e-j 4r.,,D I-- " h",' _ k,) r,m '3'-. IC,-.-4 Z _ O

Abstract not found

Recently there has been a great deal of interest in performance evaluation of parallel simulation. Most work is devoted to the time complexity and assumes that the amount of memory available for parallel simulation is unlimited. This paper studies the space complexity of parallel simulation. Our goal is to design an efficient memory management protocol which guarantees that the memory consumption of parallel simulation is of the same order as sequential simulation. (Such an algorithm is referred to as optimal.) We first derive the relationships among the space complexities of sequential simulation, Chandy-Misra simulation, and Time Warp simulation. We show that Chandy-Misra may consume more storage than sequential simulation, or vice versa. Then we show that Time Warp never consumes less memory than sequential simulation. Then we describe cancelback, an optimal Time Warp memory management protocol proposed by Jefferson. Although cancelback is considered as a complete solution for the storage management problem in Time Warp, some efficiency issues in implementing this algorithm must be considered. In this paper, we propose an optimal algorithm called artificial rollback. We show that this algorithm is easy to implement and analyze. An implementation of artificial rollback is given, which is integrated with processor scheduling to adjust the memory consumption rate based on the amount of free storage available in the system.

Complex models may have model components distributed over a network and generally require significant execution times. The field of parallel and distributed simulation has grown over the past fifteen years to accommodate the need of simulating the complex models using a distributed versus sequential method. In particular, asynchronous parallel discrete event simulation (PDES) has been widely studied, and yet we envision greater acceptance of this methodology as more readers are exposed to PDES introductions that carefully integrate real-world applications. With this in mind, we present two key methodologies (con- servative and optimistic) which have been adopted as solutions to PDES systems. We discuss PDES terminology and methodology under the umbrella of the personal communications services application.

In this paper, we introduce a new Time Warp system called ROSS: Rensselaer's Optimistic Simulation System. ROSS is an extremely modular kernel that is capable of achieving event rates as high as 1,250,000 events per second when simulating a wireless telephone network model (PCS) on a quad processor PC server. In a head-to-head comparison, we observe that ROSS out performs the Georgia Tech Time Warp (GTW) system by up to 180% on a quad processor PC server and up to 200% on the SGI Origin 2000 . ROSS only requires a small constant amount of memory buffers greater than the amount needed by the sequential simulation for a constant number of processors. ROSS demonstrates for the first time that stable, highly-efficient execution using little memory above what the sequential model would require is possible for low-event granularity simulation models. The driving force behind these high-performance and low memory utilization results is the coupling of an efficient pointer-based implementation framework, Fujimoto 's fast GVT algorithm for shared memory multiprocessors, reverse computation and the introduction of Kernel Processes (KPs). KPs lower fossil collection overheads by aggregating processed event lists. This aspect allows fossil collection to be done with greater frequency, thus lowering the overall memory necessary to sustain stable, efficient parallel execution. These characteristics make ROSS an ideal system for use in large-scale networking simulation models. The principle conclusion drawn from this study is that the performance of an optimistic simulator is largely determined by its memory usage. 1

Abstract not found

Performance of Time Warp simulation systems are often measured on exclusively available parallel computing resources. In distributed systems exclusive use is normally not feasible. Instead, due to the multi-tasking operating systems, many users share the workstations and their availability for parallel simulation purposes varies extensively. Time Warp has been found to be very sensitive to variations in available processing power. This paper presents two methods for a Time Warp VLSI simulation system to reduce the negative effect of a non-ideal environment on the execution of parallel simulations. A dynamic load balancing algorithm which adapts to the change of available processing power is presented. This mechanism, together with a multi-cluster partitioning technique significantly improves the performance of Time Warp based simulation systems on heterogeneous computing resources. 1 Introduction Due to the increasing complexity of digital circuits, digital logic simulation suffers fr...

Abstract not found

An abstraction called space-time memory is discussed that allows parallel discrete event simulation programs using the Time Warp mechanism to be written using shared memory constructs. A few salient points concerning the implementation and use of space-time memory in parallel simulation are discussed. It is argued that this abstraction is useful from a programming standpoint for certain applications, and can yield good performance. Initial performance measurements of a prototype implementation of the abstraction on a shared-memory multiprocessor are described, and compared with a conventional, message-based implementation of Time Warp.

We present an Incremental State Saving technique for which the state saving calls are inserted automatically by directly editing the application executable. This method has the advantage of being easy to use since it is fully automatic, and has good performance since it adds overhead only where state is being modified. Since the editing happens on executable code, the method is independent of the compiler, and allows third party libraries to be used. None of the previous incremental state saving methods have both of these features. We find that it is beneficial to use Automatic Incremental State Saving if less than 15% of the state is modified in each event as compared to copy state saving. This technique allows us to efficiently parallelize existing simulations, and makes Time Warp more accessible to non-Time Warp experts. 1. Introduction The efficiency with which Time Warp is able extract parallelism from a simulation problem is well recognized [4]. It is able to achieve these highe...