Due to trends like Cloud Computing and Green IT, virtualization technologies are gaining increasing importance. They promise energy and cost savings by sharing physical resources, thus making resource usage more efficient. However, resource sharing and other factors have direct effects on system performance, which are not yet well-understood. Hence, performance prediction and performance manage...

We compare numerically the effectiveness of three recently proposed algorithms, multicanonical simulations, simulations in a 1/k-ensemble, and simulated tempering, for the protein folding problem. For this we perform simulations with high statistics for one of the simplest peptides, Met-enkephalin. While the performances of all three approaches is much better than traditional methods, we find t...

We present a performance model for bandwidth limited loop kernels which is founded on the analysis of modern cache based microarchitectures. This model allows an accurate performance prediction and evaluation for existing instruction codes. It provides an in-depth understanding of how performance for different memory hierarchy levels is made up. The performance of raw memory load, store and cop...

Machine Learning methods for Performance Prediction in Intelligent Tutoring Systems (ITS) have proven their efficacy; specific methods, e.g. Matrix Factorization (MF), however suffer from the lack of available information about new tasks or new students. In this paper we show how this problem could be solved by applying Transfer Learning (TL), i.e. combining similar but not equal datasets to tr...

The BROADEN (Business Resource Optimisation for Aftermarket and Design on Engineering Networks) project aims to build a Rolls-Royce Grid as a proving ground for utilising Grid services technology. One of the central applications in this project is XTO ((eXtract Track Order) which is used to facilitate distributed diagnostics support for aircraft engines. One of the key elements in BROADEN is th...

There is a wide range of performance models being developed for the analysis of present and future generation systems. A major concern in using these models however, is the provision of realistic application workload information. There is a need for a common interface that couples generic workload information to the underlying hardware characteristics. In this work a three level framework is de...

Predicting speech recognition performance in place of expensive recognition experiments is a very useful approach for the research and development of speech recognition systems. In this paper, we propose a method to predict speech recognition performance when using new test data and/or a new acoustic model. Performance prediction tests showed that the proposed method can accurately predict reco...

Performance prediction is an appealing problem in Recommender Systems, as it enables an array of strategies for deciding when to deliver or hold back recommendations based on their foreseen accuracy. The problem, however, has been barely addressed explicitly in the area. In this paper, we propose adaptations of query clarity techniques from ad-hoc Information Retrieval to define performance pre...

In this paper we consider discrimination-aware classification of educational data. Mining and using rules that distinguish groups of students based on sensitive attributes such as gender and nationality may lead to discrimination. It is desirable to keep the sensitive attributes during the training of a classifier to avoid information loss but decrease the undesirable correlation between the se...