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DTSTAMP:20260522T150116Z
LOCATION:C140/142
DTSTART;TZID=America/Chicago:20181113T140000
DTEND;TZID=America/Chicago:20181113T143000
UID:submissions.supercomputing.org_SC18_sess206_pap551@linklings.com
SUMMARY:Optimizing High Performance Distributed Memory Parallel Hash Table
 s for DNA k-mer Counting
DESCRIPTION:Tony C. Pan (Georgia Institute of Technology, School of Comput
 ational Science and Engineering); Sanchit Misra (Intel Corporation, Parall
 el Computing Lab); and Srinivas Aluru (Georgia Institute of Technology, Sc
 hool of Computational Science and Engineering)\n\nHigh-throughput DNA sequ
 encing is the mainstay of modern genomics research. A common operation use
 d in bioinformatic analysis for many applications of high-throughput seque
 ncing is the counting and indexing of fixed length substrings of DNA seque
 nces called k-mers. Counting k-mers is often accomplished via hashing, and
  distributed memory k-mer counting algorithms for large data sets are memo
 ry access and network communication bound. In this work, we present two op
 timized distributed parallel hash table techniques that utilize cache frie
 ndly algorithms for local hashing, overlapped communication and computatio
 n to hide communication costs, and vectorized hash functions that are spec
 ialized for k-mer and other short key indices. On 4096 cores of the NERSC 
 Cori supercomputer, our implementation completed index construction and qu
 ery on an approximately 1 TB human genome dataset in just 11.8 seconds and
  5.8 seconds, demonstrating speedups of 2.06x and 3.7x, respectively, over
  the previous state-of-the-art distributed memory k-mer counter.\n\nTag: A
 lgorithms, Applications, Computational Biology, Scientific Computing\n\nRe
 gistration Category: Tech Program Reg Pass\n\nFinalist: BP Finalist\n\nSes
 sion Chair: Allison H. Baker (National Center for Atmospheric Research (NC
 AR))\n\n
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