A large number of scientific applications require computations that involve operations on sparse matrices. Due to irregularities induced by the diverse sparsity patterns, many operations on sparse matrices execute inefficiently on traditional scalar and vector architectures. In order to tackle this issue a scheme has been proposed [1] that alleviates the sparse matrix storage and computation ov...

Automatic program comprehension is particularly useful when applied to sparse matrix codes, since it allows to abstract e.g. from specific sparse matrix storage formats used in the code. In this paper we describe SPARAMAT, a system for speculative automatic program comprehension suitable for sparse matrix codes, and its implementation.

In this paper we address a sparse matrix multiplication problem posed by Schmeck et al 6]. The main contribution is an optimal run-time algorithm for for multiplying a column sparse matrix by a row sparse matrix on the reconngurable mesh architecture.

The sparse data structure represents a matrix in space proportional to the number of non-zero entries. Many storage formats have been proposed to represent sparse matrices. In this paper we evaluate and compare the storage efficiency of various sparse matrix storage formats, and consider the performance results of matrix-vector multiplication using these storage formats.

In this paper, we set up a geometric framework for solving sparse matrix problems. We introduce geometric sparseness, a notion which applies to several well-known families of sparse matrix. Two algorithms are presented for solving geometrically-sparse matrix problems. These algorithms are inspired by techniques in classical algebraic topology, and involve the construction of a simplicial comple...