A Study of Speculative Distributed Scheduling on the Cell/B.E.

Star Superscalar's (StarSs) programming model converts a sequential application in C or Fortran into an efficient parallel program. The resulting parallel code is highly dynamic in the sense that data analysis and task scheduling occur at run-time, while the application executes. In this paper we compare this approach to the strategy adopted by other multi-core programming environments. The prize to pay for dynamic scheduling and dependence tracking is higher runtime overhead. We propose a distributed scheduler for Task Dependence Graphs (TDGs) to attenuate the scheduling cost in heterogeneous multi-core architectures. This scheduler allows the cores to speculatively select tasks from a conservative estimate of the TDG. In case of conflicts or lack of tasks a lightweight centralized scheduler services the faulting core after which the latter resumes its participation in the distributed scheme. Experiments with Cell Super scalar (CellSs) on a representative set of benchmarks demonstrate the reduction in runtime overhead achieved by the distributed scheduler. This reduction in runtime overhead carries over directly to a performance improvement for a large fraction of the benchmarks.