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DTSTART:19700308T020000
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DTSTAMP:20260522T150118Z
LOCATION:C146
DTSTART;TZID=America/Chicago:20181112T133000
DTEND;TZID=America/Chicago:20181112T170000
UID:submissions.supercomputing.org_SC18_sess243_tut174@linklings.com
SUMMARY:Exploiting HPC Technologies for Accelerating Big Data Processing a
 nd Associated Deep Learning
DESCRIPTION:Dhabaleswar K. Panda, Xiaoyi Lu, and Shashank Gugnani (Ohio St
 ate University)\n\nThe convergence of HPC, Big Data, and Deep Learning is 
 the next game-changing business opportunity. Apache Hadoop, Spark, gRPC/Te
 nsorFlow, and Memcached are becoming standard building blocks for Big Data
  processing.  Recent studies have shown that default designs of these comp
 onents cannot efficiently leverage the features of modern HPC clusters, li
 ke RDMA-enabled high-performance interconnects, high-throughput parallel s
 torage systems (e.g. Lustre), Non-Volatile Memory (NVM), NVMe/NVMe-over-Fa
 bric.  This tutorial will provide an in-depth overview of the architecture
  of Hadoop, Spark, gRPC/TensorFlow, and Memcached. We will examine the cha
 llenges in re-designing networking and I/O components of these middleware 
 with modern interconnects and storage architectures.  Using the publicly a
 vailable software packages in the High-Performance Big Data project (HiBD,
  http://hibd.cse.ohio-state.edu), we will provide case studies of the new 
 designs for several Hadoop/Spark/gRPC/TensorFlow/Memcached components and 
 their associated benefits. Through these, we will also examine the interpl
 ay between high-performance interconnects, storage, and multi-core platfor
 ms to achieve the best solutions for these components and applications on 
 modern HPC clusters. We also present in-depth case-studies with modern Dee
 p Learning tools (e.g., Caffe, TensorFlow, CNTK, BigDL) with RDMA-enabled 
 Hadoop, Spark, and gRPC. Finally, hands-on exercises will be carried out w
 ith RDMA-Hadoop and RDMA-Spark software stacks over a cutting-edge HPC clu
 ster.\n\nTag: Architectures, Data Analytics, Deep Learning, I/O, Machine L
 earning, Networks, Programming Systems, Tools\n\nRegistration Category: Tu
 torial Reg Pass\n\n
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