Although there are many situations in which a model of application performance is valuable, performance modeling of parallel programs is not commonplace, largely because of the diiculty of developing accurate models of real applications executing on real multiprocessors. This paper describes a toolkit for performance tuning and prediction based on lost cycles analysis. Lost cycles analysis deco...

Despite construction of powerful parallel systems and networked computational grids, achieving a large fraction of peak performance for a range of applications has proven very diÆcult. In this paper, we describe the components of Delphi, an integrated performance measurement and prediction environment that places system design on a solid performance engineering basis.

The development of software tools for performance evaluation and modeling has been an active research area since the early years of computer science. In this paper, we offer a short overview of historical evolution of the field with an emphasis on popular performance modeling techniques such as queuing networks and Petri nets. A review of recent works that provide new perspectives to software t...

Performance prediction or forecasting sporting outcomes involves a great deal of insight into the particular area one is dealing with, and a considerable amount of intuition about the factors that bear on such outcomes and performances. The mathematical Theory of Evidence offers representation formalisms which grant experts a high degree of freedom when expressing their subjective beliefs in th...

In this paper, we study the effect of using domain knowledge structure on predicting student performance with parameterized Java programming exercises. Domain knowledge structure defines connections between elementary knowledge items. While known to be beneficial in general, it has not been used to predict performance. We compare five different approaches for this purpose: Bayesian Knowledge Tr...

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DEVS (Discrete Event Systems Specification) models are widely employed for simulation-based performance evaluation of discrete event systems. Performance modeling of such a system requires a pruning of a set of components, or objects, within the system in its subset which are eventually transformed into simulation models. Such modeling is objective-driven in that simulation models may be abstra...

What do you do to start reading probability stochastic processes and queueing theory the mathematics of computer performance model? Searching the book that you love to read first or find an interesting book that will make you want to read? Everybody has difference with their reason of reading a book. Actuary, reading habit must be from earlier. Many people may be love to read, but not a book. I...

in this work, several machine learning techniques are presented for nanofiltration modeling. according to the results, specific errors are defined. the rejection due to nanofiltration increases with pressure but decreases with increasing the concentration of chloride ion. methods of machine learning represent the rejection of nanofiltration as a function of concentration, ph, pressure and also ...

The aim of this study is to perform a comparison experiment between statistical and data mining modelling techniques. These techniques are statistical Logistic Regression, data mining Decision Tree and data mining Neural Network. The comparison will evaluate the performance of these prediction techniques in terms of measuring the overall prediction accuracy percentage agreement for each techniq...