Tuning stationary iterative solvers for fault resilience
comunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/146069
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http://dx.doi.org/10.1145/2832080.2832081 |
Metadata
Title
Tuning stationary iterative solvers for fault resilienceDate
2015Publisher
ACM. Association for Computing MachineryISBN
978-1-4503-4011-3Bibliographic citation
Anzt, H., Dongarra, J., & Quintana-Ortí, E. S. (2015, November). Tuning stationary iterative solvers for fault resilience. In Proceedings of the 6th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (p. 1). ACM.Type
info:eu-repo/semantics/conferenceObjectPublisher version
http://dl.acm.org/citation.cfm?id=2832081Subject
Abstract
As the transistor’s feature size decreases following Moore’s Law,
hardware will become more prone to permanent, intermittent, and
transient errors, increasing the number of failures experienced by
applications, and ... [+]
As the transistor’s feature size decreases following Moore’s Law,
hardware will become more prone to permanent, intermittent, and
transient errors, increasing the number of failures experienced by
applications, and diminishing the confidence of users. As a result,
resilience is considered the most difficult under addressed issue
faced by the High Performance Computing community.
In this paper, we address the design of error resilient iterative
solvers for sparse linear systems. Contrary to most previous ap-
proaches, based on Krylov subspace methods, for this purpose we
analyze stationary component-wise relaxation. Concretely, starting
from a plain implementation of the Jacobi iteration, we design a
low-cost component-wise technique that elegantly handles bit-flips,
turning the initial synchronized solver into an asynchronous itera-
tion. Our experimental study employs sparse incomplete factoriza-
tions from several practical applications to expose the convergence
delay incurred by the fault-tolerant implementation. [-]
Description
Actes del 6th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA '15)