Section: New Results

Modeling and Simulation of Parallel Applications and Distributed Infrastructures

Participant : Frédéric Suter.

Simulating MPI Applications: the SMPI Approach

Predicting the behavior of distributed algorithms has always been a challenge, and the scale of next-generation High Performance Computing (HPC) systems will only make the situation more difficult. Performance modeling and software engineering for these systems increasingly require a simulation-based approach, and this need will only become more apparent with the arrival of Exascale computing by the end of the decade. In [6] we summarized our recent work and developments on SMPI, a flexible simulator of MPI applications. In this tool, we took a particular care to ensure our simulator could be used to produce fast and accurate predictions in a wide variety of situations. Although we did build SMPI on SimGrid whose speed and accuracy had already been assessed in other contexts, moving such techniques to a HPC workload required significant additional effort. Obviously, an accurate modeling of communications and network topology was one of the key to such achievements. Another less obvious key was the choice to combine in a single tool the possibility to do both offline and online simulation.

Modeling Distributed Platforms from Application Traces

Simulation is a fast, controlled, and reproducible way to evaluate new algorithms for distributed computing platforms in a variety of conditions. However, the realism of simulations is rarely assessed, which critically questions the applicability of a whole range of findings.

In [15], we present our efforts to build platform models from application traces, to allow for the accurate simulation of file transfers across a distributed infrastructure. File transfers are key to performance, as the variability of file transfer times has important consequences on the dataflow of the application. We present a methodology to build realistic platform models from application traces and provide a quantitative evaluation of the accuracy of the derived simulations. Results show that the proposed models are able to correctly capture real-life variability and significantly outperform the state-of-the-art model.