KLU is a software package for solving sparse unsymmetric linear systems of equations that arise in circuit simulation applications. It relies on a permutation to Block Triangular Form (BTF), several methods for finding a fill-reducing ordering (variants of approximate minimum degree and nested dissection), and Gilbert/Peierls’ sparse left-looking LU factorization algorithm to factorize each block. The package is written in C and includes a MATLAB interface. Performance results comparing KLU with SuperLU, Sparse 1.3, and UMFPACK on circuit simulation matrices are presented. KLU is the default sparse direct solver in the Xyce TM circuit simulation package developed by Sandia National Laboratories.
Summary This paper explores autotuning strategies for serial divide‐and‐conquer stencil computations, comparing the efficacy of traditional “heuristic” autotuning with that of “pruned‐exhaustive” autotuning. We present a pruned‐exhaustive autotuner called Ztune that searches for optimal divide‐and‐conquer trees for stencil computations. Ztune uses three pruning properties—space‐time equivalence, divide subsumption, and favored dimension—that greatly reduce the size of the search domain without significantly sacrificing the quality of the autotuned code. We compared the performance of Ztune with that of a state‐of‐the‐art heuristic autotuner called OpenTuner in tuning the divide‐and‐conquer algorithm used in Pochoir stencil compiler. Over a nightly run on ten application benchmarks across two machines with different hardware configurations, the Ztuned code ran 5% –12% faster on average, and the OpenTuner tuned code ran from 9% slower to 2% faster on average, than Pochoir's default code. In the best case, the Ztuned code ran 40% faster, and the OpenTuner tuned code ran 33% faster than Pochoir's code. Whereas the autotuning time of Ztune for each benchmark could be measured in minutes, to achieve comparable results, the autotuning time of OpenTuner was typically measured in hours or days. Surprisingly, for some benchmarks, Ztune actually autotuned faster than the time it takes to perform the stencil computation once.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.