A Performance Evaluation of Adaptive MPI for a Particle-In-Cell Code

In the quest for extreme-scale supercomputers, the High Performance Computing (HPC) community has developed many resources (programming paradigms, architectures, method-ologies, numerical methods) to face the multiple challenges along the way. One of those resources are task-based parallel program-ming tools. The availability of mature programming models, pro-gramming languages, and runtime systems that use task-based parallelism represent a favorable ecosystem. The fundamental premise of these tools is their ability to naturally cope with dynamically changing execution conditions, i.e. adaptivity. In this paper, we explore Adaptive MPI, a parallel-object framework, as a mechanism to provide, among other features, automatic and dynamic load balancing for a particle-in-cell application. We ported a pre-existing MPI application on the Adaptive MPI infrastructure and highlight the changes required to the code. Our experimental results show Adaptive MPI has a minimum overhead, maintains the scalability of the original code, and it is able to alleviate an artificially-introduced load imbalance.