BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Denver
X-LIC-LOCATION:America/Denver
BEGIN:DAYLIGHT
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
TZNAME:MDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
TZNAME:MST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260422T000712Z
LOCATION:503-504
DTSTART;TZID=America/Denver:20231116T143000
DTEND;TZID=America/Denver:20231116T150000
UID:submissions.supercomputing.org_SC23_sess255_exforum116@linklings.com
SUMMARY:Accelerating Data Analytics Using Object Based Computational Stora
 ge in a HPC
DESCRIPTION:Jongryool Kim (SK hynix)\n\nHPC not only performs complex calc
 ulations at high speed but also processes large amount of data. HPC System
 s separates compute node and storage node to effectively process them. All
  computation is performed on compute node, and all data is stored in stora
 ge node. In order to perform data analytics, compute node has to read larg
 e amount of data from storage node because simulation output data is large
 . Compute nodes must have enough memory to hold extremely large data sets 
 but also bandwidth from storage can become a bottleneck as well. However, 
 the actual data required for analytics is only a small part of the total d
 ata. One solution to solve this problem is computational storage. Since co
 mputational storage transfer only results to compute node by processing wh
 ere data resides, it can reduce data movement and increase performance. SK
  hynix is researching computational storage technologies with Los Alamos N
 ational Laboratory. We propose Object based Computational Storage (OCS) as
  a new computational storage platform for data analytics in HPC. OCS has n
 ot only high scalability but also data-aware characteristics. Data-aware c
 haracteristics enable OCS to perform analytics independently without help 
 from compute nodes. We intend to leverage the Apache analytics ecosystem, 
 including Arrow and Substrait to enhance that ecosystem with the advantage
 s computing near storage enables. Systems that use Arrow can transfer quer
 y results using a common transfer format, and Substrait provides a standar
 d and open representation of query plans enabling pushdown of query portio
 ns to computational storage. SK hynix’s key technology for OCS is Object b
 ased Computational Storage Array(OCSA) used as a backend storage. With OCS
 A, OCS will provide flexible query pushdown and analytics acceleration as 
 well as less software overhead. This talk will introduce the OCS architect
 ure and discuss why we propose OCS as future direction for computational s
 torage in HPC.\n\nTag: Artificial Intelligence/Machine Learning, Architect
 ure and Networks, Hardware Technologies\n\nRegistration Category: Tech Pro
 gram Reg Pass, Exhibits Reg Pass\n\nSession Chair: Eishi Arima (Technical 
 University of Munich)\n\n
END:VEVENT
END:VCALENDAR