The nature of statistical learning theory
The nature of statistical learning theory
The SGI Origin: a ccNUMA highly scalable server
Proceedings of the 24th annual international symposium on Computer architecture
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Parallel Computer Architecture: A Hardware/Software Approach
Parallel Computer Architecture: A Hardware/Software Approach
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Scalable Shared-Memory Multiprocessing
Scalable Shared-Memory Multiprocessing
How Much Does Network Contention Affect Distributed Shared Memory Performance?
ICPP '97 Proceedings of the international Conference on Parallel Processing
The Use of Prediction for Accelerating Upgrade Misses in cc-NUMA Multiprocessors
Proceedings of the 2002 International Conference on Parallel Architectures and Compilation Techniques
Support Vector Machine Regression for Volatile Stock Market Prediction
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
WildFire: A Scalable Path for SMPs
HPCA '99 Proceedings of the 5th International Symposium on High Performance Computer Architecture
Statistical Simulation of Symmetric Multiprocessor Systems
SS '02 Proceedings of the 35th Annual Simulation Symposium
The impact of wrong-path memory references in cache-coherent multiprocessor systems
Journal of Parallel and Distributed Computing
An Evaluation of the Oak Ridge National Laboratory Cray XT3
International Journal of High Performance Computing Applications
Statistical sampling of microarchitecture simulation
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Travel-time prediction with support vector regression
IEEE Transactions on Intelligent Transportation Systems
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Recent advances in the development of optical technologies suggest the possible emergence of optical interconnects within distributed shared memory (DSM) multiprocessors. The performance of these DSM architectures must be evaluated under varying values of DSM parameters. In this paper, we develop a Support Vector Regression (SVR) model for predicting the performance measures (i.e. average network latency, average channel waiting time and average processor utilization) of a DSM multiprocessor architecture interconnected by the Simultaneous Optical Multiprocessor Exchange Bus (SOME-Bus), which is a high-bandwidth, fiber-optic interconnection network. The basic idea is to collect a small number of data points by using a statistical simulation and predict the performance measures of the system for a large set of input parameters based on these. OPNET Modeler is used to simulate the DSM-based SOME-Bus multiprocessor architecture and to create the training and testing datasets. The prediction error and correlation coefficient of the SVR model is compared to that of Multiple Linear Regression (MLR) and feedforward Artificial Neural Network (ANN) models. Results show that the SVR-RBF model has the lowest prediction error and is more robust. It is concluded that SVR model shortens the time quite a bit for obtaining the performance measures of a DSM multiprocessor and can be used as an effective tool for this purpose.