With many researchers now having easy access to supercomputers, coastal scientists are able to develop and run simulations that model the physical and ecological processes in ocean or nearshore areas in a distributed and collaborative environment. However, the increase in capacity of computational resources does not lead directly to a rapid improvement of the simulations themselves. Instead, it brings a new challenge that motivates scientists to fully utilize the huge amount of simulation data created in supercomputers, thus fostering advanced scientific research. Driven by the urgent need for in-depth investigations in Louisiana coastal areas, especially during hurricane seasons, a data center, which provides research communities with scientific data resources on demand, is imperative. In this paper, we present the design, implementation and use of such a simulation data archive for coastal science. The simulation data archive is capable of providing interfaces based on the requirements of user groups and its application incorporates multiple use cases. The enabling technology, as well as the challenges in the development of this simulation data archive, are also described in this paper.

Transfer learning is a new learning paradigm, in which, besides the training data for the targeted learning task, data that are related to the task (often under a different distribution) are also employed to help train a better learner. For example, out-dated data can be used as such related data. In this paper, we propose a new transfer learning framework for training neural network (NN) ensembles. The framework has two key features: 1) it uses the well-known negative correlation learning to train an ensemble of diverse neural networks from the related data, fully discovering the knowledge in the data; and 2) a penalized incremental learning scheme is used to adapt the neural networks obtained from negative correlation learning to the training data for the targeted learning task. The adaptation is guided by reference neural networks that measure the relatedness between the training and the related data. Experiments on benchmark data sets show that our framework can achieve classification accuracy competitive to existing ensemble transfer learning methods such as TrAdaBoost and TrBagg. We discuss some characteristics of our framework observed in the experiment and the scenarios under which the framework may have superior performance.

This paper motivates how collaborative tools are needed to support modern computational science, using the case study of a distributed team of numerical relativists developing and running codes to model black holes and other astrophysical objects. We describe a summary of previous tools developed within the collaboration, and how they are integrated and used with their simulation codes which are built using the Cactus framework. We also describe new Cactus tools which use the Twitter and Flickr services. These tools are fundamentally integrated with the code base as Cactus modules and provide reliable, real-time information about simulations that can be easily shared across a collaboration.

Multicluster grids provide one promising solution to satisfying growing computation demands of compute-intensive applications by collaborating various networked clusters. However, it is challenging to seamlessly integrate all participating clusters in different domains into a virtual computation platform. In order to take full advantages of multicluster grids capability, computer scientists need to deal with how to collaborate practically and efficiently participating autonomic systems to execute Grid-enabled applications. We make efforts on grid resource management and implement a toolkit called Pelecanus to improve the overall performance of application execution in multicluster grids environment. The Pelecanus takes advantages of the DA-TC (Dynamic Assignment with Task Containers) execution model to improve resource interoperability and enhance application execution and monitoring. Experiments show that it can significantly reduce turnaround time and increase resource utilization for certain applications with large number of sequential jobs.

Running applications on Supercomputers requires users to learn the low level details of how to interact with the resource manager and scheduler, write job submission scripts, submit and monitor jobs, transfer input and output files etc. These mechanisms differ on different machines and the scientist needs to waste precious time browsing websites and reading lengthy documentation. Even with tools such as Globus or Condor-G, submitting jobs requires users to learn the respective job description languages. This adds an unnecessary learning curve specially for users that want to use packages mostly pre-installed on compute resources and are interested only in the results. Our aim is to allow the user to focus on just the science of the simulation and the results and not the details of job submission and management. In order to do this we have designed a Simulation Application Manager(SAM) based on a Service Oriented Architecture(SOA) that abstracts out the details of the underlying execution environment and provides a mechanism to transfer the necessary science parameters from the user to the job. The choice of using an SOA also ensures that user interfaces to various applications can be quickly and easily generated with most of the code being generated automatically by modern day web services tools available in frameworks such as Axis and CXF.

We designed a semantic enabled metadata framework using ontology for multi-disciplinary and multi-institutional large scale scientific data sets in a Data Grid setting. There are two main issues we intend to address: data integration for semantically and physically heterogeneous distributed knowledge stores, and semantic reasoning for data verification and inference in such a setting. This framework enables data interoperability between otherwise semantically incompatible data sources, cross-domain query capabilities and multi-source knowledge extraction. In this paper, we present the basic system architecture for this framework, as well as an initial implementation.

Abstract The SURA Coastal Ocean Observing and Prediction (SCOOP) program is using geographical information system (GIS) technologies to visualize and integrate distributed data sources from across the United States and Canada. Hydrodynamic models are run at different sites on a developing multi‐institutional computational Grid. Some of these predictive simulations of storm surge and wind waves are triggered by tropical and subtropical cyclones in the Atlantic and the Gulf of Mexico. Model predictions and observational data need to be merged and visualized in a geospatial context for a variety of analyses and applications. A data archive at LSU aggregates the model outputs from multiple sources, and a data‐driven workflow triggers remotely performed conversion of a subset of model predictions to georeferenced data sets, which are then delivered to a Web Map Service located at Texas A&M University. Other nodes in the distributed system aggregate the observational data. This paper describes the use of GIS within the SCOOP program for the 2005 hurricane season, along with details of the data‐driven distributed dataflow and workflow, which results in geospatial products. We also focus on future plans related to the complimentary use of GIS and Grid technologies in the SCOOP program, through which we hope to provide a wider range of tools that can enhance the tools and capabilities of earth science research and hazard planning. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract Uncertainty analysis is critical for conducting reservoir performance prediction. However, it is challenging because it relies on (1) massive modeling‐related, geographically distributed, terabyte, or even petabyte scale data sets (geoscience and engineering data), (2) needs to rapidly perform hundreds or thousands of flow simulations, being identical runs with different models calculating the impacts of various uncertainty factors, (3) an integrated, secure, and easy‐to‐use problem‐solving toolkit to assist uncertainty analysis. We leverage Grid computing technologies to address these challenges. We design and implement an integrated problem‐solving environmentResGridto effectively improve reservoir uncertainty analysis. The ResGrid consists of data management, execution management, and a Grid portal. Data Grid tools, such as metadata, replica, and transfer services, are used to meet massive size and geographically distributed characteristics of data sets. Workflow, task farming, and resource allocation are used to support large‐scale computation. A Grid portal integrates the data management and the computation solution into a unified easy‐to‐use interface, enabling reservoir engineers to specify uncertainty factors of interest and perform large‐scale reservoir studies through a web browser. The ResGrid has been used in petroleum engineering. Copyright © 2008 John Wiley & Sons, Ltd.

The discovery and management of energy resources, especially at locations in the Gulf of Mexico, requires an economic but technically enhanced infrastructure. Research teams from Louisiana State University, University of Louisiana at Lafayette, and Southern University Baton Rouge are engaged in a collaborative effort to create a ubiquitous computing and monitoring system (UCoMS) for the discovery and management of energy resources. The UCoMS team has sucessfully addressed two difficult issues in this research: (1) the computational challenges faced by compute-intensive simulations for reservoir uncertainty analysis that requires thousands of simulations and deals with terabytes, and even petabytes, of data, (2) the development of a prototype wireless sensor network (WSN) infrastructure to collect and process realtime data from production locations. While the former requires the intensive computational power of the UCoMS grid resources, the latter requires efficient interfacing between WSN & grid. A unified workflow analysis has been performed to ensure smooth operation of both efforts and a unified portal has been created. This paper integrates the above two workflows and portals into a single platform. It illustrates the need for such integration for users with similar (but not same) goals and describes how to partition users among different groups with different access rights to ensure security within subgroups. Such a system can easily integrate future UCoMS sub-projects into a unified whole. Hence, our portal prototype serves as a good example of the benefit that may accrue from integrated workflows.

Abstract We describe the background, architecture and implementation of a user portal for the SCOOP coastal ocean observing and modeling community. SCOOP is engaged in the real‐time prediction of severe weather events, including tropical storms and hurricanes, and provides operational information including wind, storm surge and resulting inundation, which are important for emergency management. The SCOOP portal, built with the GridSphere Framework, currently integrates customized Grid portlet components for data access, job submission, resource management and notification. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract In this paper we discuss two of the major Grid portal solutions, the Open Grid Computing Environments Collaboratory (OGCE) and GridPortlets, both of which provide basic tools that portal developers can use to interact with Grid middleware when designing their own custom or application‐specific Grid portals. We investigate and compare what each of these packages provides, discuss their advantages and disadvantages, and identify missing features vital for Grid portal development. The main purpose of this paper is to identify what current toolkits provide, reveal some of their limitations, and provide motivation for the evolution of Grid portal solutions. Application groups should find this paper useful in helping to choose an appropriate Grid portal toolkit for building their Grid portals rapidly in a flexible and modular way. Copyright © 2007 John Wiley & Sons, Ltd.

Many efforts in Grid communities have focused on middleware research and development. However, Grid application-level tools are needed which can build higherlevel functionality on top of core middleware services. We work with specific classes of scientific applications and present a Grid-aware toolkit ResGrid for reservoir uncertainty analysis. With the help of the ResGrid, a reservoir engineer can transparently take advantage of Grid resources and services for compute-intensive and dataintensive uncertainty analysis as well as enforce the understanding of reservoir modeling. In this paper, the ResGrid is introduced in terms of overview, architecture, and implementation status.

The SURA Coastal Ocean Observing & Prediction (SCOOP) program is building cyberinfrastructure (CI) to enable advanced real-time ensemble forecasting of the coastal impacts from storms and hurricanes. This prototype of a reliable, flexible, grid-enabled forecast system integrates real-time distributed data and computer models for the coasts of the southeastern United States. The SCOOP system employs a service-oriented architecture with archive and transport services, metadata catalog, resource management, and portal interfaces. Currently, the SCOOP system uses distributed HPC machines (SCOOP, SURAgrid, others) to meet on-demand requirements. Geospatial web services disseminate the forecast results.We provide the architecture overview and describe the currently deployed system for Hurricane Season 2006 as an example in which a storm advisory automatically initiates a workflow that delivers timely forecasts. The system generates a wind-ensemble and then configures, deploys, and analyzes a variety of water level and wave models across distributed HPC resources to deliver timely forecasts.

In Grid computing multiple, incompatible middleware frameworks exist and are widely used in large research and production environments. Standard specifications for such Grid middleware are rarely available, and often still unstable. This hinders the ability of application programmers to write portable Grid application code.The Simple API for Grid Applications (SAGA) is an ongoing standardization effort within the Open Grid Forum (OGF). The SAGA API provides a simple, uniform interface for applications which utilize very dynamic and heterogeneous Grid environments. With SAGA, programmers of high-level Grid application do not have to learn about underlying Grid middleware layers and frameworks.Our newly developed SAGA C++ reference implementation makes this API available for real-world applications, providing a flexible, extensible, portable, and generic framework usable in dynamic environments.This poster describes the key features of SAGA, and provides examples of its use from the C++ reference implementation.

Reservoir studies are crucial to obtain accurate assessments and predictions of reservoir performance. However, this is a challenging issue because 1) it relies on massive modeling-related, geographically distributed, terabyte or even petabyte sized datasets (seismic and well-logging data), 2) needs to rapidly perform hundreds or thousands of simulations, being identical runs with different reservoir models circulating the impacts of various uncertainty factors, 3) the lack of easy-to-use problem solving toolkits to assist the uncertainty analysis.The poster focuses on leveraging Grid computing technologies to address the challenging issue mentioned above. It describes a newly developed data archive tool based on metadata and replica services and high performance file transfer. Our task farming framework enables a large amount of parallel job runs across a Grid, a related Grid portal eases the management of advanced reservoir studies. Our solutions are being employed by other Grid applications.

Core Grid technologies are rapidly maturing, but there remains a shortage of real Grid applications. One important reason is the lack of a simple and high-level application programming toolkit, bridging the gap between existing Grid middleware and application-level needs. The Grid Application Toolkit (GAT), as currently developed by the EC-funded project GridLab, provides this missing functionality. As seen from the application, the GAT provides a unified simple programming interface to the Grid infrastructure, tailored to the needs of Grid application programmers and users. A uniform programming interface will be needed for application developers to create a new generation of "Grid-aware" applications. The GAT implementation handles both the complexity and the variety of existing Grid middleware services via so-called adaptors. Complementing existing Grid middleware, GridLab also provides high-level services to implement the GAT functionality. We present the GridLab software architecture, consisting of the GAT, environment-specific adaptors, and GridLab services. We elaborate the concepts underlying the GAT and outline the corresponding application programming interface. We present the functionality of GridLab's high-level services and demonstrate how a dynamic Grid application can easily benefit from the GAT. All GridLab software is open source and can be downloaded from the project Web site.

Nomadic Migration describes a technology, which provides an application with the ability to seek out and exploit remote computing resources by migrating tasks from site to site, dynamically adapting the application to a changing Grid environment. By automating the detection and usage of free resources in a global Grid, we achieve a significantly faster throughput than by manually interfacing with these resources. In this Paper we discuss the Peer-To-Peer strategy as an approach to provide a fault tolerant service infrastructure, required for a stable Grid Migration Service in an intrinsically disruptive Grid environment. The migration technology presented here is e.g. used with large-scale, Cactus based HPC simulations.

We present a synopsis of the Grid Application Toolkit, under development in the EU GridLab project, along with some of the new application scenarios which it will enable.

Abstract We describe a Grid‐based collaboratory that supports the collaborative development and use of advanced simulation codes. Our implementation of this collaboratory uses a mix of Web technologies (for thin‐client access) and Grid services (for secure remote access to, and management of, distributed resources). Our collaboratory enables researchers in geographically disperse locations to share and access compute, storage, and code resources, without regard to institutional boundaries. Specialized services support community code development, via specialized Grid services, such as online code repositories. We use this framework to construct the Astrophysics Simulation Collaboratory, a domain‐specific collaboratory for the astrophysics simulation community. This Grid‐based collaboratory enables researchers in the field of numerical relativity to study astrophysical phenomena by using the Cactus computational toolkit. Copyright © 2002 John Wiley & Sons, Ltd.

The Testbed and Applications working group of the European Grid Forum (EGrid) is actively building and experimenting with a grid infrastructure connecting several research-based supercomputing sites located in Europe. The paper reports on our first feasibility study: running a self-migrating version of the Cactus simulation code across the European grid testbed, including "live" remote data visualization and steering from different demonstration booths at Supercomputing 2000, in Dallas, TX. We report on the problems that had to be resolved for this endeavour and identify open research challenges for building production-grade grid environments.

We discuss a set of novel techniques we are developing, which build on standard tools, to make distributed computing for large-scale simulations across multiple machines (even scattered across different continents) a reality. With these techniques we demonstrate that we are able to scale a tightly coupled scientific application in metacomputing environments. Such research and development in metacomputing will lead the way to routine, straightforward and efficient use of distributed computing resources anywhere around the world. This work applies not only to the large-scale simulations in astrophysics which provide the motivation for this work, but also opens the way for new, innovative application scenarios.

We describe the design and implementation of a technology which provides an application with the ability to seek out and exploit remote computing resources by migrating tasks from site to site, dynamically adapting the application to a changing Grid environment. The motivation for this migration framework, dubbed "The Worm", originated from the experience of having an abundance of computing time for simulations, which is distributed over multiple sites and split in time chunks by queuing systems. We describe the architecture of the Worm, describing how new or more suitable resources are located, and how the payload simulation is migrated to these resources following a trigger event. The migration technology presented here is designed to be used for any application, including large-scale HPC simulations.

Grid Portals, based on standard Web technologies, are emerging as important and useful user interfaces to computational and data grids. Grid portals enable virtual organizations, comprised of distributed researchers to collaborate and access resources more efficiently and seamlessly. The Astrophysics Simulation Collaboratory (ASC) Grid Portal provides a framework to enable researchers in the field of numerical relativity to study astrophysical phenomenon by making use of the Cactus computational toolkit. We examine user requirements and describe the design and implementation of the ASC Grid Portal.

Improvements in the performance of processors and networks make it both feasible and interesting to treat collections of workstations, servers, clusters, and supercomputers as integrated computational resources, or Grids. However, the highly heterogeneous and dynamic nature of such Grids can make application development difficult. Here we describe an architecture and prototype implementation for a Grid-enabled computational framework based on Cactus, the MPICH-G2 Grid-enabled message-passing library, and a variety of specialized features to support efficient execution in Grid environments. We have used this framework to perform record-setting computations in numerical relativity, running across four supercomputers and achieving scaling of 88% (1140 CPU's) and 63% (1500 CPUs). The problem size we were able to compute was about five times larger than any other previous run. Further, we introduce and demonstrate adaptive methods that automatically adjust computational parameters during run time, to increase dramatically the efficiency of a distributed Grid simulation, without modification of the application and without any knowledge of the underlying network connecting the distributed computers.

Cactus is an open source problem solving environment designed for scientists and engineers. Its modular structure facilitates parallel computation across different architectures and collaborative code development between different groups. The Cactus Code originated in the academic research community, where it has been developed and used over many years by a large international collaboration of physicists and computational scientists. We discuss how the intensive computing requirements of physics applications now using the Cactus Code encourage the use of distributed and metacomputing, describe the development and experiments which have already been performed with Cactus, and detail how its design makes it an ideal application test-bed for Grid computing.

We are developing a system for collaborative research and development for a distributed group of researchers at different institutions around the world. In a new paradigm for collaborative computational science, the computer code and supporting infrastructure itself becomes the collaborating instrument, just as an accelerator becomes the collaborating tool for large numbers of distributed researchers in particle physics. The design of this "collaboratory" allows many users, with very different areas of expertise, to work coherently together, on distributed computers around the world. Different supercomputers may be used separately, or for problems exceeding the capacity of any single system, multiple supercomputers may be networked together through high speed gigabit networks. Central to this collaboratory is a new type of community simulation code, called "Cactus". The scientific driving force behind this project is the simulation of Einstein's equations for studying black holes, gravitational waves, and neutron stars, which has brought together researchers in very different fields from many groups around the world to make advances in the study of relativity and astrophysics. But the system is also being developed to provide scientists and engineers, without expert knowledge of parallel or distributed computing, mesh refinement, and so on, with a simple framework for solving any system of partial differential equations on many parallel computer systems, from traditional supercomputers to networks of workstations.