Characterizing In Situ and In Transit Analytics of Molecular Dynamics Simulations for Next-Generation Supercomputers

Molecular Dynamics (MD) simulations executed on state-of-the-art supercomputers are producing data at rates faster than it can be written out to disk. In situ and in transit analysis of data generated by MD simulations reduce the original volume of information by several orders of magnitude, thereby alleviating the negative impact of I/O bottlenecks. This work focuses on characterizing the impact of in situ and in transit analytics on the overall MD workflow performance, and the capability for capturing rapid, rare events in the simulated molecular system. The MD simulation and analysis processes share data via remote direct memory access (RDMA) using DataSpaces. Our metrics of interest are time spent waiting in I/O by the MD simulation, lost frames of the MD simulation, and idle time of the analysis. We measure these metrics for a diverse set of molecular systems and characterize their trends for in situ and in transit configurations. We then model which frames are dropped and which ones are analyzed for a real use case. The insights gained from this study are generally applicable for in situ and in transit workflows that require optimization of parameters to minimize loss in workflow performance and analytic accuracy.