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Predicting the performance measures of a message-passing multiprocessor architecture using artificial neural networks

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Abstract

In this paper, we develop multi-layer feed-forward artificial neural network (MFANN) models for predicting the performance measures of a message-passing multiprocessor architecture interconnected by the simultaneous optical multiprocessor exchange bus (SOME-Bus), which is a fiber-optic interconnection network. OPNET Modeler is used to simulate the SOME-Bus multiprocessor architecture and to create the training and testing datasets. The performance of the MFANN prediction models is evaluated using standard error of estimate (SEE) and multiple correlation coefficient (R). Also, the results of the MFANN models are compared with the ones obtained by generalized regression neural network (GRNN), support vector regression (SVR), and multiple linear regression (MLR). It is shown that MFANN models perform better (i.e., lower SEE and higher R) than GRNN-based, SVR-based, and MLR-based models for predicting the performance measures of a message-passing multiprocessor architecture.

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Acknowledgments

We would like to thank the OPNET Technologies, Inc. for letting us use the OPNET Modeler under the University Program and to Cukurova University Scientific Research Projects Center for supporting this work (Project no: MMF2011D8). We would also like to thank Dr. Constantine Katsinis for letting us include the material about the SOME-Bus architecture in this paper.

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Authors and Affiliations

  1. Department of Electrical-Electronics Engineering, Cukurova University, Adana, 01330, Turkey

    Elrasheed Ismail Mohommoud Zayid

  2. Department of Computer Engineering, Cukurova University, Adana, 01330, Turkey

    Mehmet Fatih Akay

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  1. Elrasheed Ismail Mohommoud Zayid
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  2. Mehmet Fatih Akay
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Correspondence to Mehmet Fatih Akay.

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Zayid, E.I.M., Akay, M.F. Predicting the performance measures of a message-passing multiprocessor architecture using artificial neural networks. Neural Comput & Applic 23, 2481–2491 (2013). https://doi.org/10.1007/s00521-012-1267-9

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  • DOI: https://doi.org/10.1007/s00521-012-1267-9

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