A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure

REU Site: Supercomputing Undergraduate Program in Maine (SuperMe)

This award, for a new Research Experience for Undergraduates (REU) site, builds a Supercomputing Undergraduate Program in Maine (SuperMe). This new site provides ten-week summer research experiences at the University of Maine (UMaine) for ten undergraduates each year for three years. With integrated expertise of ten faculty researchers from both computer systems and domain applications, SuperMe allows each undergraduate to conduct meaningful research, such as developing supercomputing techniques and tools, and solving cutting-edge research problems through parallel computing and scientific visualization. Besides being actively involved in research groups, students attend weekly seminars given by faculty mentors, formally report and present their research experiences and results, conduct field trips, and interact with ITEST, RET and GK-12 participants. SuperMe provides scientific exploration ranging from engineering to sciences with a coherent intellectual focus on supercomputing. It consists of four computer systems projects that aim to improve techniques in grid computing, parallel I/O data accesses, high-resolution scientific visualization and information security, and five computer modeling projects that utilize world-class supercomputing and visualization facilities housed at UMaine to perform large, complex simulation experiments and data analysis in different science domains. SuperMe provides a diversity of cutting-edge research opportunities to students from under-represented groups or from universities in rural areas with limited research opportunities. Through interacting directly with the participant of existing programs at UMaine, including ITEST, RET and GK-12, REU students disseminates their research results and experiences to middle and high school students and teachers. This site is co-funded by the Department of Defense in partnership with the NSF REU Site program

Survey and Analysis of Production Distributed Computing Infrastructures

This report has two objectives. First, we describe a set of the production distributed infrastructures currently available, so that the reader has a basic understanding of them. This includes explaining why each infrastructure was created and made available and how it has succeeded and failed. The set is not complete, but we believe it is representative. Second, we describe the infrastructures in terms of their use, which is a combination of how they were designed to be used and how users have found ways to use them. Applications are often designed and created with specific infrastructures in mind, with both an appreciation of the existing capabilities provided by those infrastructures and an anticipation of their future capabilities. Here, the infrastructures we discuss were often designed and created with specific applications in mind, or at least specific types of applications. The reader should understand how the interplay between the infrastructure providers and the users leads to such usages, which we call usage modalities. These usage modalities are really abstractions that exist between the infrastructures and the applications; they influence the infrastructures by representing the applications, and they influence the ap- plications by representing the infrastructures

Towards Exascale Computing Architecture and Its Prototype: Services and Infrastructure

This paper presents the design and implementation of a scalable compute platform for processing large data sets in the scope of the EU H2020 project PROCESS. We are presenting requirements of the platform, related works, infrastructure with focus on the compute components and finally results of our work

Year 1 Report: Center for Trustworthy Scientific Cyberinfrastructure

This material is based in part on work supported by the National Science Foundation under Grant Number OCI-1234408. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation

3rd EGEE User Forum

We have organized this book in a sequence of chapters, each chapter associated with an application or technical theme introduced by an overview of the contents, and a summary of the main conclusions coming from the Forum for the chapter topic. The first chapter gathers all the plenary session keynote addresses, and following this there is a sequence of chapters covering the application flavoured sessions. These are followed by chapters with the flavour of Computer Science and Grid Technology. The final chapter covers the important number of practical demonstrations and posters exhibited at the Forum. Much of the work presented has a direct link to specific areas of Science, and so we have created a Science Index, presented below. In addition, at the end of this book, we provide a complete list of the institutes and countries involved in the User Forum

Austrian High-Performance-Computing meeting (AHPC2020)

This booklet is a collection of abstracts presented at the AHPC conference

The NIEHS Environmental Health Sciences Data Resource Portal: Placing Advanced Technologies in Service to Vulnerable Communities

BACKGROUND: Two devastating hurricanes ripped across the Gulf Coast of the United States during 2005. The effects of Hurricane Katrina were especially severe: The human and environmental health impacts on New Orleans, Louisiana, and other Gulf Coast communities will be felt for decades to come. The Federal Emergency Management Agency (FEMA) estimates that Katrina’s destruction disrupted the lives of roughly 650,000 Americans. Over 1,300 people died. The projected economic costs for recovery and reconstruction are likely to exceed $125 billion. OBJECTIVES: The NIEHS (National Institute of Environmental Health Sciences) Portal aims to provide decision makers with the data, information, and the tools they need to a) monitor human and environmental health impacts of disasters; b) assess and reduce human exposures to contaminants; and c) develop science-based remediation, rebuilding, and repopulation strategies. METHODS: The NIEHS Portal combines advances in geographic information systems (GIS), data mining/integration, and visualization technologies through new forms of grid-based (distributed, web-accessible) cyberinfrastructure. RESULTS: The scale and complexity of the problems presented by Hurricane Katrina made it evident that no stakeholder alone could tackle them and that there is a need for greater collaboration. The NIEHS Portal provides a collaboration-enabling, information-laden base necessary to respond to environmental health concerns in the Gulf Coast region while advancing integrative multidisciplinary research. CONCLUSIONS: The NIEHS Portal is poised to serve as a national resource to track environmental hazards following natural and man-made disasters, focus medical and environmental response and recovery resources in areas of greatest need, and function as a test bed for technologies that will help advance environmental health sciences research into the modern scientific and computing era