High-performance computing tools with applications to epidemics and population dynamics.

摘要

Many interesting phenomena are difficult to explore via computer simulation because limited computational resources tend to prevent the simulation's execution within an acceptable time frame. High performance computing is frequently used to alleviate this problem, with parallel computation being a commonly employed high performance computing technique. In this thesis we demonstrate how parallel computation can improve the simulation of population dynamics and epidemiological phenomena.; The study of spatial and temporal aspects of multi-species biological systems is central to ecology. Epidemics are of significance for agriculture, public health and ecology and this importance motivated their selection as application for this work. The focus of this thesis is on development of algorithms and software tools enabling parallel computation and statistical analysis in a spatially explicit simulation of multi-species ecosystems. A review of population dynamics is presented, various models of related phenomena are described, and high performance computing techniques for simulation are analyzed. Research contributions include: development of modeling techniques, design of parallel algorithms, simulation of epidemics, (including the ecologically significant vector-borne case) and performance analysis of parallel execution of the integrated simulation system on high performance computers (including a MasPar MP-1 and an IBM SP2). In addition, a novel model of epidemics, encompassing evolutionary aspects of disease spread has been developed and its preliminary version implemented on parallel computers.