Leveraging the Power of Big Data Tools for Large Scale Molecular Dynamics Analysis

International audienceParallel Molecular Dynamics simulations are generating atom trajectories of growing sizes and complexity. Analyzing these trajectories is expensive computationally and time consuming. One reason is the lack of tools that enable the computational biologist to easily implement the analysis while ensuring reduced processing times exploiting the benefits of parallel architectures. In this paper, we present a comparison between two parallel analytics frameworks based on the Map/Reduce paradigm: HiMach, a dedicated framework for trajectory analysis based on MPI, and Flink, a Big Data analytics framework. Both frameworks enable to hide the complexity of parallel code creation to the programmer, providing significant performance gains compared to a sequential execution