Wavesched: a novel scheduling technique for control-flow intensive designs

摘要

In this paper, we present a novel scheduling algorithm targeted toward minimizing the average execution time of control-flow intensive behavioral descriptions. Our algorithm uses a control/data flow graph model, which preserves the parallelism inherent in the application. It explores previously unexplored regions of the solution space by its ability to overlap the schedules of independent iterative constructs, whose bodies share resources. It also incorporates well known optimization techniques like loop unrolling in a natural fashion. This is made possible by a general loop-handling technique, which we have devised. Application of the algorithm to several common benchmarks demonstrates up to 4.8-fold improvement in expected schedule length over existing scheduling algorithms, without paying a price in terms of the best and worst case schedule lengths required to execute the behavioral description (in fact, frequently, the best/worst case schedule lengths are also better for our algorithm).