Parallel Approaches to the Numerical Transient Analysis of Stochastic Reward Nets

This paper presents parallel approaches to the complete tran sient numerical analysis of stochastic reward nets SRNs for both shared and distributed memory machines Parallelization concepts and imple mentation issues are discussed for the three main analysis steps that are generation of the underlying continuous time Markov chain CTMC solving the CTMC numerically for the desired time points and converting the results back to the net level by evaluating reward based result measure functions The distributed memory approach implements dynamic load balancing mechanisms in step to guarantee an equal distribution of the state space onto the main memories of the clustered machines The shared memory algorithms are based on elaborated syn chronization mechanisms which allow parallel read and write access to the global irregular data structure of the CTMC Performance measure ments on di erent architectures and a comparison of the approaches are given All the algorithms are integrated in PANDA which consequently is a parallel SRN modeling tool suitable for di erent multiprocessor plat forms