  The Intel Xeon Phi manycore processor is designed to provide high performance
matrix computations of the type often performed in data analysis. Common data
analysis environments include Matlab, GNU Octave, Julia, Python, and R.
Achieving optimal performance of matrix operations within data analysis
environments requires tuning the Xeon Phi OpenMP settings, process pinning, and
memory modes. This paper describes matrix multiplication performance results
for Matlab and GNU Octave over a variety of combinations of process counts and
OpenMP threads and Xeon Phi memory modes. These results indicate that using
KMP_AFFINITY=granlarity=fine, taskset pinning, and all2all cache memory mode
allows both Matlab and GNU Octave to achieve 66% of the practical peak
performance for process counts ranging from 1 to 64 and OpenMP threads ranging
from 1 to 64. These settings have resulted in generally improved performance
across a range of applications and has enabled our Xeon Phi system to deliver
significant results in a number of real-world applications.
