Scheduling Strategies for Sparse Cholesky Factorization on a Shared Virtual Memory Parallel Computer

Abstract

To solve a given problem on a distributed memory parallel computer (DMPC), the message passing programming model involves distributing both the data and the computations among the processors. While this can be easily feasible for well structured problems, it can become fairly hard for unstructured ones, like sparse matrix computations, unless you use some runtime support. In this paper, we consider a relatively new approach to implementing the Cholesky factorization on a DMPC, by using a shared virtual memory (SVM). The abstraction of a shared memory on top of a distributed memory allows us to introduce a large-grain factorization algorithm, synchronized with events. Experiments conducted so far show that some scheduling techniques enhance not only the parallelism but the SVM behavior as well, allowing interesting results.