Optimizing xeon phi for interactive data analysis

Chansup Byun, Anne Klein, Lauren Milechin, Peter Michaleas, Julie Mullen, Andrew Prout, Antonio Rosa, Siddharth Samsi, Charles Yee, Albert Reuther, Jeremy Kepner, William Arcand, David Bestor, William Bergeron, Matthew Hubbell, Vijay Gadepally, Michael Houle, Michael Jones

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations


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 KMPAFFINITY=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.

Original languageEnglish
Title of host publication2019 IEEE High Performance Extreme Computing Conference, HPEC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728150208
StatePublished - Sep 2019
Externally publishedYes
Event2019 IEEE High Performance Extreme Computing Conference, HPEC 2019 - Waltham, United States
Duration: Sep 24 2019Sep 26 2019

Publication series

Name2019 IEEE High Performance Extreme Computing Conference, HPEC 2019


Conference2019 IEEE High Performance Extreme Computing Conference, HPEC 2019
Country/TerritoryUnited States


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