Loring, BurlenWolf, MathewKress, JamesShudler, SergeiGu, JunminRizzi, SilvioLogan, JeremyFerrier, NicolaBethel, E. WesFrey, Steffen and Huang, Jian and Sadlo, Filip2020-05-242020-05-242020978-3-03868-107-61727-348Xhttps://doi.org/10.2312/pgv.20201073https://diglib.eg.org:443/handle/10.2312/pgv20201073In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N: One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an ''intelligent'' mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved.Attribution 4.0 International LicenseSoftware and its engineeringSoftware performanceHuman centered computingVisualization systems and toolsComputing methodologiesParallel algorithms10.2312/pgv.2020107335-45