Extending a hierarchical tiling arrays library to support sparse data partitioning.

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Bibliographic Details
Title: Extending a hierarchical tiling arrays library to support sparse data partitioning.
Authors: Fresno, Javier1 jfresno@infor.uva.es, Gonzalez-Escribano, Arturo1 arturo@infor.uva.es, Llanos, Diego1 diego@infor.uva.es
Source: Journal of Supercomputing. Apr2013, Vol. 64 Issue 1, p59-68. 10p.
Subjects: Electronic file management, Abstract data types (Computer science), Vertical files (Libraries), Abstract algebra, Sparse matrices
Abstract: Layout methods for dense and sparse data are often seen as two separate problems with their own particular techniques. However, they are based on the same basic concepts. This paper studies how to integrate automatic data-layout and partition techniques for both dense and sparse data structures. In particular, we show how to include support for sparse matrices or graphs in Hitmap, a library for hierarchical tiling and automatic mapping of arrays. The paper shows that it is possible to offer a unique interface to work with both dense and sparse data structures. Thus, the programmer can use a single and homogeneous programming style, reducing the development effort and simplifying the use of sparse data structures in parallel computations. Our experimental evaluation shows that this integration of techniques can be effectively done without compromising performance. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Layout methods for dense and sparse data are often seen as two separate problems with their own particular techniques. However, they are based on the same basic concepts. This paper studies how to integrate automatic data-layout and partition techniques for both dense and sparse data structures. In particular, we show how to include support for sparse matrices or graphs in Hitmap, a library for hierarchical tiling and automatic mapping of arrays. The paper shows that it is possible to offer a unique interface to work with both dense and sparse data structures. Thus, the programmer can use a single and homogeneous programming style, reducing the development effort and simplifying the use of sparse data structures in parallel computations. Our experimental evaluation shows that this integration of techniques can be effectively done without compromising performance. [ABSTRACT FROM AUTHOR]
ISSN:09208542
DOI:10.1007/s11227-012-0757-y