Practical task-oriented parallelism for Gaussian elimination in distributed memory

AbstractThis paper discusses a methodology for easily and efficiently parallelizing sequential algorithms in linear algebra using cost-effective networks of workstations (NOW), where the algorithm lends itself to parallelism. A particular target architecture of interest is the academic student laboratory, which typically contains many networked computers that lay idle at night. A case is made for why a task-oriented approach lends itself to the twin goals of programming ease and run-time efficiency. The approach is then described in the context of Task-Oriented Parallel C (TOP-C), an example of a system to support task-oriented parallelism. In this system, the programmer is relieved of lower level concerns such as latency, bandwidth, and message passing protocols, so as to better concentrate on higher level issues of task granularity and reduction of communication traffic. Gaussian elimination is chosen as the main example, since this algorithm is both widely used and sufficiently interesting to require non-trivial forms of parallelization for the sake of efficiency