Stable Adaptive Work-Stealing for Concurrent Many-Core Runtime Systems

Abstract

SUMMARYThe proliferation of many-core architectures has led to the explosive development of parallel applications using programming models, such as OpenMP, TBB, and Cilk/Cilk++. With increasing number of cores, however, it becomes even harder to efficiently schedule parallel applications on these resources since current many-core runtime systems still lack effective mechanisms to support collaborative scheduling of these applications. In this paper, we study feedback-driven adaptive scheduling based on work stealing, which provides an efficient solution for concurrently executing a set of applications on many-core systems. To dynamically estimate the number of cores desired by each application, a stable feedback-driven adaptive algorithm, called SAWS, is proposed using active workers and the length of active deques, which well captures the runtime characteristics of the applications. Furthermore, a prototype system is built by extending the Cilk runtime system, and the experimental results, which are obtained on a Sun Fire server, show that SAWS has more advantages for scheduling concurrent parallel applications. Specifically, compared with existing algorithms A-Steal and WS-EQUI, SAWS improves the performances by up to 12.43% and 21.32% with respect to mean response time respectively, and 25.78% and 46.98% with respect to processor utilization, respectively.

Scalable Hierarchical Scheduling for Malleable Parallel Jobs on Multiprocessor-based Systems

“…In these environments, the productivity and performance gains largely depend on the effective exploitation of application parallelism across the available computing resources. Due to the increasing scale and the dynamic nature of these modern computing platforms, there is a need for more efficient scheduling strategies in order to effectively allocate the available computing resources to the parallel applications [1,2].…”