Performance Models of Multiprocessor Systems
While there are several studies of computer systems modeling and performance evaluation where models of multiprocessor systems can be found as examples of applications of general modeling techniques, this is the first to focus entirely on the problem of modeling and performance evaluation of multiprocessor systems using analytical methods.Increasingly sophisticated and fast-moving technologies require models that can estimate the performance of a computer system without having actually to build and test it, models that can help designers make the correct architectural choices. The area of distributed computer architectures, or multiprocessor systems, has numerous such choices and can greatly benefit from an extensive use of performance evaluation techniques in the system design stage.The multiprocessor features that are studied here focus on contention for physical system resources, such as shared devices and interconnection networks. A brief overview covers the modeling of other important system characteristics, such as failures of components and synchronizations at the software level.Contents: Stochastic Processes. Queuing Models. Stochastic Petri Nets. Multiprocessor Architectures. Analysis of Crossbar Multiprocessor Architecture. Single Bus Multiprocessors with External Common Memory. Multiple Bus Multiprocessors with External Common Memory. Single Bus Multiprocessors with Distributed Common Memory. Multiple Bus Multiprocessors with Distributed Common Memory.The authors are affiliated with Politecnico and Universita di Torino in Italy. Performance Models of Multiprocessor Systems is included in The MIT Press Series in Computer Systems, edited by Herb Schwetman.
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access requests analysis approximate Architecture 2a assume average number B-D process balance equations BCMP theorem behavior Chapman-Kolmogorov equation chapter common memory module complexity comprises computing systems condition considered continuous-time Markov chain Coxian distribution CTMC cycle defined denote discrete-time distributed computing distributed random variables DTMC ergodic exponentially distributed external common memory FCFS finite firing function global bus GSPN in figure GSPN model immediate transitions interconnection network load macrostates Markov property memoryless multiprocessor system nonlocal common memory normalization constant number of buses number of common number of customers number of processors obtained parameters passive resources Performance Evaluation Petri Nets Poisson Poisson process possible private memory probability distribution processing power processor accesses product form solution queue queuing network random variables recurrent server service time distributions shown in figure station steady state distribution steady state probabilities stochastic process subsystem transition probability matrix transition rate diagram workload