A Multimedia Interactive Environment Using Program Archetypes: Divide-and-Conquer

As networks and distributed systems that can exploit parallel computing become more widespread, the need for ways to teach parallel programming effectively grows as well. Even though many colleges and universities provide courses on parallel programming [1], most of those courses are reserved for graduate students and advanced undergraduates. There is a demand for ways to teach fundamental parallel programming concepts to people with just a working knowledge of programming. By using the idea of a software archetype, and providing a learning environment that teaches both concept and coding, we hope to satisfy this need. This paper presents an overview of the multimedia approach we took in teaching parallel programming and offers Divide-and-Conquer as an example of its use

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

Software for Remote Parallel Simulation

This paper describes distributed/parallel simulation system Triad.Net and software, which allows geographical distributed users to participate collaboratively and remotely in simulation experiments and to observe simulation model behavior via Internet

Acceleration techniques for dependability simulation

As computer systems increase in complexity, the need to project system performance from the earliest design and development stages increases. We have to employ simulation for detailed dependability studies of large systems. However, as the complexity of the simulation model increases, the time required to obtain statistically significant results also increases. This paper discusses an approach that is application independent and can be readily applied to any process-based simulation model. Topics include background on classical discrete event simulation and techniques for random variate generation and statistics gathering to support simulation