Block scheduling of iterative algorithms and graph-level priority scheduling in a simulated data-flow multiprocessor

Iterative methods for solving linear systems are discussed. Although these methods are inherently highly sequential, it is shown that much parallelism could be exploited in a data-flow system by scheduling the iterative part of the algorithms in blocks and by looking ahead across several iterations. This approach is general and will apply to other iterative and loop-based problems. It is also demonstrated by simulation that relying solely on data-driven scheduling of parallel and unrolled loops results in low resource utilization and poor performance. A graph-level priority scheduling mechanism has been developed that greatly improves resource utilization and yields higher performance