sponsored byACMIEEE The International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 15-20, 2015

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Automating Sparse Linear Solver Selection with Lighthouse

SESSION: Regular & ACM Student Research Competition Poster Reception

EVENT TYPE: Posters, Receptions, ACM Student Research Competition

EVENT TAG(S): HPC Beginner Friendly, Regular Poster

TIME: 5:15PM - 7:00PM

SESSION CHAIR(S): Michela Becchi, Manish Parashar, Dorian C. Arnold

AUTHOR(S):Kanika Sood, Pate Motter, Elizabeth Jessup, Boyana Norris

ROOM:Level 4 - Lobby


Solving large, sparse linear systems efficiently is a
challenging problem in scientific computing. Taxonomies
and high-performance numerical linear algebra solutions
help to translate algorithms to
software. However, accessible, comprehensive, and usable tools
for high quality code production are not available. To address this challenge,
we present an extensible methodology for classifying iterative
algorithms for solving sparse linear systems.
Lighthouse is the first framework that offers an organized
taxonomy of software components for linear algebra that enables
functionality and performance-based search and generates code templates
and optimized low-level kernels. It enables the
selection of a solution method that is likely to converge and perform
well. We describe the integration of PETSc and
Trilinos iterative solvers into Lighthouse.
We present a comparative analysis of solver classification
results for a varied set of input problems and machine learning methods
achieving up to 93% accuracy in identifying the best-performing linear
solution methods.

Chair/Author Details:

Michela Becchi, Manish Parashar, Dorian C. Arnold (Chair) - University of Missouri|Rutgers University|University of New Mexico|

Kanika Sood - University of Oregon

Pate Motter - University of Colorado Boulder

Elizabeth Jessup - University of Colorado Boulder

Boyana Norris - University of Oregon

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