Parallel Simulation

1. Parallel Simulation By: Sameer Hussain, #1727466 Benaceur Assif, #1871157 Date: Tuesday, March 12, 2002 This report was prepared for Professor L. Orozco-Barbosa in partial fulfillment of the requirements for the course ELG/CEG 4183.

2. Topics Simulation Types of Simulation Parallel Simulation PARSEC, PAVE, Compose. Future of Parallel Simulation Questions Bibliography Topics to be covered in this presentationTopics to be covered in this presentation

3. What is simulation? Definition 1: The use of mathematical/logical model as a way of answering question about a particular system. Definition 2: Computer simulation is the discipline of designing a model of an actual or theoretical physical system, executing the model on a digital computer, and analyzing the execution output. Model: A simple description of an object which might be used in calculations. Simulation embodies the principle of “learning by doing” - to learn about the system we must first build a model of some sort and then operate the model.Model: A simple description of an object which might be used in calculations. Simulation embodies the principle of “learning by doing” - to learn about the system we must first build a model of some sort and then operate the model.

4. Why is simulation important? Simulation is often essential when: The model is very complex with many variables and interacting components. the underlying variables relationships are nonlinear the model contains random variables the model output is to be visual as in a 3D computer animation.

5. Use of Simulation Typically simulation is used for planning and design. Telecommunication networks Transportation systems Logistics Electronic systems (for microelectronics, computer systems) Battlefield simulation (red army vs. blue army) Manufacturing systems Ecological systems.

6. System Dynamics vs. Discrete Event Simulations can be event based or time based. System Dynamic: Analysis in a continuous time period Looks at systems at a very high level. More suited for decision making Effect shipping delay time has on production, inventory, sales Discrete Event: Analysis in a specific time horizon Looks at system in a predictive manner How many resources do we need to achieve a certain throughput? System dynamics is the rigorous study of problems in system behavior using the principles of feedback, dynamics and simulation. Discrete event deals with the change of state that occurs at discrete point in simulated time.System dynamics is the rigorous study of problems in system behavior using the principles of feedback, dynamics and simulation. Discrete event deals with the change of state that occurs at discrete point in simulated time.

7. Types of Simulation There are three types of computers simulations: Sequential: Events are schedules one after another on a single processor. Parallel: multiprocessor or cluster of workstations on a LAN Distributed: Workstation and “virtual simulators” on a WAN On the parallel or distributed computer system, the single discrete event model is broken down into a collection of smaller sub-models that can be executed concurrently. The main goal of parallel systems is to reduce model execution time. The main goal of distributed systems is to create realistic virtual environments. They are typically used for training or entertainment. On the parallel or distributed computer system, the single discrete event model is broken down into a collection of smaller sub-models that can be executed concurrently. The main goal of parallel systems is to reduce model execution time. The main goal of distributed systems is to create realistic virtual environments. They are typically used for training or entertainment.

8. Comparison between simulations Sequential vs. Parallel Simulation Some systems are too complex for sequential simulation Parallel vs. Distributed Simulation Distributed Simulations are too complex to design and control. A lack of good existing tools. Parallel simulation can be used to model complex systems while still being relatively easy to design. This is why it is very popular with researchers. Some of the systems that we with to simulate are so complex that they appear "intractable" to even the fastest uni-processor available. This is why sequential simulation are not used extensively. Distributed simulation is very complex and requires a great deal of expertise and time. Currently there are not enough tools available for it to be used as a major simulation method Some of the systems that we with to simulate are so complex that they appear "intractable" to even the fastest uni-processor available. This is why sequential simulation are not used extensively. Distributed simulation is very complex and requires a great deal of expertise and time. Currently there are not enough tools available for it to be used as a major simulation method

9. Parallel Simulation Parallel simulation always deals with discrete event systems. Advantages of parallel simulation: Reduced model execution time Scalable performance Geographically distributed users and/or resources Integrate simulations running on different platforms Fault tolerance The graphically distributed users and/or resources is an important feature when databases, specialized equipment and other needs have to be considered. They would be very hard to simulate in a sequential simulation.The graphically distributed users and/or resources is an important feature when databases, specialized equipment and other needs have to be considered. They would be very hard to simulate in a sequential simulation.

10. PARSEC PARSEC stands for PARallel Simulation Environment for Complex System It is a C-based simulation language derived from Maisie. Developed by the Parallel Computing Laboratory at UCLA It can be used for sequential and parallel execution of discrete-event simulation models. PARSEC programs consist of entities, which exchange messages. PARSEC is the next generation of Maisie, a C-based simulation language. It uses simpler syntax, new protocols to predict parallel performance and has a larger, more efficient and robust kernel. UCLA is at the forefront of Parallel Simulation Research. In addition to having one of the most well respected researches in the field (Rajive Bagrodia), it has developed PARSEC, GloMoSim and the various other parallel simulation tools. During the early 90s, researchers were expressing concern with parallel simulation due to the high level of expertise and skill required to built parallel simulation models. Having PARSEC made it much easier for researchers to perform parallel simulation.PARSEC is the next generation of Maisie, a C-based simulation language. It uses simpler syntax, new protocols to predict parallel performance and has a larger, more efficient and robust kernel. UCLA is at the forefront of Parallel Simulation Research. In addition to having one of the most well respected researches in the field (Rajive Bagrodia), it has developed PARSEC, GloMoSim and the various other parallel simulation tools. During the early 90s, researchers were expressing concern with parallel simulation due to the high level of expertise and skill required to built parallel simulation models. Having PARSEC made it much easier for researchers to perform parallel simulation.

11. PARSEC Layer Network AODV: Ad hoc On Demand Distance Vector routing algorithm is designed for ad hoc mobile networks. Bellman-Ford: A routing protocol based on the Bellman-Ford algorithm. LAR: Location Aided Routing. DSR: Dynamic Source Routing. ODMRP: On-Demand Multicast Routing Protocol. WRP: Wireless Routing Protocol. Data Link CSMA: Carrier Sense Multiple Access. IEEE 802.11: Wireless Local Area Network protocol. MACA: Medium Access with Collision Avoidance .Network AODV: Ad hoc On Demand Distance Vector routing algorithm is designed for ad hoc mobile networks. Bellman-Ford: A routing protocol based on the Bellman-Ford algorithm. LAR: Location Aided Routing. DSR: Dynamic Source Routing. ODMRP: On-Demand Multicast Routing Protocol. WRP: Wireless Routing Protocol. Data Link CSMA: Carrier Sense Multiple Access. IEEE 802.11: Wireless Local Area Network protocol. MACA: Medium Access with Collision Avoidance .

12. Use of PARSEC In Wireless: TCP Performance for Multi-hop Networks AMRIS: A Multicast Protocol for Ad hoc Providing Multiple Service Classes for Bursty Data Traffic in Cellular Networks Multi-hop Protocols Simulation and Analysis of the Cache Group Management Protocol Architectural Models for Avionics Communications Systems AMRIS: Ad hoc Multicast Routing protocol Cache Group Management Protocol: It is a solution to congestion by building a highly scalable, self-configuring, dynamically adjusting, Web caches. It will help reduce both network load and user response time. AMRIS: Ad hoc Multicast Routing protocol Cache Group Management Protocol: It is a solution to congestion by building a highly scalable, self-configuring, dynamically adjusting, Web caches. It will help reduce both network load and user response time.

13. PAVE and Compose PAVE PAVE stands for Parsec Visual Environment It is used to support visual & hierarchical design of simulation models. Compose C++ library that can be interfaced with native C++ to execute parallel simulations.

14. UCLA Simulation Environment The following figure shows the environment, supported hardware, communication packages, operating systems, synchronization algorithms and program interfaces. As can be see in the figure, the system can support uni-processor, workstation network or multiprocessor computers.The following figure shows the environment, supported hardware, communication packages, operating systems, synchronization algorithms and program interfaces. As can be see in the figure, the system can support uni-processor, workstation network or multiprocessor computers.

15. GloMoSim Architecture It uses the parallel discrete-event simulation capacity provided by Parsec. It currently supports protocols for a purely wireless network. GloMoSim stands for Global Mobile Information System Simulation Library. It was built using a layered approach that is similar to OSI layers and has been designed to support both wired and wireless networks. GloMoSim was used to model Mobile IP and Ad-hoc Networks. GloMoSim stands for Global Mobile Information System Simulation Library. It was built using a layered approach that is similar to OSI layers and has been designed to support both wired and wireless networks. GloMoSim was used to model Mobile IP and Ad-hoc Networks.

16. Future Short Term: GloMoSim will have functions for supporting wired networks and hybrid network (with both wired and wireless capabilities) Long Term: Distributed Simulation will replace parallel simulation once the proper tools are developed. More System Dynamic Simulation instead of Discrete Event simulation.

17. Questions What is simulation and why must we use it? Simulation is using logical/mathematical models to study and solve physical systems. The model is very complex with many variables and interacting components.. The variables are nonlinear. The model may contain random variables and the output has to be visual. What is the difference between discrete event simulation and system dynamic? Discrete event look at the system in a predictive manner and and analysis is for a specific time period System dynamic is more suited for decision making and is performed for continuous time.

18. Questions What are the types of computer simulation and which one is currently popular? Sequential simulation, parallel simulation, distributed simulation. Parallel simulation is popular since it can model more complex systems than sequential simulation while not being as complex to manage and program as distributed systems. Why is Parsec so important? What tools can be used with it? It allows parallel simulation. PAVE and Compose can be used with PARSEC. In addition, GloMoSim uses PARSEC calls to simulate wireless networks During the early 90s, researchers were expressing concern with parallel simulation due to the high level of expertise and skill required to built parallel simulation models. Having PARSEC made it much easier for researchers to perform parallel simulation.During the early 90s, researchers were expressing concern with parallel simulation due to the high level of expertise and skill required to built parallel simulation models. Having PARSEC made it much easier for researchers to perform parallel simulation.

19. Questions What can be modeled using parallel simulation? Various discrete event systems such as: ATM Wireless Networks Avanoics Communication systems Mobile IP

20. Bibliography Arsham H., The Use of Simulation in Discrete Event Dynamic Systems Design, Journal of System Science, 31(5), 563-573, 2000. R. Bagrodia, R. Meyer, M. Takai, Y. Chen, X. Zeng, J. Martin, and H.Y. Song, "PARSEC: A Parallel Simulation Environment for Complex Systems, " IEEE Computer, vol. 31, no. 10, October 1998, pp.77-85. R. Bagrodia and M. Gerla, "A Modular and Scalable Simulation Tool for Large Wireless Networks", International Conference on Modeling Techniques and Tools for Computer Performance Evaluation, 1998. Erik Dirkx, Parallel Computer Networks Simulations : An Evaluation, Proceedings of European Simulation MultiConference, pp. 713-716, Nuerenberg, June 1990. Xiang Zeng, GloMoSim: a Library for Parallel Simulation of Large-scale Wireless Networks, Proceedings of the 12th Workshop on Parallel and Distributed Simulations -- PADS '98, May 26-29, 1998 in Banff, Alberta, Canada.