Abstract:

Cellular-level agent based modelling is reliant on either sequential processing environments or expensive and largely unavailable PC grids. The GPU offers an alternative architecture for such systems, however the steep learning curve associated with the GPU’s data parallel architecture has previously limited the uptake of this emerging technology. In this paper we demonstrate a template driven agent architecture which provides a mapping of XML model specifications and C language scripting to optimised Compute Unified Device Architecture (CUDA) for the GPU. Our work is validated though the implementation of a Keratinocyte model using limited range message communication with non-linear time simulation steps to resolve intercellular forces. The performance gain achieved over existing modelling techniques reduces simulation times from hours to seconds. The improvement of simulation performance allows us to present a real-time visualisation technique which was previously unobtainable.

(Richmond Paul, Coakley Simon, Romano Daniela (2009), Cellular Level Agent Based Modelling on the Graphics Processing Unit, (Best Student Paper) Proc. of HiBi09 – High Performance Computational Systems Biology, 14-16 October 2009, Trento, Italy)

This article by D’Souza et al. explores large scale Agent-Based Model(ABM) simulation on the GPU. Agent-based modeling is a technique which has become increasingly popular for simulating complex natural phenomena such as swarms and biological cell colonies. An ABM describes a dynamic system by representing it as a collection of communicating, concurrent objects. Current ABM simulation toolkits and algorithms use discrete event simulation techniques and are executed serially on a CPU. This limits the size of the models that can be handled efficiently. In this paper we present a series of efficient data-parallel algorithms for simulating ABMs. These include methods for handling environment updates, agent interactions and replication. Important techniques presented in this work include a novel stochastic allocator which enables parallel agent replication in O(1) average time and an iterative method to handle collision among agents in the spatial domain. These techniques have been implemented on a modern GPU (GeForce 8800GTX), resulting in a substantial performance increase. The authors believe that their system is the first completely GPU-based ABM simulation framework. (D’Souza R., Lysenko, M., Rahmani, K., SugarScape on steroids: simulating over a million agents at interactive rates. Proceedings of the Agent2007 conference, Chicago, IL. 2007.)