AMP: A Science-driven Web-based Application for the TeraGrid

The Asteroseismic Modeling Portal (AMP) provides a web-based interface for astronomers to run and view simulations that derive the properties of Sun-like stars from observations of their pulsation frequencies. In this paper, we describe the architecture and implementation of AMP, highlighting the lightweight design principles and tools used to produce a functional fully-custom web-based science application in less than a year. Targeted as a TeraGrid science gateway, AMP's architecture and implementation are intended to simplify its orchestration of TeraGrid computational resources. AMP's web-based interface was developed as a traditional standalone database-backed web application using the Python-based Django web development framework, allowing us to leverage the Django framework's capabilities while cleanly separating the user interface development from the grid interface development. We have found this combination of tools flexible and effective for rapid gateway development and deployment.Comment: 7 pages, 2 figures, in Proceedings of the 5th Grid Computing Environments Worksho

Support vector machines for image and electronic mail classification

Support Vector Machines (SVMs) have demonstrated accuracy and efficiency in a variety of binary classification applications including indoor/outdoor scene categorization of consumer photographs and distinguishing unsolicited commercial electronic mail from legitimate personal communications. This thesis examines a parallel implementation of the Sequential Minimal Optimization (SMO) method of training SVMs resulting in multiprocessor speedup subject to a decrease in accuracy dependent on the data distribution and number of processors. Subsequently the SVM classification system was applied to the image labeling and e-mail classification problems. A parallel implementation of the image classification system\u27s color histogram, color coherence, and edge histogram feature extractors increased performance when using both noncaching and caching data distribution methods. The electronic mail classification application produced an accuracy of 96.69% with a user-generated dictionary. An implementation of the electronic mail classifier as a Microsoft Outlook add-in provides immediate mail filtering capabilities to the average desktop user. While the parallel implementation of the SVM trainer was not supported for the classification applications, the parallel feature extractor improved image classification performance

2004: Comparing Linux clusters for the Community Climate System Model. Fifth Int

Abstract. In this paper, we examine the performance of two components of the NCAR Community Climate System Model (CCSM) executing on clusters with a variety of microprocessor architectures and interconnects. Specifically, we examine the execution time and scalability of the Community Atmospheric Model (CAM) and the Parallel Ocean Program (POP) on Linux clusters with Intel Xeon and AMD Opteron processors, using Dolphin, Myrinet, and Infiniband interconnects, and compare the performance of the cluster systems to an SGI Altix and an IBM p690 supercomputer. Of the architectures examined, clusters constructed using AMD Opteron processors generally demonstrate the best performance, outperforming Xeon clusters and occasionally an IBM p690 supercomputer in simulated years per day.