Parallel discrete event simulation has been established as a technique which has great potential to speed up the execution of gate level circuit simulation. A fundamental problem posed by a parallel environment is the decision of whether it is best to simulate a particular circuit sequentially or on a parallel platform. Furthermore, in the event that a circuit should be simulated on a parallel platform, it is necessary to decide how many computing nodes should be used on the given platform. In this paper we propose a machine learning algorithm as an aid in making these decisions. The algorithm is based on the well-known K-Nearest Neighbor algorithm. After an extensive training regime, it was shown to make a correct prediction 99% of the time on whether to use a parallel or sequential simulator. The predicted number of nodes to use on a parallel platform was shown to produce an average execution time which was not more than 12% of the smallest execution time. The configuration which resulted in the minimal execution time was picked 61% of the time.
['Qing Xu', 'Carl Tropper']
On Determining How Many Computers to Use in Parallel VLSI Simulation