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DTSTAMP:20181221T160729Z
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:Paper\nAlgorithms, Applications, Computational Biology, Scient
 ific Computing, Tech Program Reg Pass\n\nOptimizing High Performance Distr
 ibuted Memory Parallel Hash Tables for DNA k-mer Counting\n\nPan, Misra, A
 luru\n\nHigh-throughput DNA sequencing is the mainstay of modern genomics 
 research. A common operation used in bioinformatic analysis for many appli
 cations of high-throughput sequencing is the counting and indexing of fixe
 d length substrings of DNA sequences called k-mers. Counting k-mers is oft
 en accomplished via hashing, and distributed memory k-mer counting algorit
 hms for large data sets are memory access and network communication bound.
  In this work, we present two optimized distributed parallel hash table te
 chniques that utilize cache friendly algorithms for local hashing, overlap
 ped communication and computation to hide communication costs, and vectori
 zed hash functions that are specialized for k-mer and other short key indi
 ces. On 4096 cores of the NERSC Cori supercomputer, our implementation com
 pleted index construction and query 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.
URL:https://sc18.supercomputing.org/presentation/?id=pap551&sess=sess206
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