Changes

In this instance the MKL used DGEMM to improve the calculation time. DGEMM stands for '''D'''ouble-precision, '''GE'''neral '''M'''atrix-'''M'''atrix multiplication. In the example used to demonstrate matrix multiplication, the code defines the multiplication of two matrices along with scaling factors alpha and beta. It can be noted that without MKL implementation the matrix multiplication is done though nested loops, however in the MKL optimized version cblas_dgemm() is called. The dgemm refers to DGEMM defined above and cblas refers to the CBLAS interface, which stands for '''B'''asic '''L'''inear '''A'''lgebra '''S'''ubprograms in '''C'''. One part of BLAS, level 3, is dedicated to matrix-matrix operations, which in this case includes the matrix multiplication calculations. While the math and logic behind the implementation of the cblas_dgemm() function is fairly complicated, a simplified explanation on how it works can be expressed as the decomposition of either one or both of the matrices being multiplied and taking advantage of cache memory to improve computation speed.