Experiences from Integrating Algorithmic and Systemic Load Balancing Strategies

Load balancing increases the efficient usage of existing resources for parallel and distributed applications. At a coarse level of granularity, advances in runtime systems for parallel programs have been proposed in order to control available resources as efficiently as possible by utilizing idle resources and using task migration. Simultaneous- ly, at a finer granularity level, advances in algorithmic strategies for dynamically balancing computational loads by data redistribution have been proposed in order to respond to variations in processor performance during the execution of a given parallel application. Combining strategies from each level of granularity can result in a system which delivers ad- vantages of both. The resulting integration is systemic in nature and transfers the responsibility of efficient resource uti- lization from the application programmer to the runtime system. This paper reports on recent experimental results of running a computationally intensive sc..