Machine Learning Models to Predict Performance of Computer System Design Alternatives

Computer manufacturers spend a huge amount of time, resources, and money in designing new systems and newer configurations, and their ability to reduce costs, charge competitive prices, and gain market share depends on how good these systems perform. In this work, we concentrate on both the system design and the architectural design processes for parallel computers and develop methods to expedite them. Our methodology relies on extracting the performance levels of a small fraction of the machines in the design space and using this information to develop linear regression and neural network models to predict the performance of any machine in the whole design space. In terms of architectural design, we show that by using only 1% of the design space (i.e., cycle-accurate simulations), we can predict the performance of the whole design space within 3.4% error rate. In the system design area, we utilize the previously published Standard Performance Evaluation Corporation (SPEC) benchmark n...

• Type: Conference
• Year: 2008
• Where: ICPP
• Authors: Berkin Özisikyilmaz, Gokhan Memik, Alok N. Choudhary
• Keywords: Architectural Design, Design Space, Distributed And Parallel Computing, ICPP 2008, Performance