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DTSTART:19700308T020000
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DTSTAMP:20260522T150115Z
LOCATION:D161
DTSTART;TZID=America/Chicago:20181112T171000
DTEND;TZID=America/Chicago:20181112T173000
UID:submissions.supercomputing.org_SC18_sess158_ws_lasalss117@linklings.co
 m
SUMMARY:On Advanced Monte Carlo Methods for Linear Algebra on Advanced Acc
 elerator Architectures
DESCRIPTION:Anton Lebedev (University of Tübingen) and Vassil Alexandrov (
 ICREA, Barcelona Supercomputing Center)\n\nIn this paper we present comput
 ational experiments performed using the Markov Chain Monte Carlo Matrix In
 version (MCMCMI) on several architectures of NVIDIA accelerators and two i
 terations of the Intel x86 architecture and investigate their impact on pe
 rformance and scalability of the method.\nThe method is used as a precondi
 tioner and iterative methods, such as generalized minimal residuals (GMRES
 ) or bi-conjugate gradient stabilized (BICGstab), are used for solving the
  corresponding system of linear equations.\nNumerical experiments are carr
 ied out to highlight the benefits and deficiencies of both architecture ty
 pes and to assess their overall usefulness in light of the scalability of 
 the method.\n\nTag: Algorithms, Heterogeneous Systems, Resiliency\n\nRegis
 tration Category: Workshop Reg Pass\n\nSession Chairs: Vassil Alexandrov (
 Hartree Centre, STFC); Jack Dongarra (University of Tennessee, Knoxville; 
 Oak Ridge National Laboratory (ORNL)); Christian Engelmann (Oak Ridge Nati
 onal Laboratory (ORNL)); and Al Geist (Oak Ridge National Laboratory (ORNL
 ))\n\n
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