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diff --git a/eigen/bench/sparse_product.cpp b/eigen/bench/sparse_product.cpp
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+
+//g++ -O3 -g0 -DNDEBUG  sparse_product.cpp -I.. -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.005 -DSIZE=10000 && ./a.out
+//g++ -O3 -g0 -DNDEBUG  sparse_product.cpp -I.. -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.05 -DSIZE=2000 && ./a.out
+// -DNOGMM -DNOMTL -DCSPARSE
+// -I /home/gael/Coding/LinearAlgebra/CSparse/Include/ /home/gael/Coding/LinearAlgebra/CSparse/Lib/libcsparse.a
+
+#include <typeinfo>
+
+#ifndef SIZE
+#define SIZE 1000000
+#endif
+
+#ifndef NNZPERCOL
+#define NNZPERCOL 6
+#endif
+
+#ifndef REPEAT
+#define REPEAT 1
+#endif
+
+#include <algorithm>
+#include "BenchTimer.h"
+#include "BenchUtil.h"
+#include "BenchSparseUtil.h"
+
+#ifndef NBTRIES
+#define NBTRIES 1
+#endif
+
+#define BENCH(X) \
+  timer.reset(); \
+  for (int _j=0; _j<NBTRIES; ++_j) { \
+    timer.start(); \
+    for (int _k=0; _k<REPEAT; ++_k) { \
+        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)
+{
+//   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;
+
+  EigenSparseMatrix sm1(rows,cols), sm2(rows,cols), sm3(rows,cols), sm4(rows,cols);
+
+  BenchTimer timer;
+  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
+    {
+      std::cout << "Eigen Dense\t" << nnzPerCol << "%\n";
+      DenseMatrix m1(rows,cols), m2(rows,cols), m3(rows,cols);
+      eiToDense(sm1, m1);
+      eiToDense(sm2, m2);
+
+      timer.reset();
+      timer.start();
+      for (int k=0; k<REPEAT; ++k)
+        m3 = m1 * m2;
+      timer.stop();
+      std::cout << "   a * b:\t" << timer.value() << endl;
+
+      timer.reset();
+      timer.start();
+      for (int k=0; k<REPEAT; ++k)
+        m3 = m1.transpose() * m2;
+      timer.stop();
+      std::cout << "   a' * b:\t" << timer.value() << endl;
+
+      timer.reset();
+      timer.start();
+      for (int k=0; k<REPEAT; ++k)
+        m3 = m1.transpose() * m2.transpose();
+      timer.stop();
+      std::cout << "   a' * b':\t" << timer.value() << endl;
+
+      timer.reset();
+      timer.start();
+      for (int k=0; k<REPEAT; ++k)
+        m3 = m1 * m2.transpose();
+      timer.stop();
+      std::cout << "   a * b':\t" << timer.value() << endl;
+    }
+    #endif
+
+    // eigen sparse matrices
+    {
+      std::cout << "Eigen sparse\t" << sm1.nonZeros()/(float(sm1.rows())*float(sm1.cols()))*100 << "% * "
+                << sm2.nonZeros()/(float(sm2.rows())*float(sm2.cols()))*100 << "%\n";
+
+      BENCH(sm3 = sm1 * sm2; )
+      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;
+
+
+//       std::cout << "\n";
+//
+//       BENCH( sm3._experimentalNewProduct(sm1, sm2); )
+//       std::cout << "   a * b:\t" << timer.value() << endl;
+//
+//       BENCH(sm3._experimentalNewProduct(sm1.transpose(),sm2); )
+//       std::cout << "   a' * b:\t" << timer.value() << endl;
+// //
+//       BENCH(sm3._experimentalNewProduct(sm1.transpose(),sm2.transpose()); )
+//       std::cout << "   a' * b':\t" << timer.value() << endl;
+// //
+//       BENCH(sm3._experimentalNewProduct(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";
+
+//       timer.reset();
+//       timer.start();
+      BENCH(for (int k=0; k<REPEAT; ++k) m3 = m1 * m2;)
+//       timer.stop();
+      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) m3 = m1.transpose() * m2;)
+//       timer.stop();
+      std::cout << "   a' * b:\t" << timer.value() << endl;
+
+//       timer.reset();
+//       timer.start();
+      BENCH( for (int k=0; k<REPEAT; ++k) m3 = m1.transpose() * m2.transpose(); )
+//       timer.stop();
+      std::cout << "   a' * b':\t" << timer.value() << endl;
+
+//       timer.reset();
+//       timer.start();
+      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
+    {
+      std::cout << "CSparse \t" << nnzPerCol << "%\n";
+      cs *m1, *m2, *m3;
+      eiToCSparse(sm1, m1);
+      eiToCSparse(sm2, m2);
+
+      BENCH(
+      {
+        m3 = cs_sorted_multiply(m1, m2);
+        if (!m3)
+        {
+          std::cerr << "cs_multiply failed\n";
+        }
+//         cs_print(m3, 0);
+        cs_spfree(m3);
+      }
+      );
+//       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
+
+    #ifndef NOUBLAS
+    {
+      std::cout << "ublas\t" << nnzPerCol << "%\n";
+      UBlasSparse m1(rows,cols), m2(rows,cols), m3(rows,cols);
+      eiToUblas(sm1, m1);
+      eiToUblas(sm2, m2);
+
+      BENCH(boost::numeric::ublas::prod(m1, m2, m3););
+      std::cout << "   a * b:\t" << timer.value() << endl;
+    }
+    #endif
+
+    // GMM++
+    #ifndef NOGMM
+    {
+      std::cout << "GMM++ sparse\t" << nnzPerCol << "%\n";
+      GmmDynSparse  gmmT3(rows,cols);
+      GmmSparse m1(rows,cols), m2(rows,cols), m3(rows,cols);
+      eiToGmm(sm1, m1);
+      eiToGmm(sm2, m2);
+
+      BENCH(gmm::mult(m1, m2, gmmT3););
+      std::cout << "   a * b:\t" << timer.value() << endl;
+
+//       BENCH(gmm::mult(gmm::transposed(m1), m2, gmmT3););
+//       std::cout << "   a' * b:\t" << timer.value() << endl;
+//
+//       if (rows<500)
+//       {
+//         BENCH(gmm::mult(gmm::transposed(m1), gmm::transposed(m2), gmmT3););
+//         std::cout << "   a' * b':\t" << timer.value() << endl;
+//
+//         BENCH(gmm::mult(m1, gmm::transposed(m2), gmmT3););
+//         std::cout << "   a * b':\t" << timer.value() << endl;
+//       }
+//       else
+//       {
+//         std::cout << "   a' * b':\t" << "forever" << endl;
+//         std::cout << "   a * b':\t" << "forever" << endl;
+//       }
+    }
+    #endif
+
+    // MTL4
+    #ifndef NOMTL
+    {
+      std::cout << "MTL4\t" << nnzPerCol << "%\n";
+      MtlSparse m1(rows,cols), m2(rows,cols), m3(rows,cols);
+      eiToMtl(sm1, m1);
+      eiToMtl(sm2, m2);
+
+      BENCH(m3 = m1 * m2;);
+      std::cout << "   a * b:\t" << timer.value() << endl;
+
+//       BENCH(m3 = trans(m1) * m2;);
+//       std::cout << "   a' * b:\t" << timer.value() << endl;
+//
+//       BENCH(m3 = trans(m1) * trans(m2););
+//       std::cout << "  a' * b':\t" << timer.value() << endl;
+//
+//       BENCH(m3 = m1 * trans(m2););
+//       std::cout << "   a * b' :\t" << timer.value() << endl;
+    }
+    #endif
+
+    std::cout << "\n\n";
+  }
+
+  return 0;
+}
+
+
+