Parallel Discrete Event Simulation Techniques for Scientific Simulations

• Main Author: Dave, Jagrut Durdant
• Format: Others
• Language: en_US
• Published: Georgia Institute of Technology 2005
• Subjects: Parallel; Computing; Discrete events; Simulation
• Online Access: http://hdl.handle.net/1853/6942

Exponential growth in computer technology, both in terms of individual CPUs and parallel technologies over the past decades has triggered rapid progress in large scale simulations. However, despite these achievements it has become clear that many conventional state-of-the-art techniques are ill-equipped to tackle problems that inherently involve multiple scales in configuration space. Our difficulty is that conventional ("time driven" or "time stepped") techniques update all parts of simulation space (fields, particles) synchronously, i.e. at time intervals assumed to be the same throughout the global computation domain or at best varying on a sub-domain basis (in adaptive mesh refinement algorithms). Using a serial electrostatic model, it was recently shown that discrete event techniques can lead to more than two orders of magnitude speedup compared to the time-stepped approach. In this research, the focus is on the extension of this technique to parallel architectures, using parallel discrete event simulation. Previous research in parallel discrete event simulations of scientific phenomena has been limited This thesis outlines a technique for converting a time-stepped simulation in the scientific domain into an equivalent parallel discrete event model. As a candidate simulation, an electromagnetic hybrid plasma simulation is considered. The experiments and analysis show the trade-offs on performance by varying the following factors: the simulations model characteristics (e.g. lookahead), applications load balancing, and accuracy of simulation results. The experiments are performed on a high performance cluster, using a conservative synchronization mechanism. Initial performance results are encouraging, demonstrating very good parallel speedup for large-scale model configurations containing tens of thousands of cells. Overheads for inter-processor communication remain a challenge for smaller computations.