereby disclaims all copyright interest in the program
`Gnomovision' (which makes passes at compilers) written by James Hacker.
<signature of Ty Coon>, 1 April 1989
Ty Coon, President of Vice
This General Public License does not permit incorporating your program into
proprietary programs. If your program is a subroutine library, you may
consider it more useful to permit linking proprietary applications with the
library. If this is what you want to do, use the GNU Library General
Public License instead of this License.
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