A virtual PNNI network testbed

A parallel circuit simulator, iPRIDE, which uses a direct solution method and runs on a shared-memory multiprocessor is described. The simulator is based on a multilevel node tearing approach which produces a nested bordered-block-diagonal (BBD) form of the circuit equation matrix. The parallel solution of the nested BBD matrix is described. Its efficiency is shown to depend on how the circuit is partitioned as well as the sequence in which the concurrent tasks (scheduling) are solved. A partitioning heuristic is proposed, assuming that an arbitrary number of processors is available. Scheduling methods are studied when the number of processors is fixed. A method for determining the optimal level of partitioning, which depends on the number of processors as well as the scheduling methods, is described. The program is implemented on an ALLIANT FX/8 multiprocessor with shared memory. The performance of the program is reported. >

This paper examines the cost/performance of simulating a hypothetical target parallel computer using a commercial host parallel computer. We address the question of whether parallel simulation is simply faster than sequential simulation, or if it is also more cost-effective. To answer this, we develop a performance model of the Wisconsin Wind Tunnel (WWT), a system that simulates cache-coherent shared-memory machines on a message-passing Thinking Machines CM-5. The performance model uses Kruskal and Weiss's fork-join model to account for the effect of event processing time variability on WWT's conservative fixed-window simulation algorithm. A generalization of Thiebaut and Stone's footprint model accurately predicts the effect of cache interference on the CM-5. The model is calibrated using parameters extracted from a fully-parallel simulation (p=N), and validated by measuring the speedup as the number of processors (p) ranges from one to the number of target nodes (N. Together with simple cost models, the performance model indicates that for target system sizes of 32 nodes and larger, parallel simulation is more cost-effective than sequential simulation. The key intuition behind this result is that large simulations require large memories, which dominate the cost of a uniprocessor; parallel computers allow multiple processors to simultaneously access this large memory.

Research in parallel simulation has been around for more than two decades. However, the number of papers reporting on its application to real world problems is limited. At the 2002 PADS conference, researchers discussed the need to go beyond synchronization and performance issues, and, in particular, to demonstrate that parallel simulation could be used in real world applications outside military and network simulations. Since then, we have seen an increase in the number of papers on the parallel simulation applications in areas such as operations management and the physical sciences. This paper advocates the application of parallel simulation in population dynamics which is often used as the basis for policy planning and analysis. In this paper, we show how the simulation model is implemented using the μsik parallel simulation library. We conduct some experiments to measure the simulation performance. The result shows that good event parallelism can be achieved. 1

One approach to network emulation involves simulating a virtual network with a real-time network simulator and providing an I/O interface that enables interaction between real hosts and the virtual network. This allows real protocols and applications to be tested in a controlled and repeatable environment. To reflect conditions of large networks such as the Internet it is important that the emulation environment be scalable. This paper examines improvements in scalability of the virtual network achieved through the use of parallel discrete event simulation and simulation abstraction. Using just parallel simulation techniques, real-time emulation performance of nearly 50 million packet transmissions per second is achieved on 128 processors for a network model consisting of about 20,000 nodes. Using both parallel simulation and abstraction techniques, real-time emulation performance of nearly 500 million packet transmissions per second is achieved on 128 processors for a network model consisting of about 200,000 nodes.

Presents an approach for creating distributed, component-based simulations of communication networks by interconnecting models of sub-networks drawn from different network simulation packages. This approach supports the rapid construction of simulations for large networks by reusing existing models and software, and fast execution using parallel discrete event simulation techniques. A dynamic simulation backplane is proposed that provides a common format and protocol for message exchange, and services for transmitting data and synchronizing heterogeneous network simulation engines. In order to achieve plug-and-play interoperability, the backplane uses existing network communication standards and dynamically negotiates among the participant simulators to define a minimal subset of required information that each simulator must supply, as well as other optional information. The backplane then automatically creates a message format that can be understood by all participating simulators and dynamically creates the content of each message by using callbacks to the simulation engines. We describe our approach to interoperability as well as an implementation of the backplane. We present results that demonstrate the proper operation of the backplane by distributing a network simulation between two different simulation packages, ns2 and GloMoSim. Performance results show that the overhead for the creation of the dynamic messages is minimal. Although this work is specific to network simulations, we believe our methodology and approach can be used to achieve interoperability in other distributed computing applications as well.

MIMOSE1 is an abbreviation for MIcro- and multilevel MOdelling SoftwarE. It stands for a modelling and simulation software system which consists of a model description language and an experimental frame for the simulation of the described models.

We introduce a simulation engine to visually evaluate and compare distance based lateration algorithms and deployments called the FU Berlin Parallel Lateration-Algorithm Simulation and Visualization Engine (LS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). Our engine simulates a scenario which consists of given anchor positions and evaluates all positions of a playing field in parallel, instead of only randomly selected positions. At the end of a simulation run, the user is able to judge the strengths and weaknesses of the algorithm in a picture that displays the spatial position error distribution, a representation of positions in a plane with their errors. Understanding spacial distribution of error is especially valuable, because we observed that it depends on the placement of anchor nodes. This enables the developer to optimize his algorithm or aid him in selecting an algorithm for his application. The simulator's design separates the simulation engine, the lateration algorithms, and the error models. The simulator can be easily extended with additional lateration algorithms and error models. The engine is written in GNU C99 and uses the SSE or AVX vector extensions of modern microprocessors. Thus, it is able to scale fully to all cores. Beside extendability, the main focus is set on execution speed.

This paper describes the parallel simulation of a connectionist network stereo matching algorithm. In the algorithm a relaxation computation uses a variety of consistency measures between candidate matches to determine the correct match for each edge. These measures include disparity consistency, contour consistency, and multiresolution consistency. In the network each node represents a candidate match and the connections between nodes implement the consistency relations. The simulation involves constructing the list of candidate matches, constructing the list of connections between matches, and performing the iterations of the relaxation computation. Within each of these stages of the simulation, computation may be partitioned among a number of processors with very little need for synchronization or mutual exclusion except at the end of the stage. As a result, the implementation of the simulation on a shared-memory multiprocessor produced nearly linear speed-ups for up to nine processors (the maximum available at the time of implementation).

Parallelism can significantly reduce the execution time of a simulation program. However, language support is needed to handle the complexities involved so that the underlying parallel mechanisms become transparent to the users. This paper presents a simulation environment called SIMA which addresses such issues. SIMA is an experimental environment for parallel discrete-event simulation in which a simulation model can be specified by the provided primitives and be executed on a shared memory multiprocessor. The parallel mechanisms of SIMA are totally transparent to users. This property makes usage of SIMA very convenient. Users can have the benefit of faster execution on a parallel computer without requiring knowledge about parallel processing. The base language is C and the interfaces are library routines and input arguments. The interfaces facilitate both parallel and sequential simulation. Several experiments indicate that SIMA has a satisfactory performance. Speedups of over 10 for many cases have been achieved using 15 processors. >

Parallelism can significantly reduce the execution time of a simulation program. However, language support is needed to handle the complexities involved so that the underlying parallel mechanisms become transparent to the users. This paper presents a simulation environment called SIMA which addresses such issues. SIMA is an experimental environment for parallel discrete-event simulation in which a simulation model can be specified by the provided primitives and be executed on a shared memory multiprocessor. The parallel mechanisms of SIMA are totally transparent to users. This property makes usage of SIMA very convenient. Users can have the benefit of faster execution on a parallel computer without requiring knowledge about parallel processing. The base language is C and the interfaces are library routines and input arguments. The interfaces facilitate both parallel and sequential simulation. Several experiments indicate that SIMA has a satisfactory performance. Speedups of over 10 for many cases have been achieved using 15 processors. >

This paper critically reviews certain aspects of the simulation of telecommunication networks. In particular we consider simulation software, the use of high-speed simulation techniques using parallel simulation and the incorporation of artificial intelligence in simulation models. The paper concludes by considering likely future developments in simulation environments including the incorporation of simulation modelling tools as an integral part of the overall network management strategy. (4 pages)

The optimal level of performance from parallel discrete-event simulation depends on the circuit being simulated, the vectors being simulated, and the machine on which the simulation is being performed. Empirical studies based on very simple models suggest that the amount of parallelism available in typical circuits is very small. A model of optimal performance for a machine with an infinite number of processors having uniform memory accesses is presented. It demonstrates that some circuits have significantly more parallelism than previously believed. The model is refined to define the optimal load partitioning for a machine with a finite number of processors with uniform access and extended to define the optimal static data partitioning. A metric is obtained which can be used to benchmark different models of parallel simulation. The effectiveness of these models in detecting performance problems of the version of RSIM running on the BBN Butterfly is shown.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Expert simulation for online production control

Parallel and distributed simulation techniques have been investigated in a number of studies as a way to decrease long execution times of personal communication system (PCS) simulations. However, most studies have omitted important issues in radio propagation in the simulations. Including these radio propagation issues makes it significantly more difficult to partition a PCS model efficiently. In this paper we consider parallel simulation of PCS models using time warp and focus on the partitioning issue. We compare base station based partitioning with channel based partitioning for an F/TDMA system. Analytical and experimental results on a shared memory multiprocessor indicate that for realistic parameters the partitioning per channel will significantly outperform the partitioning per base station.

An object-oriented simulator model for parallel computer systems is described. It is designed for a shared memory multiprocessor and uses the principle of execution-driven simulation. Two alternatives for structuring the simulation are presented and evaluated. The first uses a single shared event list object and treats all processes uniformly. In the second, the parallel-program model and the target parallel-architecture model are each represented as a set of process objects that communicate with each other by means of messages. Only the local simulation times of the processes involved are examined prior to removing an event from the event list. This method enforces timestamp ordering among all events, thus providing the advantages of a global time and a global event list. Two independent concepts are integrated: parallel simulation on shared memory and execution-driven simulation of concurrent programs. >