Accelerating an Electromagnetic Simulation Via Memory-Constrained Task-Based Load Balancing.

While load balancing in distributed-memory computing has been well-studied, we present an innovative approach to tackle challenges that an electromagnetic application poses due to irregular workloads and tight memory constraints. To this end, we present a unified model for approximating work in a distributed system that combines three key components: computation, communication, and memory. This enables the exploration of complex trade-offs in task placement, such as increased parallelism at the expense of data replication. We then present our new fully distributed load balancing strategy that incorporates this model. To predict workloads for the matrix assembly of the electromagnetics application, we apply machine learning across an ensemble of executions to train a neural network, which makes online predictions for our task-based decomposition, informing the load balancer of the computational loads. Finally, we demonstrate that our approach, when applied to this application, leads to substantial speedups, up to 2.0x, thereby decreasing time-to-solution for the imbalanced execution.