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Application of Alternating Decision Trees in Selecting Sparse Linear Solvers

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Software Automatic Tuning

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Abstract

The solution of sparse linear systems, a fundamental and resource-intensive task in scientific computing, can be approached through multiple algorithms. Using an algorithm well adapted to characteristics of the task can significantly enhance the performance, such as reducing the time required for the operation, without compromising the quality of the result. However, the “best” solution method can vary even across linear systems generated in course of the same PDE-based simulation, thereby making solver selection a very challenging problem. In this paper, we use a machine learning technique, Alternating Decision Trees (ADT), to select efficient solvers based on the properties of sparse linear systems and runtime-dependent features, such as the stages of simulation. We demonstrate the effectiveness of this method through empirical results over linear systems drawn from computational fluid dynamics and magnetohydrodynamics applications. The results also demonstrate that using ADT can resolve the problem of “over-fitting”, which occurs when limited amount of data is available.

This work was sponsored in part by the U.S. National Science Foundation under award 04-06403 to the University of Tennessee, with subcontracts to Columbia University, the University of California at San Diego, and the College of Information Science and Technology at the University of Nebraska, Omaha.

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Notes

  1. 1.

    This work was sponsored in part by the U.S. National Science Foundation under award 04-06403 to the University of Tennessee, with subcontracts to Columbia University, the University of California at San Diego, and the College of Information Science and Technology at the University of Nebraska, Omaha.

  2. 2.

    We say that a set of examples is drawn Independently and Identically Distributed (IID) according to \(\mathcal{D}\) if they can be seen as independent draws from the fixed distribution \(\mathcal{D}\). In other words, if they are independent random variables all having distribution \(\mathcal{D}\).

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Acknowledgments

We would like to thank Jin Chen of the Princeton Plasma Physics Lab for providing us with the M3D matrices. We are also grateful to Raphael Pelossof of Columbia University for his package to render ROC curves from the MLJava output files.

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Authors and Affiliations

  1. Department of Computer Science, University of Nebraska, Omaha, NE, USA

    Sanjukta Bhowmick

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  1. Sanjukta Bhowmick
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  2. Victor Eijkhout
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  3. Yoav Freund
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  4. Erika Fuentes
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  5. David Keyes
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Correspondence to Sanjukta Bhowmick .

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Editors and Affiliations

  1. Central Research Laboratory, Hitachi Ltd., Higashi-Koigakubo 1-280, Kokubunji-shi, Tokyo, 185-8601, Japan

    Ken Naono

  2. Cray, Inc., Jackson St. 380, St Paul, 55101, Minnesota, USA

    Keita Teranishi

  3. Dept. Computer & Information Sciences, University of Delaware, Smith Hall 101, Newark, 19716, Delaware, USA

    John Cavazos

  4. Dept. Computer Science, University of Tokyo, Hongo 7-3-1, Tokyo, 113-0033, Japan

    Reiji Suda

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Bhowmick, S., Eijkhout, V., Freund, Y., Fuentes, E., Keyes, D. (2011). Application of Alternating Decision Trees in Selecting Sparse Linear Solvers. In: Naono, K., Teranishi, K., Cavazos, J., Suda, R. (eds) Software Automatic Tuning. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6935-4_10

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  • DOI: https://doi.org/10.1007/978-1-4419-6935-4_10

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