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Algorithms and Software Architectures
for Distributed and HPC Platforms

Team leader: Christian Perez

Keywords: Energy Efficiency, Programing Models & Runtimes, Distributed Resource Management


  • Energy Efficiency for Ultrascale Systems
    Despite recent improvements, there is still a long road to follow in order to obtain energy efficient, energy proportional and eco-responsible exascale systems by 2022. Energy efficiency is therefore a major challenge for building next generation large-scale platforms. We aim at investigating two research directions. First, we need to improve measurement, understanding, and analysis on how large-scale platforms consume energy. Second, we need to find new mechanisms that consume less and better on such platforms.

  • HPC Programming Models and Runtimes

    Machines and applications are getting more and more complex so that the cost of manually handling an application is becoming very high. Hardware complexity also stems from the unclear path towards next generations of hardware coming from the frequency wall. Therefore, the challenge we aim to address is to define a model, for describing the dynamic structure of parallel and distributed applications that enables code variations but also efficient executions on parallel and distributed infrastructures. In particular, we focus on component based models as well as OpenMP runtimes.

  • Distributed Resource Management
    This research axis is at the crossroad of the Avalon team. In particular, we plan to consider application mapping and scheduling addressing the following issues: Large Scale Cloud and Fog Workflow and Application Scheduling, Data Stream Processing and Edge Computing, Autonomic Multiple Clouds Resource Management, Security for Virtualization and Clouds, and Performance Prediction of Parallel Regular Applications.


Main collaborations: Rutgers Univ. (USA); Joint Laboratory on Extreme Scale Computing; Univ. Melbourne (Australia); Irisa; LS2N.