Panacea: Towards holistic optimization of MapReduce applications

Jun Liu, Nishkam Ravi, Srimat Chakradhar, Mahmut Kandemir

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

Abstract

MapReduce has emerged as one of the most popular programming models for data parallel enterprise applications. Despite advances in runtime, the opportunities for optimizing MapReduce applications remain largely unexplored. In this paper, we present a framework for performing holistic compiler optimizations on legacy MapReduce applications. We have identified and implemented two optimizations and evaluated them with a set of Hadoop applications on a cluster of Xeon servers. Our experiments show that performance gains of more than 3X can be achieved without user involvement.

Original languageEnglish (US)
Title of host publicationProceedings - International Symposium on Code Generation and Optimization, CGO 2012
Pages33-43
Number of pages11
DOIs
StatePublished - 2012
Event10th International Symposium on Code Generation and Optimization, CGO 2012 - San Jose, CA, United States
Duration: Mar 31 2012Apr 4 2012

Publication series

NameProceedings - International Symposium on Code Generation and Optimization, CGO 2012

Other

Other10th International Symposium on Code Generation and Optimization, CGO 2012
CountryUnited States
CitySan Jose, CA
Period3/31/124/4/12

All Science Journal Classification (ASJC) codes

  • Software

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