Probabilistic inference schemes for sparsity structures of Fortran 90 array intrinsics
In this paper, we address the issues of partitioning sparse arrays whose non-zero elements are distributed non-uniformly. We consider inference schemes for Fortran 90 array intrinsics so that the non-zero structure of the output array can be deduced from the non-zero structures of the input arrays. Experiments are conducted to measure the effectiveness of our method with the Harwell-Boeing sparse matrix collection. We also demonstrate that, given the sparsity structures of the source arrays and with the help of our inference schemes, one can predict the performance differences among a collection of equivalent Fortran 90 code for sample on-line analytical processing (OLAP). The experiments are performed on an IBM SP2 cluster with the library support of our sparse array intrinsics.