Behavioral Simulations in MapReduce

TL;DR

This paper introduces BRACE, a framework extending MapReduce for scalable behavioral simulations, featuring a high-level language BRASIL for easier programming and optimization, achieving near-linear scale-up and high performance.

Contribution

The paper presents BRACE, a novel extension of MapReduce for behavioral simulations, including a new high-level language BRASIL for simplified programming and optimization.

Findings

Achieves nearly linear scale-up on realistic simulations

Provides a high-level language that simplifies agent behavior programming

Optimizations enable performance comparable to hand-coded simulations

Abstract

In many scientific domains, researchers are turning to large-scale behavioral simulations to better understand important real-world phenomena. While there has been a great deal of work on simulation tools from the high-performance computing community, behavioral simulations remain challenging to program and automatically scale in parallel environments. In this paper we present BRACE (Big Red Agent-based Computation Engine), which extends the MapReduce framework to process these simulations efficiently across a cluster. We can leverage spatial locality to treat behavioral simulations as iterated spatial joins and greatly reduce the communication between nodes. In our experiments we achieve nearly linear scale-up on several realistic simulations. Though processing behavioral simulations in parallel as iterated spatial joins can be very efficient, it can be much simpler for the domain scientists to program the behavior of a single agent. Furthermore, many simulations include a considerable amount of complex computation and message passing between agents, which makes it important to optimize the performance of a single node and the communication across nodes. To address both of these challenges, BRACE includes a high-level language called BRASIL (the Big Red Agent SImulation Language). BRASIL has object oriented features for programming simulations, but can be compiled to a data-flow representation for automatic parallelization and optimization. We show that by using various optimization techniques, we can achieve both scalability and single-node performance similar to that of a hand-coded simulation.