Parallel input/output impact on sparse matrix compression

Sparse matrices efficiently store structured information, particularly when represented in compressed formats. The advantages of using compressed formats rather than expanded representations are reduced storage space and faster computation achieved by avoiding processing the zero elements. We address the I/O bottleneck associated with the compression operation. We show that such a bottleneck can be reduced if parallel I/O techniques are used. We study several available parallel file system (PFS) access modes available on an Intel Paragon with 64 processing nodes (among whom 56 are compute nodes and 3 are I/O nodes)