Enhancing Energy Production with Exascale HPC Methods

High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the Intel Corporation, which enabled us to obtain the presented experimental results in uncertainty quantification in seismic imagingPostprint (author's final draft

Applying future Exascale HPC methodologies in the energy sector

The appliance of new exascale HPC techniques to energy industry simulations is absolutely needed nowadays. In this sense, the common procedure is to customize these techniques to the specific energy sector they are of interest in order to go beyond the state-of-the-art in the required HPC exascale simulations. With this aim, the HPC4E project is developing new exascale methodologies to three different energy sources that are the present and the future of energy: wind energy production and design, efficient combustion systems for biomass-derived fuels (biogas), and exploration geophysics for hydrocarbon reservoirs. In this work, the general exascale advances proposed as part of HPC4E and its outcome to specific results in different domains are presented.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the Intel Corporation, which enabled us to obtain the presented experimental results in uncertainty quantification in seismic imaging.Postprint (author's final draft

10181 Abstracts Collection -- Program Development for Extreme-Scale Computing

From May 2nd to May 7th, 2010, the Dagstuhl Seminar 10181 ``Program Development for Extreme-Scale Computing \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. Links to extended abstracts or full papers are provided, if available

Uniform resource visualization

Computing environments continue to increase in scale, heterogeneity, and hierarchy, with resource usage varying dynamically during program execution. Computational and data grids and distributed collaboration environments are examples. To understand performance and gain insights into developing applications that efficiently use system resources, performance visualization has proven useful. Performance visualization tools, however, often are specific to a particular resource at a certain level of the system, possibly with fixed views. Thus, they limit a user\u27s ability to observe a performance problem associated with multiple resources across system levels and platforms. To address this limitation, information integration is necessary. In this research, we propose a new performance visualization framework, Uniform Resource Visualization (URV), focusing on integration of performance information into system-level visualizations. The goal of URV research is to systemize the performance visualization of resources with reusable and composable visualizations

Essays on the use of e-Learning in statistics and the implementation of statistical software

Die vorliegende Doktorarbeit bündelt die Veröffentlichungen des Autors und seiner Koautoren zu den Themen e-Learning und statistischer Software. Die Kapitel 2 bis 5 sind Aspekten des e-Learning gewidmet, die Kapitel 6 bis 9 beschreiben die Entwicklung der statistischen Programmiersprache Yxilon. In Kapitel 2, Koautoren Wolfgang Härdle und Sigbert Klinke, wird erörtert, ob und wie computerbasierte Elemente in den Kanon der methodischen Bildung integriert werden sollen und wo die Grenzen des e-Learning in der Statistik-Ausbildung liegen. Kapitel 3, Koautoren Wolfgang Härdle und Sigbert Klinke, gibt Einschätzungen verschiedener e-Learning Plattformen und beschreibt Punkte, die bei der Entwicklung von e-Learning Plattformen berücksichtigt werden sollten. Kapitel 4, geschrieben mit Wolfgang Härdle und Sigbert Klinke, diskutiert zwei Veröffentlichungen in der "International Statistical Review", die eine technische Lösung für die Verbesserung des Verständnisses der Statistik-Lehre vorstellen. Kapitel 5, Koautoren Wolfgang Härdle und Sigbert Klinke, beschreibt die Anwendung von Web-Techniken für die Lehre in Statistik. Weiterhin stellt es die Quantnet Plattform vor, eine Plattform für die Verwaltung von Programmen und Daten. In Kapitel 6, Koautoren Wolfgang Härdle und Sigbert Klinke, diskutieren die Autoren die Anforderungen an eine Statistical Engine. Kapitel 7, geschrieben mit Yuval Guri und Sigbert Klinke, erläutert die Ideen, die zur Re-Implementierung der XploRe Sprache geführt haben und diskutiert ausgewählte technische Aspekte der Yxilon Plattform wie Objektdatenbank und die Erzeugung von kompilierbarem Code für Hochsprachen. In Kapitel 8, Koautoren Wolfgang Härdle und Sigbert Klinke, wird die implementierte Client-Server Struktur beschrieben. Server und Kommunikationsprotokoll werden zusammen mit dem entwickelten Client und der Grafik-Engine beschrieben. Das letzte Kapitel, beschreibt die Struktur der Yxilon Plattform in ihrer jetzigen Form.The following doctoral thesis collects the papers the author has written with his coauthors on e-Learning and statistical software. The chapters 2 to 5 are devoted to selected aspects of e-Learning, the chapters 6 to 9 describe the development of the statistical programming environment Yxilon. In chapter 2, coauthored by Wolfgang Härdle and Sigbert Klinke, the question whether and how computational elements should be integrated into the canon of methodological education and where e-techniques have their limits in statistics education is discussed. Chapter 3, coauthored by Wolfgang Härdle and Sigbert Klinke, gives reviews of different e-learning platforms for statistics and reveals facts that may be taken into account for future e-learning platforms in statistics and related fields. Chapter 4, written with Wolfgang Härdle and Sigbert Klinke, discusses two papers published in International Statistical Review which both offer a technical solution to improve the understanding of statistics by students. Chapter 5, coauthored by Wolfgang Härdle and Sigbert Klinke, describes web-related techniques for teaching statistics. It furthermore introduces the Quantnet platform, a framework to manage scientific code and data. In chapter 6, coauthored by Wolfgang Härdle and Sigbert Klinke, the requirements for a statistical engine are discussed. Chapter 7, written jointly with Yuval Guri and Sigbert Klinke, explains ideas which led to the reimplementation of the XploRe language. In chapter 8, coauthored by Wolfgang Härdle and Sigbert Klinke, the implemented client/server structure of the Yxilon platform is laid out in terms of technical features. The server and the communication protocol are described together with the developed Java client featuring the Jasplot graphics engine. Finally chapter 9 describes the structure of the Yxilon environment in its present form

CAMP: A Common API for Measuring Performance

Accurate performance testing of heterogeneous distributed systems, such as those created using GRID technology, requires a consistent method for retrieving system performance data from multiple platforms. This paper presents CAMP: a low-level platform independent performance data API designed for use with distributed testing frameworks. CAMP is not necessarily tied to the distributed testing task: it provides a simple, low-level interface into operating system performance data that can be used to build complex performance measurement applications. This paper discusses CAMP\u27s functionality and implementation in detail. It also contains a detailed analysis of the API\u27s correctness, performance, and overhead

Performance Engineering Technology for Scientific Component Software

Large-scale, complex scientific applications are beginning to benefit from the use of component software design methodology and technology for software development. Integral to the success of component-based applications is the ability to achieve high-performing code solutions through the use of performance engineering tools for both intra-component and inter-component analysis and optimization. Our work on this project aimed to develop performance engineering technology for scientific component software in association with the DOE CCTTSS SciDAC project (active during the contract period) and the broader Common Component Architecture (CCA) community. Our specific implementation objectives were to extend the TAU performance system and Program Database Toolkit (PDT) to support performance instrumentation, measurement, and analysis of CCA components and frameworks, and to develop performance measurement and monitoring infrastructure that could be integrated in CCA applications. These objectives have been met in the completion of all project milestones and in the transfer of the technology into the continuing CCA activities as part of the DOE TASCS SciDAC2 effort. In addition to these achievements, over the past three years, we have been an active member of the CCA Forum, attending all meetings and serving in several working groups, such as the CCA Toolkit working group, the CQoS working group, and the Tutorial working group. We have contributed significantly to CCA tutorials since SC'04, hosted two CCA meetings, participated in the annual ACTS workshops, and were co-authors on the recent CCA journal paper [24]. There are four main areas where our project has delivered results: component performance instrumentation and measurement, component performance modeling and optimization, performance database and data mining, and online performance monitoring. This final report outlines the achievements in these areas for the entire project period. The submitted progress reports for the first two years describe those year's achievements in detail. We discuss progress in the last project period in this document. Deployment of our work in CCA components, frameworks, and applications is an important metric of success. We also summarize the project's accomplishments in this regard at the end of the report. A list of project publications is also given