Adaptive Performance Modeling on Hierarchical Grid Computing Environments

8 pagesInternational audienceIn the past, efficient parallel algorithms have always been developed specifically for the successive generations of parallel systems (vector machines, shared-memory machines, distributed-memory machines, etc.). Today, due to many reasons, such as the inherent heterogeneity, the diversity, and the continuous evolution of the existing parallel execution supports, it is very hard to solve efficiently a target problem by using a single algorithm or to write portable programs that perform well on any computational supports. Toward this goal, we propose a generic framework based on communication models and adaptive approaches in order to adaptively model performances on grid computing environments. We apply this methodology on collective communication operations and show, by achieving experiments on a real platform, that the framework provides significant performances while determining the best combination model-algorithm depending on the problem and architecture parameters

A Framework for Adaptive Collective Communications on Heterogeneous Hierarchical Networks

Extended version of the IPDPS 2006 paperToday, due to the wide variety of existing parallel systems consisting on collections of heterogeneous machines, it is very difficult for a user to solve a target problem by using a single algorithm or to write portable programs that perform well on multiple computational supports. The inherent heterogeneity and the diversity of networks of such environments represent a great challenge to model the communications for high performance computing applications. Our objective within this work is to propose a generic framework based on communication models and adaptive techniques for dealing with prediction of communication performances on cluster-based hierarchical platforms. Toward this goal, we introduce the concept of polyalgorithmic model of communications, which correspond to selection of the most adapted communication algorithms and scheduling strategies, giving the characteristics of the hardware resources of the target parallel system. We apply this methodology on collective communication operations and show that the framework provides significant performances while determining the best algorithm depending on the problem and architecture parameters