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DTSTAMP:20181221T160903Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181115T083000
DTEND;TZID=America/Chicago:20181115T170000
UID:submissions.supercomputing.org_SC18_sess324_post107@linklings.com
SUMMARY:Processing-in-Storage Architecture for Machine Learning and Bioinf
 ormatics
DESCRIPTION:Poster\nTech Program Reg Pass, Exhibits Reg Pass\n\nProcessing
 -in-Storage Architecture for Machine Learning and Bioinformatics\n\nKaplan
 , Yavits, Ginosar\n\nUser-generated and bioinformatics database volumes ha
 s been increasing exponentially for more than a decade. With the slowdown 
 and approaching end of Moore's law, traditional technologies cannot satisf
 y the increasing demands for processing power.   This work presents PRINS,
  a highly-parallel in-storage processing architecture. PRINS combines non-
 volatile memory with processing capabilities on every bitcell. An emerging
  technology, memristors, form the basis for the design. \n\nImplementation
 s of three data-intensive and massively parallel algorithms are demonstrat
 ed: (1) Smith-Waterman DNA local sequence alignment (bioinformatics), (3) 
 K-means clustering (machine learning) and (3) data deduplication. Performa
 nce and energy efficiency of PRINS compared to other published solutions i
 s presented for each algorithm. PRINS is shown to achieve orders-of-magnit
 ude improvement in performance and power efficiency over existing solution
 s, from large-scale bioinformatics and machine-learning to single-GPU or F
 PGA implementations.
URL:https://sc18.supercomputing.org/presentation/?id=post107&sess=sess324
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