Data parallel execution challenges and runtime performance of agent simulations on GPUs

Programmable graphics processing units (GPUs) have emerged as excellent computational platforms for certain general-purpose applications. The data parallel execution capabilities of GPUs specifically point to the potential for effective use in simulations of agent-based models (ABM). In this paper, the computational efficiency of ABM simulation on GPUs is evaluated on representative ABM benchmarks. The runtime speed of GPUbased models is compared to that of traditional CPU-based implementation, and also to that of equivalent models in traditional ABM toolkits (Repast and NetLogo). As expected, it is observed that, GPU-based ABM execution affords excellent speedup on simple models, with better speedup on models exhibiting good locality and fair amount of computation per memory element. Execution is two to three orders of magnitude faster with a GPU than with leading ABM toolkits, but at the cost of decrease in modularity, ease of programmability and reusability. At a more fundamental l...

• Type: Conference
• Year: 2008
• Where: SPRINGSIM
• Authors: Kalyan S. Perumalla, Brandon G. Aaby
• Keywords: ABM Execution Affords, ABM Toolkits, Data Parallel Execution, Modeling And Simulation, SPRINGSIM 2008