SC15 Austin, TX

Automating Sparse Linear Solver Selection with Lighthouse


Authors: Kanika Sood (University of Oregon), Pate Motter (University of Colorado Boulder), Elizabeth Jessup (University of Colorado Boulder), Boyana Norris (University of Oregon)

Abstract: 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.

Poster: pdf
Two-page extended abstract: pdf


Poster Index