Parallel Data Distribution Management on Shared-Memory Multiprocessors

TL;DR

This paper introduces two parallel algorithms for Data Distribution Management in agent-based simulations, leveraging shared-memory multiprocessors to efficiently identify intersections among multi-dimensional regions.

Contribution

It presents novel parallel solutions for DDM, including a concurrent interval tree and a parallel extension of the Sort Based Matching algorithm, optimized for shared-memory systems.

Findings

Both algorithms effectively utilize multi-core architectures.

Experimental results show significant performance improvements.

The parallel solutions outperform sequential counterparts.

Abstract

The problem of identifying intersections between two sets of d-dimensional axis-parallel rectangles appears frequently in the context of agent-based simulation studies. For this reason, the High Level Architecture (HLA) specification -- a standard framework for interoperability among simulators -- includes a Data Distribution Management (DDM) service whose responsibility is to report all intersections between a set of subscription and update regions. The algorithms at the core of the DDM service are CPU-intensive, and could greatly benefit from the large computing power of modern multi-core processors. In this paper we propose two parallel solutions to the DDM problem that can operate effectively on shared-memory multiprocessors. The first solution is based on a data structure (the Interval Tree) that allows concurrent computation of intersections between subscription and update regions. The second solution is based on a novel parallel extension of the Sort Based Matching algorithm, whose sequential version is considered among the most efficient solutions to the DDM problem. Extensive experimental evaluation of the proposed algorithms confirm their effectiveness on taking advantage of multiple execution units in a shared-memory architecture.