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DTSTAMP:20260522T150121Z
LOCATION:D174
DTSTART;TZID=America/Chicago:20181116T090000
DTEND;TZID=America/Chicago:20181116T092000
UID:submissions.supercomputing.org_SC18_sess146_ws_ftxs103@linklings.com
SUMMARY:Fault Tolerant Cholesky Factorization on GPUs
DESCRIPTION:Felix Loh, Kewal K. Saluja, and Parameswaran Ramanathan (Unive
 rsity of Wisconsin)\n\nDirect Cholesky-based solvers are typically used to
  solve large linear systems where the coefficient matrix is symmetric posi
 tive definite. These solvers offer faster performance in solving such line
 ar systems, compared to other more general solvers such as LU and QR solve
 rs. In recent days, graphics processing units (GPUs) have become a popular
  platform for scientific computing applications, and are increasingly bein
 g used as major computational units in supercomputers. However, GPUs are s
 usceptible to transient faults caused by events such as alpha particle str
 ikes and power fluctuations. As a result, the possibility of an error incr
 eases as more and more GPU computing nodes are used. In this paper, we int
 roduce two efficient fault tolerance schemes for the Cholesky factorizatio
 n method, and study their performance using a direct Cholesky solver in th
 e presence of faults. We utilize a transient fault injection mechanism for
  NVIDIA GPUs and compare our schemes with a traditional checksum fault tol
 erance technique, and show that our proposed schemes have superior perform
 ance, good error coverage and low overhead.\n\nTag: Resiliency, Scientific
  Computing\n\nRegistration Category: Workshop Reg Pass\n\n
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