This repository is a fork of Mike Croucher's awesome performance test Jupyter Notebook, adding the same performance test on IBM's Data Science Experience platform. A copy of the DSX notebook can be found here, and this can be easily imported into DSX by clicking 'Create Notebook' and selecting 'create from URL'.
Matrix-Matrix multiplication is often used to benchmark machines because the mathematics is such that it is one of the few operations where one can obtain close to theoretical peak performance in pratice.
The number of floating point operations(Flops) in a Matrix-Matrix multiplication of two
For this benchmark, we construct two random
For highest performance, you should use a version of numpy that has been linked against a high performance BLAS library such as OpenBLAS or the Intel MKL(https://software.intel.com/en-us/intel-mkl). The Anaconda Python distribution includes the Intel MKL by default on Windows and Linux (Mac includes its own high performance BLAS library).