Particle-In-Cell Simulation using Asynchronous Tasking

TL;DR

This paper explores asynchronous task-based programming models for particle-in-cell simulations, demonstrating significant performance improvements and near-perfect scaling on multi-core systems.

Contribution

It presents a novel asynchronous task-based parallelization strategy for electromagnetic particle-in-cell simulations, showing substantial performance gains over traditional methods.

Findings

Asynchronous implementation outperforms synchronous approaches.

Near perfect scaling achieved for 48 cores.

Significant performance improvements demonstrated.

Abstract

Recently, task-based programming models have emerged as a prominent alternative among shared-memory parallel programming paradigms. Inherently asynchronous, these models provide native support for dynamic load balancing and incorporate data flow concepts to selectively synchronize the tasks. However, tasking models are yet to be widely adopted by the HPC community and their effective advantages when applied to non-trivial, real-world HPC applications are still not well comprehended. In this paper, we study the parallelization of a production electromagnetic particle-in-cell (EM-PIC) code for kinetic plasma simulations exploring different strategies using asynchronous task-based models. Our fully asynchronous implementation not only significantly outperforms a conventional, synchronous approach but also achieves near perfect scaling for 48 cores.