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DTSTAMP:20210402T160544Z
LOCATION:Poster Module
DTSTART;TZID=America/New_York:20201119T083000
DTEND;TZID=America/New_York:20201119T170000
UID:submissions.supercomputing.org_SC20_sess337_rpost114@linklings.com
SUMMARY:SaddlebagX: High Performance Data Processing with PGAS and UPC++
DESCRIPTION:Posters, Research Posters\n\nSaddlebagX: High Performance Data
  Processing with PGAS and UPC++\n\nB. Ovesen, M. Khan, Chau, Ha\n\nThe eve
 r growing scale of the data and the emerging challenges of processing it e
 fficiently at that scale are driving the need for high-performance data pr
 ocessing frameworks that\nare efficient yet highly programmable across big
  data analytics (BDA) and high-performance computing (HPC) communities. We
  introduce SaddlebagX, a new data-centric framework that\noffers the high 
 programmability of BDA frameworks, such as the simple BSP programming mode
 l, while striving for high performance by leveraging the Partitioned Globa
 lly Address Space (PGAS) computing paradigm and the Remote Memory Access (
 RMA) capability provided in HPC frameworks. Graph analytics and non-iterat
 ive bulk transformation benchmarks using SaddlebagX show significant perfo
 rmance gains (up to 40×) compared to the Apache Spark counterparts. To eva
 luate the overheads incurred by SaddlebagX, we compare SaddlebagX with UPC
 ++ using a basic sparse matrix vector multiplication (SpMV) benchmark, and
  observe no noticeable slowdown. These results show that SaddlebagX is a h
 igh-performance\ndata processing framework.\n\nRegistration Category: Tech
  Program Reg Pass, Exhibits Reg Pass
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