Adaptive approaches for efficient parallel algorithms on cluster-based systems

A few years ago, there was a huge development of new parallel and distributed systems. For many reasons, such as the inherent heterogeneity, the diversity and the continuous evolution of such computational 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 architecture. Towards this goal, we propose a generic framework combining communication models and adaptive approaches to deal with the performance modelling problem associated with the design of efficient parallel algorithms in grid computing environments, and we apply this methodology to collective communication operations. Experiments performed on a grid platform prove that the framework provides significant performances while determining the best combination of model and algorithm depending on the problem and architecture parameters.