Towards understanding block partitioning for sparse Cholesky factorization

The authors examine the effect of two partitioning parameters on the performance of block-based distributed sparse Cholesky factorization. They present result to show the trends in the effect of these parameters on the computation speeds, communication costs, extent of processor idling because of load imbalances, and bookkeeping overheads. These results provide a better understanding in selecting the partitioning parameters so as to reduce the computation and communication costs without increasing the overhead costs or the load imbalance among the processors. Experimental results from a 32-processor iPSC/860 are presented.<>