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Syst."],"published-print":{"date-parts":[[2018,9,30]]},"abstract":"<jats:p>The rapid growth in data yields challenges to process data efficiently using current high-performance server architectures such as big Xeon cores. Furthermore, physical design constraints, such as power and density, have become the dominant limiting factor for scaling out servers.\u00a0Low-power embedded cores in servers such as little Atom\u00a0have emerged as a promising solution to enhance energy-efficiency\u00a0to address these challenges.\u00a0Therefore, the question of whether to\u00a0process the big data\u00a0applications on big Xeon- or Little Atom-based servers becomes important.\u00a0In this work, through methodical investigation of power and performance measurements, and comprehensive application-level, system-level, and micro-architectural level analysis, we characterize dominant big data applications on big Xeon- and little Atom-based server architectures. The characterization results across a wide range of real-world big data applications, and various software stacks demonstrate how the choice of big- versus little-core-based server for energy-efficiency is significantly influenced by the size of data, performance constraints, and presence of accelerator.\u00a0In addition, we analyze processor resource utilization of this important class of applications,\u00a0such as\u00a0memory footprints, CPU\u00a0utilization, and disk bandwidth,\u00a0to understand their run-time behavior.\u00a0Furthermore, we perform micro-architecture-level analysis to highlight where improvement is needed in big- and little-core microarchitectures to address their performance bottlenecks.<\/jats:p>","DOI":"10.1145\/3229049","type":"journal-article","created":{"date-parts":[[2018,7,24]],"date-time":"2018-07-24T14:41:49Z","timestamp":1532443309000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["System and Architecture Level Characterization of Big Data Applications on Big and Little Core Server Architectures"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8425-2501","authenticated-orcid":false,"given":"Maria","family":"Malik","sequence":"first","affiliation":[{"name":"George Mason University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Setareh","family":"Rafatirad","sequence":"additional","affiliation":[{"name":"George Mason University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Houman","family":"Homayoun","sequence":"additional","affiliation":[{"name":"George Mason University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,7,23]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Accelerating Hadoop* applications using Intel QuickAssist tech. 2013.  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