Integrating dense and sparse data partitioning

Layout methods for dense and sparse data are often seen as two separate prob-lems with its 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 is possible to offer a unique interface to work with both dense and sparse data structures, without losing significant performance. Thus, the programmer can use a single and homogeneous programing style, reducing the development effort and simplifying the use of sparse data structures in parallel computations. Key words: data partition, layouts, distributed computing, sparse.