New data-parallel language features for sparse matrix computations

High-level data-parallel languages such as Vienna Fortran and High Performance Fortran (HPF) have been introduced to allow the programming of massively parallel distributed-memory machines at a relatively high level of abstraction, based on the Single-Program-Multiple-Data (SPMD) paradigm. Their main features include mechanisms for expressing the distribution of data across the processors of a machine. This paper introduces additional language functionality to allow the efficient processing of sparse matrix codes. We introduce new methods for the representation and distribution of sparse matrices, which forms a powerful mechanism for storing and manipulating sparse matrices able to be efficiently implemented on massively parallel machines.