Operator and Workflow Optimization for High-Performance Analytics

Hans Vandierendonck, Karen L. Murphy, Mahwish Arif, Jiawen Sun, Dimitrios S. Nikolopoulos

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Abstract

We make a case for studying the impact of intra-node parallelism on the performance of data analytics. We identify four performance optimizations that are enabled by an increasing number of processing cores on a chip. We discuss the performance impact of these opimizations on two analytics operators and we identify how these optimizations affect each another.
Original languageEnglish
Number of pages4
Publication statusAccepted - 15 Mar 2016
Event1st International Workshop on Multi-Engine Data Analytics (MEDAL) - Bordeaux, France
Duration: 15 Mar 201615 Mar 2016

Conference

Conference1st International Workshop on Multi-Engine Data Analytics (MEDAL)
CountryFrance
CityBordeaux
Period15/03/201615/03/2016

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  • Cite this

    Vandierendonck, H., Murphy, K. L., Arif, M., Sun, J., & Nikolopoulos, D. S. (Accepted/In press). Operator and Workflow Optimization for High-Performance Analytics. Paper presented at 1st International Workshop on Multi-Engine Data Analytics (MEDAL), Bordeaux, France.