Application of Alternating Decision Trees in Selecting Sparse Linear Solvers

Bhowmick, Sanjukta; Eijkhout, Victor; Freund, Yoav; Fuentes, Erika; Keyes, David E.

doi:10.1007/978-1-4419-6935-4_10

Cited by 21 publications

(25 citation statements)

References 19 publications

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“…Our formalization of the metadata and access to it will follow in the next section. While in the subsections to follow we only sketch possible scenarios, we would like to point out that research exists that uses such an approach with considerable success [Bhowmick et al 2006;Demmel et al 2005;Dongarra et al 2006;Ramakrishnan and Ribbens 2000;Xu et al 2003; Laboratory for High Performance Scientific Computing and Computer Simulation 2009].…”

Section: Use Of Metadata In Practicementioning

confidence: 99%

A Standard and Software for Numerical Metadata

Eijkhout

Fuentes²

2009

ACM Trans. Math. Softw.

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We propose a standard for generating, manipulating, and storing metadata describing numerical problems, in particular properties of matrices and linear systems. The standard comprises:-an API for metadata generating and querying software, and -an XML format for permanent storage of metadata.The API is open-ended, allowing for other parties to define additional metadata categories to be generated and stored within this framework. Furthermore, we present two software libraries, NMD and AnaMod, that implement this standard, and that contain a number of computational modules for numerical metadata. The libraries, more than simply illustrating the use of the standard, provide considerable utility to numerical researchers. ACM Reference Format:Eijkhout, V. and Fuentes, E. 2009. A standard and software for numerical metadata. ACM

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Section: Use Of Metadata In Practicementioning

confidence: 99%

A Standard and Software for Numerical Metadata

Eijkhout

Fuentes²

2009

ACM Trans. Math. Softw.

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“…The Self Adapting Largescale Solver Architecture (SALSA) project uses statistical techniques for solver selection [8]. In [6] the authors study the construction of a selection process for linear system resolution. The construction of automated selection process requires the identification of a representative set of features.…”

Section: B Related Workmentioning

confidence: 99%

Combining multiple heuristics on discrete resources

Bougeret

Dutot

Goldman

et al. 2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

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Abstract-In this work we study the portfolio problem which is to find a good combination of multiple heuristics to solve given instances on parallel resources in minimum time. The resources are assumed to be discrete, it is not possible to allocate a resource to more than one heuristic. Our goal is to minimize the average completion time of the set of instances, given a set of heuristics on homogeneous discrete resources. This problem has been studied in the continuous case in [18]. We first show that the problem is hard and that there is no constant ratio polynomial approximation in the general case. Then, we design several approximation schemes for a restricted version of the problem where each heuristic must be used at least once. These results are obtained by using oracle with several guesses, leading to various tradeoff between the size of required information and the approximation ratio. Some additional results based on experiments are finally reported using a benchmark of instances on SAT solvers.

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“…However, a more systematic approach is needed, for instance since such choices may need to be made in a dynamic context such as a time-evolving system. Certain recent efforts have tried to tackle this situation by the application of various automatic learning techniques [3,6,14,11,21]. Such methods perform mining of the problem features, typically relying on a database of prior knowledge, in order to recommend a suitable method.…”

Section: Introductionmentioning

confidence: 99%

Multi-stage Learning of Linear Algebra Algorithms

Eijkhout¹,

Fuentes

2008

2008 Seventh International Conference on Machine Learning and Applications

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In evolving applications, there is a need for the dynamic selection of algorithms or algorithm parameters. Such selection is hardly ever governed by exact theory, so intelligent recommender systems have been proposed. In our application area, the iterative solution of linear systems of equations, the recommendation process is especially complicated, since the classes have a multi-dimensional structure. We discuss different strategies of recommending the different components of the algorithms.

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Application of Alternating Decision Trees in Selecting Sparse Linear Solvers

Cited by 21 publications

References 19 publications

A Standard and Software for Numerical Metadata

A Standard and Software for Numerical Metadata

Combining multiple heuristics on discrete resources

Multi-stage Learning of Linear Algebra Algorithms

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