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. The paper introduces additional language functionality to allow the efficient processing of sparse matrix codes. It introduces 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