Predicting performance measures of a multiprocessor architecture by using machine learning methods

In this thesis, we develop machine learning models for predicting the performance measures of both a message passing and a distributed shared memory multiprocessor architecture interconnected by the Simultaneous Optical Multiprocessor Exchange Bus (SOME-Bus), which is a fiber-optic interconnection network. Machine learning models include multi-layer feed-forward artificial neural networks (MFANN?s), support vector regression (SVR) and generalized regression neural networks (GRNN). OPNET Modeler is used to simulate the SOME-Bus multiprocessor architecture and to create the training and testing datasets. The simulation has been run under different traffic patterns including uniform, hot-region, perfect shuffle and bit-reverse for varying values of the ratio of the average channel transfer time to the average thread run time (T/R). Client-server and asynchronous traffic models are considered for the message passing protocol. Using different number of cross validations, the performance of the machine learning prediction models are evaluated using standard error of estimate (SEE), multiple correlation coefficient (R), mean absolute error (MAE), relative absolute error (RAE) and root relative square error (RRSE). It is shown that MFANN models perform better (i.e., lower SEE, MAE, RAE, RRSE and higher R) than GRNN-based, SVR-based and multiple linear regression (MLR) based models for predicting the performance measures of a message passing and distributed shared memory multiprocessor architecture.Keywords: Multiprocessor architectures, message passing, distributedshared memory, artificial neural networks, support vector regression.