Improving the scalability of parallel N-body applications with an event driven constraint based execution model

TL;DR

This paper presents an event-driven, constraint-based execution model that enhances the scalability and efficiency of parallel N-body applications, especially for dynamic workloads like those generated by the Barnes-Hut algorithm.

Contribution

It introduces an Exascale computing execution model, ParalleX, demonstrating improved load balancing and automatic parallelism discovery over conventional models.

Findings

Enhanced load balancing during runtime

Automatic parallelism discovery improves efficiency

Better scalability for dynamic graph workloads

Abstract

The scalability and efficiency of graph applications are significantly constrained by conventional systems and their supporting programming models. Technology trends like multicore, manycore, and heterogeneous system architectures are introducing further challenges and possibilities for emerging application domains such as graph applications. This paper explores the space of effective parallel execution of ephemeral graphs that are dynamically generated using the Barnes-Hut algorithm to exemplify dynamic workloads. The workloads are expressed using the semantics of an Exascale computing execution model called ParalleX. For comparison, results using conventional execution model semantics are also presented. We find improved load balancing during runtime and automatic parallelism discovery improving efficiency using the advanced semantics for Exascale computing.