# A Timer-Augmented Cost Function for Load Balanced DSMC

## TL;DR

This paper introduces a timer-augmented cost function for load balancing in large-scale DSMC simulations, combining performance models and timers to improve efficiency and scalability across many processors.

## Contribution

It presents a novel load balancing method that integrates timers with performance models, achieving faster convergence and better processor workload distribution in DSMC simulations.

## Key findings

- Achieves 2x speedup at steady-state on 1024 processors
- Effectively balances load in evolving systems with shifting computational demands
- Outperforms particle-based performance models alone

## Abstract

Due to a hard dependency between time steps, large-scale simulations of gas using the Direct Simulation Monte Carlo (DSMC) method proceed at the pace of the slowest processor. Scalability is therefore achievable only by ensuring that the work done each time step is as evenly apportioned among the processors as possible. Furthermore, as the simulated system evolves, the load shifts, and thus this load-balancing typically needs to be performed multiple times over the course of a simulation. Common methods generally use either crude performance models or processor-level timers. We combine both to create a timer-augmented cost function which both converges quickly and yields well-balanced processor decompositions. When compared to a particle-based performance model alone, our method achieves 2x speedup at steady-state on up to 1024 processors for a test case consisting of a Mach 9 argon jet impacting a solid wall.