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Can I read Evidence-based Static Branch Prediction Using Machine Learning on EtoBox?
Evidence-based Static Branch Prediction Using Machine Learning by Brad Calder; Dirk Grunwald; Michael Jones; Donald Lindsay; James Martin; Michael Mozer; Benjamin Zorn is a Computer Science article available to read on EtoBox.
What is Evidence-based Static Branch Prediction Using Machine Learning about?
Correctly predicting the direction that branches will take is increasingly important in today's wide-issue computer architectures. The name__program-based__branch prediction is given to static branch prediction techniques that base their prediction on a program's structure. In this article, we investigate a new approach to program-based branch prediction that uses a body of existing programs to predict the branch behavior in a new program. We call this approach to program-based branch prediction__evidence-based static prediction__, or ESP. The main idea of ESP is that the behavior of a corpus of programs can be used to infer the behavior of new programs. In this article, we use neural networks and decision trees to map static features associated with each branch to a prediction that the branch will be taken. ESP shows significant advantages over other prediction mechanisms. Specifically, it is a program-based technique; it is effective across a range of programming languages and programming styles; and it does not rely on the use of expert-defined heuristics. In this article, we describe the application of ESP to the problem of static branch prediction and compare our results to ex
Who reads Evidence-based Static Branch Prediction Using Machine Learning?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Brad Calder; Dirk Grunwald; Michael Jones; Donald Lindsay; James Martin; Michael Mozer; Benjamin Zorn
- Publisher
- ACM
- Published
- 1997
- Language
- EN
- Field
- Computer Science (Physical Sciences)