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.
|Number of pages||4|
|Publication status||Accepted - 15 Mar 2016|
|Event||1st International Workshop on Multi-Engine Data Analytics (MEDAL) - Bordeaux, France|
Duration: 15 Mar 2016 → 15 Mar 2016
|Conference||1st International Workshop on Multi-Engine Data Analytics (MEDAL)|
|Period||15/03/2016 → 15/03/2016|
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.