POSE: getting over grainsize in parallel discrete event simulation

Parallel discrete event simulations (PDES) encom-pass a broad range of analytical simulations. Their utility lies in their ability to model a system and pro-vide information about its behavior in a timely manner. Current PDES methods provide limited performance im-provements over sequential simulation. Many logical models for applications have fine granularity making them challenging to parallelize. In POSE, we exam-ine the overhead required for optimistically synchroniz-ing events. We have designed an object model based on the concept of virtualization and new adaptive op-timistic methods to improve the performance of fine-grained PDES applications. These novel approaches exploit the speculative nature of optimistic protocols to improve single-processor parallel over sequential per-formance and achieve scalability for previously hard-to-parallelize fine-grained simulations.1 1

POSE: Scalable General-purpose Parallel Discrete Event Simulation

Parallel discrete event simulation (PDES) applications encompass a broad range of analytical simulations. Their utility lies in their ability to model a system under study and provide information about the behavior of that system in a timely manner. The most comprehensive models for such systems can be vastly complex, have highly irregular structures and fine granularity, making them challenging problems to parallelize. Current PDES methods provide limited performance improvements over sequential simulation and complicate the modelling process, requiring knowledge of specialized parallel computing practices that may be well outside the application developer's field. We propose a novel environment for PDES that facilitates the development of highly parallel models and requires minimal understanding of parallel computing concepts. We propose four primary approaches to improving the performance of PDES. We first examine the overhead required for synchronizing events to obtain correct results in parallel and develop a new approach to the structure of model entities and mechanisms for PDES that help to reduce that overhead. Secondly, we design new adaptive synchronization strategies that exploit this new model structure to obtain better cache performance and reduce context switching overhead. We then develop techniques to optimize communication in concert with these new strategies. Finally, we study load balancing in the context of optimistic synchronization and design new approaches to fit with our other techniques. These four approaches form an integrated system for handling non-ideal simulation models. We demonstrate our techniques via a highly flexible synthetic benchmark capable of mimicking a variety of simulation behaviors, as well as with simulations of network models for very large parallel computers

Pose: Scalable General-Purpose Parallel Discrete Event Simulation

151 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2005.We propose a novel environment for PDES that facilitates the development of highly parallel models and requires minimal understanding of parallel computing concepts. We propose four primary approaches to improving the performance of PDES. We first examine the overhead required for synchronizing events to obtain correct results in parallel and develop a new approach to the structure of model entities and mechanisms for PDES that help to reduce that overhead. Secondly, we design new adaptive synchronization strategies that exploit this new model structure to obtain better cache performance and reduce context switching overhead. We then develop techniques to optimize communication in concert with these new strategies. Finally, we study load balancing in the context of optimistic synchronization and design new approaches to fit with our other techniques. These four approaches form an integrated system for handling non-ideal simulation models. We demonstrate our techniques via a highly flexible synthetic benchmark capable of mimicking a variety of simulation behaviors, as well as with simulations of network models for very large parallel computers.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Acknowledgements

Parallel discrete event simulation (PDES) applications encompass a broad range of analyti-cal simulations. Their utility lies in their ability to model a system under study and provide information about the behavior of that system in a timely manner. The most comprehen-sive models for such systems can be vastly complex, have highly irregular structures and fine granularity, making them challenging problems to parallelize. Current PDES methods provide limited performance improvements over sequential simulation and complicate the modelling process, requiring knowledge of specialized parallel computing practices that may be well outside the application developer’s field. We propose a novel environment for PDES that facilitates the development of highly par-allel models and requires minimal understanding of parallel computing concepts. We propose four primary approaches to improving the performance of PDES. We first examine the over-head required for synchronizing events to obtain correct results in parallel and develop a new approach to the structure of model entities and mechanisms for PDES that help to reduce that overhead. Secondly, we design new adaptive synchronization strategies that exploi

Parallel Mesh Adaptation for Highly Evolving Geometries with Application to Solid Propellant Rockets

Summary. We describe our parallel 3-D surface and volume mesh modification strategy for large-scale simulation of physical systems with dynamically changing domain boundaries. Key components include an accurate, robust, and efficient surface propagation scheme, frequent mesh smoothing without topology changes, infrequent remeshing at regular intervals or when triggered by declining mesh quality, a novel hybrid geometric partitioner, accurate and conservative solution transfer to the new mesh, and a high degree of automation. We apply these techniques to simulations of internal gas flows in firing solid propellant rocket motors, as various geometrical features in the initially complex propellant configuration change dramatically due to burn-back. Smoothing and remeshing ensure that mesh quality remains high throughout these simulations without dominating the run time.

ABSTRACT Scaling an Optimistic Parallel Simulation of Large-scale Interconnection Networks

Parallel computers today are designed with larger number of processors than ever before, connected by large scale Interconnection Networks (INs). Communication is the key to achieving high performance on such machines, making the study of Interconnection Networks more important. Parallel simulations of Interconnection Networks present a unique problem characterized by fine-grained computation and a strong dependence among events. The absence of large lookaheads makes it unsuitable to use a conservative simulation. Using an optimistic Parallel Discrete Event Simulation (PDES) allows us to extract reasonable parallelism from this simulation. In this paper we present BigNetSim, an Interconnection Network simulator. We analyze its performance and present techniques related to enhancing performance and scaling it to a large number of processors on different artificial traffic patterns and real application logs. In spite of the overheads of a parallel optimistic simulation, we have achieved a breakeven point with sequential simulation at 4 processors and demonstrate perfect scaling to 128 processors. 1