architecture behavior, most analytical methods use hierarchical models. In [3], Adve provides a framework for parallel program performance prediction models which well characterizes most of the existing models by a hierarchy of higher and lower level models. In the higher-level component, task graphs [11, 13] are usually used to represent the task-level behavior of the program. A task graph is ...

Semi-supervised learning methods using Generative Adversarial Networks (GANs) have shown promising empirical success recently. Most of these methods use a shared discriminator/classifier which discriminates real examples from fake while also predicting the class label. Motivated by the ability of the GANs generator to capture the data manifold well, we propose to estimate the tangent space to t...

The aim of this work is to evaluate three methodologies regarding semi-empirical scroll compressor modeling for different refrigerants and conduct a comparative analysis their results accuracy. The first step improve model compressors based on established techniques, further enhance the physical background some its sub-processes leading more accurate predictions. Focus then given operati...

Empirical studies on formal methods and tools are rare. In this paper, we provide guidelines for such studies. We mention their main ingredients then define nine different study strategies (usability testing, laboratory experiments with software human subjects, case studies, qualitative surveys, judgement systematic literature reviews, mapping studies) discuss each of them crucial characteristi...

This paper presents a multiprocessor performance prediction methodology supported by experimental measurements, which predicts the execution time of large application programs on large parallel architectures based on a small set of sample data. We propose a graph model to describe application program behavior. In order to precisely abstract an architecture model for the prediction, important an...