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/* Ergo, version 3.5, a program for linear scaling electronic structure
* calculations.
* Copyright (C) 2016 Elias Rudberg, Emanuel H. Rubensson, Pawel Salek,
* and Anastasia Kruchinina.
*
* 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 3 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, see <http://www.gnu.org/licenses/>.
*
* Primary academic reference:
* KohnâSham Density Functional Theory Electronic Structure Calculations
* with Linearly Scaling Computational Time and Memory Usage,
* Elias Rudberg, Emanuel H. Rubensson, and Pawel Salek,
* J. Chem. Theory Comput. 7, 340 (2011),
* <http://dx.doi.org/10.1021/ct100611z>
*
* For further information about Ergo, see <http://www.ergoscf.org>.
*/
#include <stdio.h>
#include <unistd.h>
#include <memory>
#include <limits>
#include "matrix_typedefs.h"
#include "matrix_utilities.h"
#include "utilities.h"
/** @file mat_performance_test.cc Performs some matrix-matrix
multiplication operations and outputs timings. The point is to
show how different block sizes affects the performance of
matrix-matrix multiplication using the hierarchic matrix
library. */
static void get_random_matrix_full(int n, std::vector<ergo_real> & fullMat) {
for (int col = 0; col < n; ++col)
for (int row = 0; row < n; ++row) {
ergo_real randomNumber = ((ergo_real)rand() / (ergo_real)RAND_MAX);
fullMat[row + col * n] = randomNumber;
}
}
static void get_matrix_from_full(normalMatrix & result,
mat::SizesAndBlocks sizeBlockInfo,
const std::vector<ergo_real> & fullMat) {
result.resetSizesAndBlocks(sizeBlockInfo, sizeBlockInfo);
result.assignFromFull(fullMat);
}
static void verify_mmul_result(int n,
const std::vector<ergo_real> & fullMat_A,
const std::vector<ergo_real> & fullMat_B,
const normalMatrix & C) {
// Choose some matrix elements to check.
const int noOfElements = 2*n;
std::vector<int> rowIdxList(noOfElements);
std::vector<int> colIdxList(noOfElements);
std::vector<ergo_real> valuesList(noOfElements);
for(int i = 0; i < noOfElements; i++) {
rowIdxList[i] = rand() % n;
colIdxList[i] = rand() % n;
}
C.get_values(rowIdxList, colIdxList, valuesList);
for(int i = 0; i < noOfElements; i++) {
int row = rowIdxList[i];
int col = colIdxList[i];
ergo_real value = valuesList[i];
ergo_real sum = 0;
for(int k = 0; k < n; k++)
sum += fullMat_A[k*n+row] * fullMat_B[col*n+k];
if(fabs(sum - value) > 1e-7)
throw std::runtime_error("ERROR: matrix-matrix multiplication gave wrong result.");
}
}
int main(int argc, char *argv[])
{
int n = 50;
int blockSize_min = 12;
int blockSize_max = 32;
int blockSize_step = 2;
if(argc > 1 && argc != 5) {
std::cout << "This program expects the following arguments:" << std::endl;
std::cout << "./mat_performance_test n blockSize_min blockSize_max blockSize_step" << std::endl;
return -1;
}
if(argc == 5) {
n = atoi(argv[1]);
blockSize_min = atoi(argv[2]);
blockSize_max = atoi(argv[3]);
blockSize_step = atoi(argv[4]);
}
std::cout << "n = " << n << std::endl;
std::cout << "blockSize_min = " << blockSize_min << std::endl;
std::cout << "blockSize_max = " << blockSize_max << std::endl;
std::cout << "blockSize_step = " << blockSize_step << std::endl;
if(n <= 1)
throw std::runtime_error("ERROR: (n <= 1)");
#ifdef _OPENMP
int defThreads;
const char *env = getenv("OMP_NUM_THREADS");
if ( !(env && (defThreads=atoi(env)) > 0) )
defThreads = 1;
mat::Params::setNProcs(defThreads);
mat::Params::setMatrixParallelLevel(1);
std::cout<<"OpenMP is used, number of threads set to "
<<mat::Params::getNProcs()<<". Matrix parallel level: "
<<mat::Params::getMatrixParallelLevel()<<"."<<std::endl;
#endif
// Set up full matrices A and B.
std::vector<ergo_real> fullMat_A(n*n);
get_random_matrix_full(n, fullMat_A);
std::vector<ergo_real> fullMat_B(n*n);
get_random_matrix_full(n, fullMat_B);
// Set up vector with blocksizes to test.
std::vector<int> blockSizes(444);
int count = 0;
blockSizes.at(count) = blockSize_min;
while(blockSizes.at(count) < blockSize_max) {
count++;
blockSizes.at(count) = blockSizes.at(count-1) + blockSize_step;
}
int extraBlockSizes[] = {64, 128, 256, 512, 1024, 2048};
int noOfExtraBlockSizes = sizeof(extraBlockSizes) / sizeof(int);
for(int i = 0; i < noOfExtraBlockSizes; i++) {
int extraBlockSize = extraBlockSizes[i];
if(blockSizes.at(count) < extraBlockSize && extraBlockSize < n)
blockSizes.at(++count) = extraBlockSize;
}
blockSizes.at(++count) = n;
int noOfBlockSizes = count;
for(int i = 0; i < noOfBlockSizes; i++) {
int blockSize = blockSizes[i];
mat::SizesAndBlocks sizeBlockInfo;
static const int sparseMatrixBlockFactor = 2;
sizeBlockInfo =
prepareMatrixSizesAndBlocks(n,
blockSize,
sparseMatrixBlockFactor,
sparseMatrixBlockFactor,
sparseMatrixBlockFactor);
// Create random matrices A and B using the current block size.
normalMatrix A, B;
get_matrix_from_full(A, sizeBlockInfo, fullMat_A);
get_matrix_from_full(B, sizeBlockInfo, fullMat_B);
// Compute C = A * B
normalMatrix C;
Util::TimeMeter tm;
C.resetSizesAndBlocks(sizeBlockInfo, sizeBlockInfo);
C = A * B;
double secondsTaken = tm.get_wall_seconds() - tm.get_start_time_wall_seconds();
std::cout << "C = A * B operation using blockSize "
<< std::setw(7) << blockSize << " took "
<< std::setw(12) << std::setprecision(5) << std::fixed << secondsTaken << " wall seconds." << std::endl;
// Verify result.
verify_mmul_result(n, fullMat_A, fullMat_B, C);
}
puts("mat_performance_test finished OK.");
return 0;
}
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