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DTSTAMP:20260522T150116Z
LOCATION:D167/174
DTSTART;TZID=America/Chicago:20181112T163000
DTEND;TZID=America/Chicago:20181112T170000
UID:submissions.supercomputing.org_SC18_sess151_ws_mlhpce121@linklings.com
SUMMARY:Optimizing Machine Learning on Apache Spark in HPC Environments
DESCRIPTION:Zhenyu Li, James Davis, and Stephen Jarvis (University of Warw
 ick)\n\nMachine learning has established itself as a powerful tool for the
  construction of decision making models and algorithms through the use of 
 statistical techniques on training data. However, a significant impediment
  to its progress is the time spent training and improving the accuracy of 
 these models. A common approach to accelerate this process is to employ th
 e use of multiple machines simultaneously, a trait shared with the field o
 f High Performance Computing (HPC) and its clusters. However, existing dis
 tributed frameworks for data analytics and machine learning are designed f
 or commodity servers, which do not realize the full potential of a HPC clu
 ster.\n\nIn this work, we adapt the application of Apache Spark, a distrib
 uted data-flow framework, to support the use of machine learning in HPC en
 vironments for the purposes of machine learning. There are inherent challe
 nges to using Spark in this context;  memory management, communication cos
 ts and synchronization overheads all pose challenges to its efficiency. To
  this end we introduce: (i) the application of MapRDD, a fine grained dist
 ributed data representation; (ii) a task-based all-reduce implementation; 
 and (iii) a new asynchronous Stochastic Gradient Descent (SGD) algorithm u
 sing non-blocking all-reduce. We demonstrate up to a 2.6x overall speedup 
 (or a 11.2x theoretical speedup with a Nvidia K80 graphics card), when tra
 ining the GoogLeNet model to classify 10% of the ImageNet dataset on a 32-
 node cluster. We also demonstrate a comparable convergence rate using the 
 new asynchronous SGD with respect to the synchronous method.\n\nTag: Deep 
 Learning, Machine Learning\n\nRegistration Category: Workshop Reg Pass\n\n
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