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dc.contributor.advisor Sarkar, Vivek
dc.creatorZhang, Yunming
dc.date.accessioned 2016-01-07T22:00:11Z
dc.date.available 2016-01-07T22:00:11Z
dc.date.created 2014-12
dc.date.issued 2014-05-27
dc.date.submitted December 2014
dc.identifier.citation Zhang, Yunming. "Optimized Runtime Systems for MapReduce Applications in Multi-core Clusters." (2014) Master’s Thesis, Rice University. https://hdl.handle.net/1911/87782.
dc.identifier.urihttps://hdl.handle.net/1911/87782
dc.description.abstract This research proposes a novel runtime system, Habanero Hadoop, to tackle the inefficient utilization of multi-core machines' memory in the existing Hadoop MapReduce runtime system. Insufficient memory for each map task leads to the inability to tackle large-scale problems such as genome sequencing and data clustering. The Habanero Hadoop system integrates a shared memory model into the fully distributed memory model of the Hadoop MapReduce system. The improvements eliminate duplication of in-memory data structures used in the map phase, making more memory available to each map task. Previous works optimizing multi-core performance for MapReduce runtime focused on maximizing CPU utilization rather than memory efficiency. My work provided multiple approaches to significantly improve the memory efficiency of the Hadoop MapReduce runtime. The optimized Habanero Hadoop runtime can increase the throughput and maximum input size for certain widely used data analytics applications such as Kmeans and Hash Join by 2x.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectMapReduce
Memory
dc.title Optimized Runtime Systems for MapReduce Applications in Multi-core Clusters
dc.contributor.committeeMember Cox, Alan
dc.contributor.committeeMember Mellor-Crummey, John
dc.date.updated 2016-01-07T22:00:11Z
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Computer Science
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Masters
thesis.degree.name Master of Science


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