extend benchmark for sparse products
diff --git a/bench/BenchSparseUtil.h b/bench/BenchSparseUtil.h
index f8dc8bd..39db693 100644
--- a/bench/BenchSparseUtil.h
+++ b/bench/BenchSparseUtil.h
@@ -42,7 +42,7 @@
void fillMatrix2(int nnzPerCol, int rows, int cols, EigenSparseMatrix& dst)
{
- std::cout << "alloc " << nnzPerCol*cols << "\n";
+// std::cout << "alloc " << nnzPerCol*cols << "\n";
dst.reserve(nnzPerCol*cols);
for(int j = 0; j < cols; j++)
{
diff --git a/bench/sparse_product.cpp b/bench/sparse_product.cpp
index f1524d2..0c72993 100644
--- a/bench/sparse_product.cpp
+++ b/bench/sparse_product.cpp
@@ -8,17 +8,19 @@
#endif
#ifndef NNZPERCOL
-#define NNZPERCOL 2
+#define NNZPERCOL 32
#endif
#ifndef REPEAT
#define REPEAT 1
#endif
+#include <algorithm>
+#include "BenchTimer.h"
#include "BenchSparseUtil.h"
#ifndef NBTRIES
-#define NBTRIES 1
+#define NBTRIES 4
#endif
#define BENCH(X) \
@@ -29,24 +31,67 @@
X \
} timer.stop(); }
+// #ifdef MKL
+//
+// #include "mkl_types.h"
+// #include "mkl_spblas.h"
+//
+// template<typename Lhs,typename Rhs,typename Res>
+// void mkl_multiply(const Lhs& lhs, const Rhs& rhs, Res& res)
+// {
+// char n = 'N';
+// float alpha = 1;
+// char matdescra[6];
+// matdescra[0] = 'G';
+// matdescra[1] = 0;
+// matdescra[2] = 0;
+// matdescra[3] = 'C';
+// mkl_scscmm(&n, lhs.rows(), rhs.cols(), lhs.cols(), &alpha, matdescra,
+// lhs._valuePtr(), lhs._innerIndexPtr(), lhs.outerIndexPtr(),
+// pntre, b, &ldb, &beta, c, &ldc);
+// // mkl_somatcopy('C', 'T', lhs.rows(), lhs.cols(), 1,
+// // lhs._valuePtr(), lhs.rows(), DST, dst_stride);
+// }
+//
+// #endif
+
#ifdef CSPARSE
cs* cs_sorted_multiply(const cs* a, const cs* b)
{
- cs* A = cs_transpose (a, 1) ;
- cs* B = cs_transpose (b, 1) ;
- cs* D = cs_multiply (B,A) ; /* D = B'*A' */
+// return cs_multiply(a,b);
+ cs* A = cs_transpose(a, 1);
+ cs* B = cs_transpose(b, 1);
+ cs* D = cs_multiply(B,A); /* D = B'*A' */
cs_spfree (A) ;
cs_spfree (B) ;
cs_dropzeros (D) ; /* drop zeros from D */
cs* C = cs_transpose (D, 1) ; /* C = D', so that C is sorted */
cs_spfree (D) ;
return C;
+
+// cs* A = cs_transpose(a, 1);
+// cs* C = cs_transpose(A, 1);
+// return C;
+}
+
+cs* cs_sorted_multiply2(const cs* a, const cs* b)
+{
+ cs* D = cs_multiply(a,b);
+ cs* E = cs_transpose(D,1);
+ cs_spfree(D);
+ cs* C = cs_transpose(E,1);
+ cs_spfree(E);
+ return C;
}
#endif
+void bench_sort();
+
int main(int argc, char *argv[])
{
+// bench_sort();
+
int rows = SIZE;
int cols = SIZE;
float density = DENSITY;
@@ -54,10 +99,13 @@
EigenSparseMatrix sm1(rows,cols), sm2(rows,cols), sm3(rows,cols), sm4(rows,cols);
BenchTimer timer;
- for (int nnzPerCol = NNZPERCOL; nnzPerCol>1; nnzPerCol/=2)
+ for (int nnzPerCol = NNZPERCOL; nnzPerCol>1; nnzPerCol/=1.1)
{
+ sm1.setZero();
+ sm2.setZero();
fillMatrix2(nnzPerCol, rows, cols, sm1);
fillMatrix2(nnzPerCol, rows, cols, sm2);
+// std::cerr << "filling OK\n";
// dense matrices
#ifdef DENSEMATRIX
@@ -102,40 +150,36 @@
std::cout << "Eigen sparse\t" << sm1.nonZeros()/(float(sm1.rows())*float(sm1.cols()))*100 << "% * "
<< sm2.nonZeros()/(float(sm2.rows())*float(sm2.cols()))*100 << "%\n";
-// timer.reset();
-// timer.start();
- BENCH(for (int k=0; k<REPEAT; ++k) sm3 = sm1 * sm2;)
-// timer.stop();
+ BENCH(sm3 = sm1 * sm2; )
std::cout << " a * b:\t" << timer.value() << endl;
-// std::cout << sm3 << "\n";
- timer.reset();
- timer.start();
-// std::cerr << "transpose...\n";
-// EigenSparseMatrix sm4 = sm1.transpose();
-// std::cout << sm4.nonZeros() << " == " << sm1.nonZeros() << "\n";
-// exit(1);
-// std::cerr << "transpose OK\n";
-// std::cout << sm1 << "\n\n" << sm1.transpose() << "\n\n" << sm4.transpose() << "\n\n";
- BENCH(for (int k=0; k<REPEAT; ++k) sm3 = sm1.transpose() * sm2;)
-// timer.stop();
- std::cout << " a' * b:\t" << timer.value() << endl;
+// BENCH(sm3 = sm1.transpose() * sm2; )
+// std::cout << " a' * b:\t" << timer.value() << endl;
+//
+// BENCH(sm3 = sm1.transpose() * sm2.transpose(); )
+// std::cout << " a' * b':\t" << timer.value() << endl;
+//
+// BENCH(sm3 = sm1 * sm2.transpose(); )
+// std::cout << " a * b' :\t" << timer.value() << endl;
-// timer.reset();
-// timer.start();
- BENCH( for (int k=0; k<REPEAT; ++k) sm3 = sm1.transpose() * sm2.transpose(); )
-// timer.stop();
- std::cout << " a' * b':\t" << timer.value() << endl;
-// timer.reset();
-// timer.start();
- BENCH( for (int k=0; k<REPEAT; ++k) sm3 = sm1 * sm2.transpose(); )
-// timer.stop();
- std::cout << " a * b' :\t" << timer.value() << endl;
+// std::cout << "\n\n";
+
+ BENCH( sm3.setprod(sm1, sm2); )
+ std::cout << " a * b:\t" << timer.value() << endl;
+
+// BENCH(sm3.setprod(sm1.transpose(),sm2); )
+// std::cout << " a' * b:\t" << timer.value() << endl;
+//
+// BENCH(sm3.setprod(sm1.transpose(),sm2.transpose()); )
+// std::cout << " a' * b':\t" << timer.value() << endl;
+//
+// BENCH(sm3.setprod(sm1, sm2.transpose());)
+// std::cout << " a * b' :\t" << timer.value() << endl;
}
// eigen dyn-sparse matrices
- {
+ /*{
DynamicSparseMatrix<Scalar> m1(sm1), m2(sm2), m3(sm3);
std::cout << "Eigen dyn-sparse\t" << m1.nonZeros()/(float(m1.rows())*float(m1.cols()))*100 << "% * "
<< m2.nonZeros()/(float(m2.rows())*float(m2.cols()))*100 << "%\n";
@@ -170,7 +214,7 @@
BENCH( for (int k=0; k<REPEAT; ++k) m3 = m1 * m2.transpose(); )
// timer.stop();
std::cout << " a * b' :\t" << timer.value() << endl;
- }
+ }*/
// CSparse
#ifdef CSPARSE
@@ -180,9 +224,10 @@
eiToCSparse(sm1, m1);
eiToCSparse(sm2, m2);
- timer.reset();
- timer.start();
- for (int k=0; k<REPEAT; ++k)
+// timer.reset();
+// timer.start();
+// for (int k=0; k<REPEAT; ++k)
+ BENCH(
{
m3 = cs_sorted_multiply(m1, m2);
if (!m3)
@@ -193,8 +238,12 @@
// cs_print(m3, 0);
cs_spfree(m3);
}
- timer.stop();
+ );
+// timer.stop();
std::cout << " a * b:\t" << timer.value() << endl;
+
+// BENCH( { m3 = cs_sorted_multiply2(m1, m2); cs_spfree(m3); } );
+// std::cout << " a * b:\t" << timer.value() << endl;
}
#endif
@@ -289,3 +338,5 @@
return 0;
}
+
+
