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implement a smarter parallelization strategy for gemm avoiding multiple

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paking of the same data
This commit is contained in:
Gael Guennebaud
2010-02-26 12:32:00 +01:00
parent a1e1103328
commit 3ac2b96a2f
5 changed files with 246 additions and 138 deletions
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83
bench/bench_gemm_blas.cpp
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@@ -1,83 +0,0 @@
#include <Eigen/Core>
#include <bench/BenchTimer.h>
extern "C"
{
#include <bench/btl/libs/C_BLAS/blas.h>
#include <cblas.h>
}
using namespace std;
using namespace Eigen;
#ifndef SCALAR
#define SCALAR float
#endif
typedef SCALAR Scalar;
typedef Matrix<Scalar,Dynamic,Dynamic> M;
static float fone = 1;
static float fzero = 0;
static double done = 1;
static double szero = 0;
static char notrans = 'N';
static char trans = 'T';
static char nonunit = 'N';
static char lower = 'L';
static char right = 'R';
static int intone = 1;
void blas_gemm(const MatrixXf& a, const MatrixXf& b, MatrixXf& c)
{
int M = c.rows();
int N = c.cols();
int K = a.cols();
int lda = a.rows();
int ldb = b.rows();
int ldc = c.rows();
sgemm_(&notrans,&notrans,&M,&N,&K,&fone,
const_cast<float*>(a.data()),&lda,
const_cast<float*>(b.data()),&ldb,&fone,
c.data(),&ldc);
}
void blas_gemm(const MatrixXd& a, const MatrixXd& b, MatrixXd& c)
{
int M = c.rows();
int N = c.cols();
int K = a.cols();
int lda = a.rows();
int ldb = b.rows();
int ldc = c.rows();
dgemm_(&notrans,&notrans,&M,&N,&K,&done,
const_cast<double*>(a.data()),&lda,
const_cast<double*>(b.data()),&ldb,&done,
c.data(),&ldc);
}
int main(int argc, char **argv)
{
int rep = 1;
int s = 2048;
int m = s;
int n = s;
int p = s;
M a(m,n); a.setOnes();
M b(n,p); b.setOnes();
M c(m,p); c.setOnes();
BenchTimer t;
BENCH(t, 5, rep, blas_gemm(a,b,c));
std::cerr << "cpu " << t.best(CPU_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/t.best(CPU_TIMER))*1e-9 << " GFLOPS \t(" << t.total(CPU_TIMER) << "s)\n";
std::cerr << "real " << t.best(REAL_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/t.best(REAL_TIMER))*1e-9 << " GFLOPS \t(" << t.total(REAL_TIMER) << "s)\n";
return 0;
}