Reliable attributes selection technique for predicting the performance measures of a DSM multiprocessor architecture

In this study we develop a model for predicting the performance measures of a distributed shared memory (DSM) multiprocessor architecture by using a reliable attributes selection method. The structure of a DSM platform is interconnected by the Simultaneous Optical Multiprocessor Exchange Bus (SOME-Bus), which is a low latency high bandwidth fiber-optic interconnection network. OPNET Modeler is used to simulate the SOME-Bus multiprocessor architecture and to create the datasets. The input variables chosen for the prediction model include the ratio service time over packet transfer time (varies from 0.01 to 1), traffic patterns (uniform, hot region, bit reverse and perfect shuffle), DSM protocol type, node number (varies to 16, 32 and 64), thread number (varies from 1 to 6). The attributes selection method examined the models using different machine learning tools. These tools include: multilayer feed forward artificial neural network (MFANNs), support vector regression with radial basis function (SVR-RBF) and multiple linear regression (MLR). Cross validation (CV) technique is applied using 10 folds. The results show that MFANN-based model gives the best results (i.e. SEE=11.1 and R = 0.998587 for CWT; SEE=18.96 and R = 0.997 for NRT; SEE=60.46 and R=0.8638 for IWT; SEE=0.04795 and R = 0.9838 for PU; SEE=0.0348 and R=0.9990 for CU). Results of the constructed new selected subset are compared with the original feature space and the findings prove the accuracy and reliability of the model.