high performance computing on graphics processing units: hgpu.org

In spatial agent-based models (SABMs) each entity of the system being modeled is uniquely represented as an independent agent. Large scale emergent behavior in SABMs is population sensitive. Thus, the number of agents should reflect the system being modeled, which can be in the order of billions. Models can be decomposed such that each component can be concurrently processed by a different thread. In this thesis, a conceptual model for investigating parallelization strategies for SABMs is presented. The model, PPHPC, captures important characteristics of SABMs. NetLogo, Java and OpenCL (CPU and GPU) implementations are proposed. To confirm that all implementations yield the same behavior, their outputs are compared using two methodologies. The first is based on common model comparison techniques found in literature. The second is a novel approach which uses principal component analysis to convert simulation output into a set of linearly uncorrelated measures which can be analyzed in a model-independent fashion. In both cases, statistical tests are applied to determine if the implementations are properly aligned. Results show that most implementations are statistically equivalent, with lower-level parallel implementations offering substantial speedups. The PPHPC model was shown to be a valid template model for comparing SABM implementations.