Performance Evaluation of Task Pools Based on Hardware Synchronization

A task-based execution provides a universal approach to dynamic load balancing for irregular applications. Tasks are arbitrary units of work that are created dynamically at run-time and that are stored in a parallel data structure, the task pool, until they are scheduled onto a processor for execution. In this paper, we evaluate the performance of different task pool implementations for shared-memory computer systems using several realistic applications. We consider task pools with different data structures, different load balancing strategies and a specialized memory management. In particular, we use synchronization operations based on hardware support that is available on many modern micro-processors. We show that the resulting task pool implementations lead to a much better performance than implementations using Pthreads library calls for synchronization. The applications considered are parallel quicksort, volume rendering, ray tracing, and hierarchical radiosity. The target machines are an IBM p690 server and a SunFire 6800.