A work-stealing scheduling framework supporting fault tolerance

Fault tolerance and load balancing are critical points for executing long-running parallel applications on multicore clusters. This paper addresses both fault tolerance and load balancing on multicore clusters by presenting a novel work-stealing task scheduling framework which supports hardware fault tolerance. In this framework, both transient and permanent faults are detected and recovered at task granularity. We incorporate task-based fault detection and recovery mechanisms into a hierarchical work-stealing scheme to establish the framework. This framework provides low-overhead fault-tolerance and optimal load balancing by fully exploiting task parallelism.