Adaptive Task Pools: Efficiently Balancing Large Number of Tasks on Shared-address Spaces

Abstract

Task based approaches with dynamic load balancing are well suited to exploit parallelism in irregular applications. For such applications, the execution time of tasks can often not be predicted due to input dependencies. Therefore, a static task assignment to execution resources usually does not lead to the best performance. Moreover, a dynamic load balancing is also beneficial for heterogeneous execution environments. In this article a new adaptive data structure is proposed for storing and balancing a large number of tasks, allowing an efficient and flexible task management. Dynamically adjusted blocks of tasks can be moved between execution resources, enabling an efficient load balancing with low overhead, which is independent of the actual number of tasks stored. We have integrated the new approach into a runtime system for the execution of task-based applications for shared address spaces. Runtime experiments with several irregular applications with different execution schemes show that the new adaptive runtime system leads to good performance also in such situations where other approaches fail to achieve comparable results.