miscellaneousSparse matrixdense vector products using intel MKLIn Matlab 2013b, dense linear algebra operations have been generally well optimized by using BLAS and LAPACK tuned to the CPU processors in use. On the other hand, we have observed that some sparse linear algebra operations in Matlab 2013b seem to have not been as highly optimized. In particular, when doing multiplications between a sparse matrix and a dense vector or matrix (denoted by ‘‘SpMM’’) the performance of Matlab's own version of SPMM can differ significantly from that of the corresponding routine in Intel's Math Kernel Library (MKL), which is named ‘‘mkl_dcscmm’’.
seed: 88137586, A*B: err 0.00e+00, matlabcpu: 0.23, mklcpu: 0.10 seed: 87017600, A*B: err 0.00e+00, matlabcpu: 0.22, mklcpu: 0.11 seed: 49049047, A*B: err 0.00e+00, matlabcpu: 0.23, mklcpu: 0.10
