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:20260422T000605Z
LOCATION:DEF Concourse
DTSTART;TZID=America/Denver:20231116T100000
DTEND;TZID=America/Denver:20231116T170000
UID:submissions.supercomputing.org_SC23_sess300_spostu114@linklings.com
SUMMARY:Simultaneous Evaluation of Mindful Fault Checking across the CPU a
 nd GPU
DESCRIPTION:Hayden Estes (University of Alabama, Huntsville)\n\nThis work 
 comprehensively analyzes the overhead when implementing fault-checking alg
 orithms for sparse preconditioned conjugate gradient (PCG) solvers on many
 -core and GPU-accelerated systems. Our objective is to selectively utilize
  GPUs for duplicate calculations based on the numerical properties of the 
 sparse matrices to enhance the reliability and performance of linear syste
 m solutions. Enabling the ability to rely on the relatively underutilized 
 CPU for fault detection improves scientific applications' ability to effic
 iently manage their resources on large-scale systems. By leveraging existi
 ng fault-checking techniques, we validate calculations and address potenti
 al numerical instabilities or precision-related issues during iterative so
 lving. Through extensive experimentation on real hardware, we demonstrate 
 the effectiveness of the conjugate gradient algorithm in providing accurat
 e and reliable solutions for large linear systems.\n\nRegistration Categor
 y: Tech Program Reg Pass, Exhibits Reg Pass\n\n
END:VEVENT
END:VCALENDAR