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. >
['Manuel Ujaldon', 'Emilio L. Zapata', 'Barbara M. Chapman', 'Hans P. Zima']
New data-parallel language features for sparse matrix computations