<i>In Situ</i> Workload Estimation for Block Assignment and Duplication in Parallelization‐Over‐Data Particle Advection

Abstract Particle advection is a foundational algorithm for analyzing a flow field. The commonly used Parallelization‐Over‐Data (POD) strategy for particle advection can become slow and inefficient when there are unbalanced workloads, which are particularly prevalent in in situ workflows. In this work, we present an in situ workflow containing workload estimation for block assignment and duplication in a parallelization‐over‐data algorithm. With tightly coupled workload estimation and load‐balanced block assignment strategy, our workflow offers a considerable improvement over the traditional round‐robin block assignment strategy. Our experiments demonstrate that particle advection is up to 3 X faster and associated workflow saves approximately 30% of execution time after adopting strategies presented in this work.