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Over recent years’ large-scale distributed synthetic environment enterprises have been evolving in a diverse range of scientific and engineering fields. These computer modelling and simulation systems are increasing in scale and dimension in order to allow scientists and engineers to explore the attributes and emergent properties of a given system design. Within the field of computational science, the grid facilitates very large-scale collaborative simulation enterprises. The grid is similar to distributed interactive simulation/high level architecture (DIS/HLA) in that it supports interconnectivity but differs in the sense that it supports intercommunication of large super computing resources. An important factor in the rapid adoption of the grid has been its role in enabling access to significant supercomputing resources not usually available at a single institution. However, the major challenge for the grid has been the lack of an effective and ubiquitous interface to the huge computational resource (which can comprise over 6000 CPUs distributed across the globe) at any time and from any location. This paper describes a unique user interface built on systems engineering principles and practices to solve the problem of delivering real-time interaction (from lightweight computing devices such as personal digital assistants, commonly known as tablet devices, to high end computing platforms) with simulations delivering high resolution 3D images. The application of our work has far reaching benefits for many sectors including: aerospace, medical informatics, engineering design, distributed simulation, and modelling.

The Evaluating and Enhancing the eXtreme Digital Cyberinfrastructure for Maximum Usability and Science Impact project, known as the Technology Investigation Service (TIS), was a collaboration between University of Illinois at Urbana-Champaign National Center for Supercomputing Applications, Pittsburgh Supercomputing Center, The University of Texas at Austin Texas Advanced Computing Center, University of Tennessee National Institute for Computational Sciences, and University of Virginia which identified and evaluated potential technologies to close the gap between the XSEDE (http://www.xsede.org) service offerings and the needs of XSEDE users. This project was funded by the NSF Division of Advanced Cyberinfrastructure (award ACI 09-46505) in response to the "Technology Audit and Insertion Service" component of the "TeraGrid Phase III: eXtreme Digital Resources for Science and Engineering (XD)" solicitation (NSF 08-571). Over the project lifetime the two major goals of TIS were: 1) identifying, tracking, evaluating and making recommendations of new technologies to XSEDE for consideration of adoption and 2) raising awareness of TIS to XSEDE and other stakeholders to solicit their input on technologies for consideration for evaluation. In accomplishing the goals, the following four significant outcomes from TIS resulted: the development and deployment of the XSEDE Technology Evaluation Database; the development of the significantly improved XSEDE software search capability; the technology evaluation process; and the evaluations performed along with their corresponding technology adoption recommendations to XSEDE. This paper highlights the life-cycle of the TIS project, including lessons learned and project outcomes.

MapReduce is increasingly becoming a popular programming model. However, the widely used implementation, Apache Hadoop, uses the Hadoop Distributed File System (HDFS), which is currently not directly applicable to a majority of existing HPC environments such as Teragrid and NERSC that support other distributed file systems. On such resourceful High Performance Computing (HPC) infrastructures, the MapReduce model can rarely make use of full resources, as special circumstances must be created for its adoption, or simply limited resources must be isolated to the same end. This paper not only presents a MapReduce implementation directly suitable for such environments, but also exposes the design choices for better performance gains in those settings. By leveraging inherent distributed file systems’ functions, and abstracting them away from its MapReduce framework, MARIANE (MApReduce Implementation Adapted for HPC Environments) not only allows for the use of the model in an expanding number of HPC environments, but also shows better performance in such settings. This paper identifies the components and trade-offs necessary for this model, and quantifies the performance gains exhibited by our approach in HPC environments over Apache Hadoop in a data intensive setting at the National Energy Research Scientific Computing Center (NERSC).

The UltraScan data analysis application is a software package that is able to take advantage of computational resources in order to support the interpretation of analytical ultracentrifugation experiments. Since 2006, the UltraScan scientific gateway has been used with Web browsers in TeraGrid by scientists studying the solution properties of biological and synthetic molecules. UltraScan supports its users with a scientific gateway in order to leverage the power of supercomputing. In this contribution, we will focus on several advancements of the UltraScan scientific gateway architecture with a standardized job management while retaining its lightweight design and end user interaction experience. This paper also presents insights into a production deployment of UltraScan in Europe. The approach is based on open standards with respect to job management and submissions to the Extreme Science and Engineering Discovery Environment in the USA and to similar infrastructures in Europe such as the European Grid Infrastructure or the Partnership for Advanced Computing in Europe PRACE. Our implementation takes advantage of the Apache Airavata framework for scientific gateways that lays the foundation for easy integration into several other scientific gateways. Copyright © 2014 John Wiley & Sons, Ltd.

In this investigation, we examine how the precision of energy spectra and scattering phase shifts, extracted in lattice QCD, depend upon the degree of distillation type smearing. We use the variational method to extract energy spectra for the isospin-1, $J^{PC}=1^{--}$ channel and use the L\"{u}scher method to compute scattering amplitudes, relevant for the $\rho$ resonance, in $\pi\pi$ elastic scattering. Optimised interpolating operators for a single ground state pion are constructed and these are used to construct two pion operators. Calculations are performed on an anisotropic lattice with a pion mass of $m_\pi=236$ MeV. We provide a comprehensive comparison of energy spectra and scattering phase shifts across distillation spaces of varying rank., Comment: 7 pages, 2 figures, Proceedings of Lattice 2016 (34th Annual International Symposium on Lattice Field Theory)

We examine the evolution of one NSF-funded cyberinfrastructure organization, the TeraGrid, into its successor organization, the Extreme Science and Engineering Discovery Environment (XSEDE) via a case study methodology with two different frames of reference: virtual organization literature and public management networks theory. Our aim in examining these two projects is to situate the study of cyberinfrastructure in support of basic science in both the virtual organization and public management networks literature. We find that our cases confirm the utility of hierarchy when dealing with complexity and that expert use of ICT for governance tasks improves network capacity, especially when time-tested technologies are available in the environment. These ICT resources can be used for monitoring of both service delivery functions as well as network health. We also note that the establishment of common inter-organizational standards facilitates interoperability between organizations, allowing sustainability and modularity of network members.

It is generally accepted that the ability to develop large-scale distributed applications has lagged seriously behind other developments in cyberinfrastructure. In this paper, we provide insight into how such applications have been developed and an understanding of why developing applications for distributed infrastructure is hard. Our approach is unique in the sense that it is centered around half a dozen existing scientific applications; we posit that these scientific applications are representative of the characteristics, requirements, as well as the challenges of the bulk of current distributed applications on production cyberinfrastructure (such as the US TeraGrid). We provide a novel and comprehensive analysis of such distributed scientific applications. Specifically, we survey existing models and methods for large-scale distributed applications and identify commonalities, recurring structures, patterns and abstractions. We find that there are many ad hoc solutions employed to develop and execute distributed applications, which result in a lack of generality and the inability of distributed applications to be extensible and independent of infrastructure details. In our analysis, we introduce the notion of application vectors: a novel way of understanding the structure of distributed applications. Important contributions of this paper include identifying patterns that are derived from a wide range of real distributed applications, as well as an integrated approach to analyzing applications, programming systems and patterns, resulting in the ability to provide a critical assessment of the current practice of developing, deploying and executing distributed applications. Gaps and omissions in the state of the art are identified, and directions for future research are outlined.

Grid Computing proposes unlimited access to different computational resources in a transparent way. High performance execution in grid environments is virtually impossible without timely access to accurate and up-to-date information related to distributed resources and services. Due to inherent difficulty of testing the different information propagation policies in real grid infrastructures, several simulation frameworks arose to help in this issue. In this work, we present Grid Matrix, an extension to one of the most used grid simulation tools (SimGrid2) to focus on the propagation of monitoring and resource information allowing the creation of virtual grid infrastructures. This extension enables GUI editing of network topology and provides the feature of scripting to define simulation details based on the newly developed C++ and Python bindings of SimGrid2 API. As a case study, Grid Matrix was used to test four different policies: hierarchical, super-peer, best-neighbor and random. The simulated scenario consisted of 96 master nodes based on the real Teragrid infrastructure as was publicly available at the time of writing this paper. We introduce three metrics that capture and summarize the information propagation behavior: LIR, GIR and GIV. LIR captures the local behavior quantifying the amount of up-to-date information in each node. GIR evaluates the global information state in the whole network averaging the LIR values, while GIV measures the variability of LIR. In the presented case, the best results in terms of the proposed metrics were attained by the hierarchical policy, followed by super-peer which outperformed random and best-neighbor. The modern and modular design of the scripting features included in Grid Matrix, in close conjunction with the user friendly GUI happened to be a very powerful tool for the evaluation of new propagation policies of resource information.

"Give a man a fish, feed him for a day. Teach a man to fish, feed him for a lifetime"--Lau Tzu. Large-scale grid computing projects such as TeraGrid and Open Science Grid provide researchers vast amounts of compute resources but with requirements that could limit access, delay results due to potentially long job queues, and involve environments and policies that might affect a user's work flow. In many scenarios and in particular with the advent of Infrastructure-as-a-Service (IaaS) cloud computing, individual users and communities can benefit from less restrictive, dynamic systems that include a combination of local resources and on-demand resources provisioned by one or more IaaS provider. These types of scenarios benefit from flexibility in deploying resources, remote access, and environment configuration. In this paper, we address how small groups can dynamically create, join, and manage grid infrastructures with low administrative overhead. Our work distinguishes itself from other projects with similar objectives by enabling a combination of decentralized system organization and user access for job submission in addition to a web 2.0 interfaces for managing grid membership and automate certificate management. These components contribute to the design of the "Grid Appliance," an implementation of a wide area overlay network of virtual workstations (WOW), which has developed over the past six years into a mature system with several deployments and many users. In addition to an architectural description, this paper contains lessons learned during the development and deployment of "Grid Appliance" systems and a case study backed by quantitative analysis that verifies the utility of our approach.

The Software as a Service (SaaS) methodology is a key paradigm of Cloud computing. In this paper, we focus on an interesting topic--to dynamically host services on existing production Grid infrastructures. In general, production Grids normally employ a Job-Submission-Execution (JSE) model with rigid access interfaces. In this paper, we implement the Cyberaide onServe, a lightweight middleware with a virtual appliance. The Cyberaide onServe implements the SaaS model on production Grids by translating the SaaS model to the JSE model. The Cyberaide onServe can be deployed on demand in a virtual appliance, host users' software as a Web service, accept Web service invocations; finally, the Cyberaide onServe can execute them on production Grids. We have deployed the Cyberaide onServe on the TeraGrid and the test results show that the Cyberaide onServe can provide SaaS functionalities with a good performance.

Models are increasingly being used to quantify the effects of best management practices (BMPs) on water quality. While these models offer the ability to study multiple BMP scenarios, and to analyze impacts of various management decisions on watershed response, associated analyses can be very computationally intensive due to a large number of runs needed to fully capture the various uncertainties in the model outputs. There is, thus, the need to develop suitable and efficient techniques to handle such comprehensive model evaluations. We demonstrate a novel approach to accomplish a large number of model runs with Condor, a distributed high-throughput computing framework for model runs with the Soil and Water Assessment Tool (SWAT) model. This application required more than 43,000 runs of the SWAT model to evaluate the impacts of 172 different watershed management decisions combined with weather uncertainty on water quality. The SWAT model was run in the Condor environment implemented on the TeraGrid. This framework significantly reduced the model run time from 2.5 years to 18 days and enabled us to perform comprehensive BMP analyses that may not have been possible with traditional model runs on a few desktop computers. The Condor system can be used effectively to make Monte Carlo analyses of complex watershed models requiring a large number of computational cycles.

In this paper, we propose resource provisioning as the means to reduce the completion time of scientific workflows in a Grid environment. We propose task clustering as a form of workflow optimization that can be used along with provisioning in order to achieve this reduction in completion time. Provisioning can be done statically using advance reservations (ARs) or using dynamic provisioning mechanisms. A simulation is done using the Maui simulator, a workload trace collected from the NCSA Teragrid cluster and 13 workflows to study the effect of provisioning on the completion time of the scientific workflows. The results show in general a reduction of about 50% in the workflow completion time using provisioning for the First In First Out and fair share scheduling policies. In this paper, we also examine the cost of resource provisioning and propose a utilization-based metric that can be used to guide the provisioning decisions in order to reduce the cost. Finally, we present the results of a survey on the support of AR at the Grid sites. Copyright © 2011 John Wiley & Sons, Ltd.

Deadline-sensitive workflows require careful coordination of user constraints with resource availability. Current distributed resource access models provide varying degrees of resource control: from limited or none in grid batch systems to explicit in cloud systems. Additionally applications experience variability due to competing user loads, performance variations, failures, etc. These variations impact the quality of service (QoS) that goes unaccounted for in planning strategies. In this paper we propose Workflow ORchestrator for Distributed Systems (WORDS) architecture based on a least common denominator resource model that abstracts the differences and captures the QoS properties provided by grid and cloud systems. We investigate algorithms for effective orchestration (i.e., resource procurement and task mapping) for deadline-sensitive workflows atop the resource abstraction provided in WORDS. Our evaluation compares orchestration methodologies over TeraGrid and Amazon EC2 systems. Experimental results show that WORDS enables effective orchestration possible at reasonable costs on batch queue grid and cloud systems with or without explicit resource control.

We investigate how the benefits of the TeraGrid supercomputing infrastructure are distributed across the scientific community. Do mostly high-impact scientists benefit from the TeraGrid? Are some scientific domains more strongly represented than others in TeraGrid-supported work? To answer these questions, we examine the relation between TeraGrid usage and scientific impact for a set of scientists whose projects relied to varying degrees on the TeraGrid infrastructure. For each scientist we measure TeraGrid usage expressed in terms of allocated Service Units (SU) vs. various indicators of their scientific impact such as the h-index, total citations, and citations per article. Our results show a significant correlation between scientific impact and TeraGrid usage. We furthermore examine the distribution of TeraGrid-related publications across various scientific journals. A superposition of these journals over an existing large-scale map of science shows how TeraGrid-supported work is mostly concentrated in Physics and Chemistry, with a lesser focus on biology.

In this paper, we present a programming and runtime framework that enables the autonomic management of complex application workflows on hybrid computing infrastructures. The framework is designed to address system and application heterogeneity and dynamics to ensure that application objectives and constraints are satisfied. The need for such autonomic system and application management is becoming critical as computing infrastructures become increasingly heterogeneous, integrating different classes of resources from high-end HPC systems to commodity clusters and clouds. For example, the framework presented in this paper can be used to provision the appropriate mix of resources based on application requirements and constraints. The framework also monitors the system/application state and adapts the application and/or resources to respond to changing requirements or environment. To demonstrate the operation of the framework and to evaluate its ability, we employ a workflow used to characterize an oil reservoir executing on a hybrid infrastructure composed of TeraGrid nodes and Amazon EC2 instances of various types. Specifically, we show how different applications objectives such as acceleration, conservation and resilience can be effectively achieved while satisfying deadline and budget constraints, using an appropriate mix of dynamically provisioned resources. Our evaluations also demonstrate that public clouds can be used to complement and reinforce the scheduling and usage of traditional high performance computing infrastructure.

Many engineering and environmental problems that involve the determination of unknown system characteristics from observation data can be categorized as inverse problems. A common approach undertaken to solve such problems is the simulation-optimization approach where simulation models are coupled with optimization or search methods. Simulation-optimization approaches, particularly in environmental characterization involving natural systems, are computationally expensive due to the complex three-dimensional simulation models required to represent these systems and the large number of such simulations involved. Emerging grid computing environments (e.g., TeraGrid) show promise for improving the computational tractability of these approaches. However, harnessing grid resources for most computational applications is a nontrivial problem due to the complex hierarchy of heterogeneous and geographically distributed resources involved in a grid. This paper reports and discusses the development and evaluation of ...

Since its conception, Grid technology concerned itself with interoperability between heterogeneous computers by providing a middleware layer to abstract underlying resource characteristics. Unfortunately there is no agreement today on a common set of standards and, therefore, different conceptions lead to different middleware implementations. That effectively rendered interoperability between grid infrastructures to be a complex issue, from which originally sprang the idea of Grid. In this paper we present technologies to achieve interoperation (i.e. interoperability not based on standards) between sites and show its feasibility, aiming to provide a mid-term solution to the federation problem until the promise of interoperability through standards becomes a reality. Using interoperation techniques and bringing them together in a common federation component, the GridWay metascheduler, we are able to offer common access to well known grid infrastructures. This approach is demonstrated in the performance evaluation of the execution of one benchmark of the NAS Grid Benchmark suite.

Many Minority Serving Institutions (MSI) in the United States lack sufficient resources and knowledge to take significant advantage of the national and international computational resources available to advance computational science research and education. National challenges raised for Homeland Security (HS) have focused US government attention on the critical yet untapped human resource potential at MSIs. The US National Science Foundation’s TeraGrid has attempted to define, promote and deliver an integrated set of high performance computing resources to the national academic research community. However, a gap persists in the connections between the TeraGrid program and non-TeraGrid national computational resources and data. For MSIs and other users, these circumstances hinder seamless, natural use of resources from local, campus infrastructure through national high performance computing research tools. In spite of impressive TeraGrid advances, its user community falls short of engaging a much broader potential community especially the MSI community. A campus-based integrated CyberInfrastructure (CI) is essential to bridge this gap.

Cyberinfrastructure science and engineering gateways have become an important modality to connect science and engineering communities and cyberinfrastructure. The use of cyberinfrastructure through gateways is fundamental to the advancement of science and engineering. However, learning science gateway technologies and developing science gateways remain a significant challenge, given that science gateway technologies are still actively evolving and often include a number of sophisticated components. A geosciences gateway must be designed to accommodate legacy methods that geoscientists use in conventional computational tools. The research described in this paper establishes an open-source toolkit-SimpleGrid for learning and developing science gateways based on a service-oriented architecture using a component-based approach that allows flexible separation and integration of the components between geocomputation applications and cyberinfrastructure. The design and implementation of SimpleGrid is based on the National Science Foundation TeraGrid-a key element of the U.S. and world cyberinfrastructure. This paper illustrates our experience of using SimpleGrid and a spatial interpolation method in a tutorial to teach TeraGrid science gateways.

BioDrugScreen is a resource for ranking molecules docked against a large number of targets in the human proteome. Nearly 1600 molecules from the freely available NCI diversity set were docked onto 1926 cavities identified on 1589 human targets resulting in >3 million receptor–ligand complexes requiring >200 000 cpu-hours on the TeraGrid. The targets in BioDrugScreen originated from Human Cancer Protein Interaction Network, which we have updated, as well as the Human Druggable Proteome, which we have created for the purpose of this effort. This makes the BioDrugScreen resource highly valuable in drug discovery. The receptor–ligand complexes within the database can be ranked using standard and well-established scoring functions like AutoDock, DockScore, ChemScore, X-Score, GoldScore, DFIRE and PMF. In addition, we have scored the complexes with more intensive GBSA and PBSA approaches requiring an additional 120 000 cpu-hours on the TeraGrid. We constructed a simple interface to enable users to view top-ranking molecules and access purchasing and other information for further experimental exploration.

This paper describes experiences developing a grid-enabled framework for solving environmental inverse problems. The solution approach taken here couples environmental simulation models with global search methods and requires readily available computational resources of the grid for computational tractability. The solution framework developed by the authors uses a master—worker strategy for task distribution and a pool for task mapping. Solution and computational performance results are presented for groundwater source identification and release history reconstruction problems. They indicate that high-quality solutions and significant raw performance improvements were attained for a deployment of the solution framework on the TeraGrid.

Groundwater characterization involves the resolution of unknown system characteristics from observation data, and is often classified as an inverse problem. Inverse problems are difficult to solve due to natural ill-posedness and computational intractability. Here we adopt the use of a simulation–optimization approach that couples a numerical pollutant-transport simulation model with evolutionary search algorithms for solution of the inverse problem. In this approach, the numerical transport model is solved iteratively during the evolutionary search. This process can be computationally intensive since several hundreds to thousands of forward model evaluations are typically required for solution. Given the potential computational intractability of such a simulation–optimization approach, parallel computation is employed to ease and enable the solution of such problems. In this paper, several variations of a groundwater source identification problem is examined in terms of solution quality and computational performance. The computational experiments were performed on the TeraGrid cluster available at the National Center for Supercomputing Applications. The results demonstrate the performance of the parallel simulation–optimization approach in terms of solution quality and computational performance.

We describe computational science research that uses petascale resources to achieve scientific results at unprecedented scales and resolution. The applications span a wide range of domains, from investigation of fundamental problems in turbulence through computational materials science research to biomedical applications at the forefront of HIV/AIDS research and cerebrovascular haemodynamics. This work was mainly performed on the US TeraGrid ‘petascale’ resource, Ranger, at Texas Advanced Computing Center, in the first half of 2008 when it was the largest computing system in the world available for open scientific research. We have sought to use this petascale supercomputer optimally across application domains and scales, exploiting the excellent parallel scaling performance found on up to at least 32 768 cores for certain of our codes in the so-called ‘capability computing’ category as well as high-throughput intermediate-scale jobs for ensemble simulations in the 32–512 core range. Furthermore, this activity provides evidence that conventional parallel programming with MPI should be successful at the petascale in the short to medium term. We also report on the parallel performance of some of our codes on up to 65 636 cores on the IBM Blue Gene/P system at the Argonne Leadership Computing Facility, which has recently been named the fastest supercomputer in the world for open science.

Geospatial cyberinfrastructure integrates distributed geographic information processing (DGIP) technology, high-performance computing resources, interoperable Web services, and sharable geographic knowledge to facilitate the advancement of geographic information science (GIScience) research, geospatial technology, and geographic education. This article addresses three major development issues of geospatial cyberinfrastructure: the performance of grid-enabled DGIP services, the integration of Internet GIService resources, and the technical challenges of spatial Web portal implementation. A four-tier grid-enabled Internet GIService framework was designed for geospatial cyberinfrastructure. The advantages of the grid-enabled framework were demonstrated by a spatial Web portal. The spatial Web portal was implemented based on current available Internet technologies and utilizes multiple computing resources and high-performance systems, including local PC clusters and the TeraGrid. By comparing their performance testing results, we found that grid computing (TeraGrid) is more powerful and flexible than local PC clusters. However, job queuing time and relatively poor performance of cross-site computation are the major obstacles of grid computing for geospatial cyberinfrastructure. Detailed analysis of different computational settings and performance testing contributes to a deeper understanding of the improvements of DGIP services and geospatial cyberinfrastructure. This research demonstrates that resource/service integration and performance improvement can be accomplished by deploying the new four-tier grid-enabled Internet GIService framework. This article also identifies four research priorities for developing geospatial cyberinfrastructure: the design of GIS middleware, high-performance geovisualization methods, semantic GIService, and the integration of multiple GIS grid applications.

Cyberinfrastructure (CI) represents the integrated information and communication technologies for distributed information processing and coordinated knowledge discovery, and is promising to revolutionize how science and engineering are conducted in the twenty-first century. The value of bridging CI and GIScience is significant to advance CI and benefit GIScience research and education, particularly in distributed geographic information processing (DGIP). This article presents a holistic framework that bridges CI and GIScience by integrating CI capabilities to empower GIScience research and education and establish generic DGIP services supported by CI. The framework, the TeraGrid GIScience Gateway, is based on a CI science gateway approach developed on the National Science Foundation (NSF) TeraGrid-a key element of US and world CI. This gateway develops a unifying service-oriented framework with respect to its architecture, design, and implementation as well as its integration with the TeraGrid. The functions of the gateway focus on enabling parallel and distributed processing for geographical analysis, managing the complexity of TeraGrid software environment, and establishing a Web-based GIS for the GIScience community to gain shared and collaborative access to TeraGrid-based geospatial processing services. The gateway implementation uses Web 2.0 technologies to create a highly configurable and interactive multiuser environment. Two case studies, Bayesian geostatistical modeling and a spatial statistic [image omitted] for detecting local clustering, are used to demonstrate the gateway functions and user environment. The service transformation for these analyses is applied to create a shared, decentralized, and collaborative geographical analysis environment in which GIScience community users can contribute new analysis services and reuse existing gateway services.

Holistic approaches are needed for understanding and addressing a wide range of environmental issues that require multidis- ciplinary studies of complex and interlocking systems. The writers' vision of a cyberinfrastructure for end-to-end environmental explo- ration C4E4 that combines data and modeling tools in an integrated environment across different spatial and temporal scales is presented. The overall goal behind C4E4 is to enable a broad environmental research and remediation community to address the challenges of environmental data management and integration in real-world settings. The St. Joseph Watershed in northern Indiana is chosen as a test bed in this effort. The C4E4 framework will allow researchers to combine heterogeneous data resources with state-of- the-art modeling and visualization tools through a user-friendly web portal. By engaging TeraGrid resources, C4E4 will have the computational resources to store, manipulate, and query large data sets, thereby facilitating new science. C4E4 will serve as a prototype, and provide valuable experience for scaling up to larger observatories at the national level. This paper presents the writers' vision and goals, initial efforts, and briefly describes how C4E4 can benefit the environmental community.

Funded by the National Science Foundation (NSF), TeraGrid is one of the world's largest distributed cyberinfrastructures for open scientific research. The project began in 2001 as the Distributed Tera-scale Facility, which linked computers, visualization systems, and data at four sites through a dedicated 40-gigabit optical network. Today TeraGrid includes 25 platforms at 11 sites and provides access to more than a petaflop of computing power and petabytes of storage. TeraGrid has three primary focus areas. Its deep goal is to support the most challenging computational science activities those that cannot be achieved without TeraGrid facilities. TeraGrid's wide mission is to broaden its user base. The project's open goal is to achieve compatibility with peer grids and information services that allow development of programmatic interfaces to TeraGrid. The Science Gateways program seeks to provide researchers with easy access to TeraGrid's high-performance computing resources. A look at four successful gateways illustrates the program's goals, challenges, and opportunities.

We present a de novo hierarchical simulation framework for first-principles based predictive simulations of materials and their validation on high-end parallel supercomputers and geographically distributed clusters. In this framework, high-end chemically reactive and non-reactive molecular dynamics (MD) simulations explore a wide solution space to discover microscopic mechanisms that govern macroscopic material properties, into which highly accurate quantum mechanical (QM) simulations are embedded to validate the discovered mechanisms and quantify the uncertainty of the solution. The framework includes an embedded divide-and-conquer (EDC) algorithmic framework for the design of linear-scaling simulation algorithms with minimal bandwidth complexity and tight error control. The EDC framework also enables adaptive hierarchical simulation with automated model transitioning assisted by graph-based event tracking. A tunable hierarchical cellular decomposition parallelization framework then maps the O( N) EDC algorithms onto petaflops computers, while achieving performance tunability through a hierarchy of parameterized cell data/ computation structures, as well as its implementation using hybrid grid remote procedure call + message passing + threads programming. High-end computing platforms such as IBM BlueGene/L, SGI Altix 3000 and the NSF TeraGrid provide an excellent test grounds for the framework. On these platforms, we have achieved unprecedented scales of quantum-mechanically accurate and well validated, chemically reactive atomistic simulations—1.06 billion-atom fast reactive force-field MD and 11.8 million-atom (1.04 trillion grid points) quantum-mechanical MD in the framework of the EDC density functional theory on adaptive multigrids— in addition to 134 billion-atom non-reactive space—time multiresolution MD, with the parallel efficiency as high as 0.998 on 65,536 dual-processor BlueGene/L nodes. We have also achieved an automated execution of hierarchical QM/MD simulation on a grid consisting of 6 supercomputer centers in the US and Japan (in total of 150,000 processor hours), in which the number of processors change dynamically on demand and resources are allocated and migrated dynamically in response to faults. Furthermore, performance portability has been demonstrated on a wide range of platforms such as BlueGene/L, Altix 3000, and AMD Opteron-based Linux clusters.

Grid computing, in which a network of computers is integrated to create a very fast virtual computer, is becoming ever more prevalent. Examples include the TeraGrid and Planet-lab.org, as well as applications on the existing Internet that take advantage of unused computing and storage capacity of idle desktop machines, such as Kazaa, SETI@home, Climateprediction.net, and Einstein@home. Grid computing permits a network of computers to act as a very fast virtual computer. With many alternative computers available, each with varying extra capacity, and each of which may connect or disconnect from the grid at any time, it may make sense to send the same task to more than one computer. The application can then use the output of whichever computer finishes the task first. Thus, the important issue of the dynamic assignment of tasks to individual computers is complicated in grid computing by the option of assigning multiple copies of the same task to different computers. We show that under fairly mild and often reasonable conditions, maximizing task replication stochastically maximizes the number of task completions by any time. That is, it is better to do the same task on as many computers as possible, rather than assigning different tasks to individual computers. We show maximal task replication is optimal when tasks have identical size and processing times have a NWU (New Worse than Used; defined later) distribution. Computers may be heterogeneous and their speeds may vary randomly, as is the case in grid computing environments. We also show that maximal task replication, along with a c μ rule, stochastically maximizes the successful task completion process when task processing times are exponential and depend on both the task and computer, and tasks have different probabilities of completing successfully.

We describe a system for creating personal clusters in user-space to support the submission and management of thousands of compute-intensive serial jobs to the network-connected compute resources on the NSF TeraGrid. The system implements a robust infrastructure that submits and manages job proxies across a distributed computing environment. These job proxies contribute resources to personal clusters created dynamically for a user on-demand. The personal clusters then adapt to the prevailing job load conditions at the distributed sites by migrating job proxies to sites expected to provide resources more quickly. Furthermore, the system allows multiple instances of these personal clusters to be created as containers for individual scientific experiments, allowing the submission environment to be customized for each instance. The version of the system described in this paper allows users to build large personal Condor and Sun Grid Engine clusters on the TeraGrid. Users then manage their scientific jobs, within each personal cluster, with a single uniform interface using the feature-rich functionality found in these job management environments.

Grid-computing environments are becoming increasingly popular for scientific computing due to the significant increase in capacity that they represent when compared to a single computational resource (e.g., a single cluster), their ubiquitous availability and advances in grid middleware components. Several such specialized grid environments are now available for users in the commercial and research sectors. One such effort for research is the TeraGrid, consisting of a collection of geographically distributed heterogeneous supercomputer resources including data storage resources. Parallel implementations for these environments are inherently multilevel and obtaining efficient mapping of work to processors can be eXtremely challenging. This paper eXtends an eXisting MPI application to the grid via the use of grid-enabled MPI libraries. The application uses a simulation-optimization framework involving coarse-grained parallelism in the optimizer and fine-grained parallelism in the finite-element-based simulator. Using parallelism at both these levels is essential for problems involving computationally intensive simulation steps. A hierarchical grid architecture consisting of a collection of supercomputers is ideally suited for these types of problems as a good application-to-architecture mapping can be obtained with a proper implementation. This paper presents the performance results of our implementation on the TeraGrid network consisting of three geographically distributed supercomputers.

Visualization is an important part of the data analysis process. Many researchers, however, do not have access to the resources required to do visualization effectively for large datasets. This problem is illustrated through several user scenarios. To remedy this problem, we propose a Visualization Gateway that provides simplified access to such resources to a broad population of users. The current implementation of this gateway is described, including the technology used and the services made available. In particular, a detailed description of a ParaView portlet is included. A proposed design for enabling access to community users is discussed. Technology as well as policy issues that were raised, including security and data management, are covered, as are methods for providing additional services, scaling to include additional resources, and other areas of future development. The paper concludes with a summary of the topics covered. Copyright © 2006 John Wiley & Sons, Ltd.

The Linked Environments for Atmospheric Discovery (LEAD) Portal is a science application portal designed to enable effective use of Grid resources in exploring mesoscale meteorological phenomena. The aim of the LEAD Portal is to provide a more productive interface for doing experimental work by the meteorological research community, as well as bringing weather research to a wider class of users, meaning pre-college students in grades 6–12 and undergraduate college students. In this paper, we give an overview of the LEAD project and the role that LEAD portal is playing in reaching its goals. We then describe the various technologies we are using to bring powerful and complex scientific tools to educational and research users. These technologies—a fine-grained capability based authorization framework, an application service factory toolkit, and a Web services-based workflow execution engine and supporting tools—enable our team to deploy these once inaccessible, stovepipe scientific codes onto a Grid where they can be collectively utilized. Copyright © 2006 John Wiley & Sons, Ltd.

The National Science Foundation's Extensible Terascale Facility (ETF), or TeraGrid (http://www.teragrid.org/), is entering its operational phase. An example of an ETF science gateway effort is the Neutron Science TeraGrid Gateway (NSTG). The Oak Ridge National Laboratory (ORNL) resource provider effort (ORNL-RP) now in operation is bridging the gap between a large-scale experimental community and the TeraGrid as a large-scale national cyberinfrastructure. Of particular importance here is the collaboration with the Spallation Neutron Source (SNS) at ORNL. The U.S. Department of Energy's SNS (http://www.sns.gov/) at ORNL will be commissioned in the spring of 2006 as the world's brightest source of neutrons. Neutron science users can run experiments, generate datasets, perform data reduction, analysis, visualize results, collaborate with remotes users, and archive long-term data in repositories with curation services. The ORNL-RP and the SNS data analysis group have spent 18 months developing and exploring user requirements, including the creation of prototypical services such as a facility portal, data, and application execution services. We describe results from these efforts and discuss implications for science gateway creation. Finally, we show incorporation into implementation planning for the NSTG and SNS architectures. The plan is for a primarily portal-based user interaction supported by a service-oriented architecture for functional implementation. Published in 2006 by John Wiley & Sons, Ltd.

This research proposal proposes the examination of user attitudes about the Extreme Science and Engineering Discovery Environment (XSEDE). The XSEDE project supports basic research with a common system for making use of national cyberinfrastructure. The systems and infrastructure that make XSEDE useful for researchers are part of an actor network: these systems are socially constructed and they play their own part in the work of XSEDE, and in turn have an effect on the progress of basic research. I have completed previous work on the user relationships in the predecessor to XSEDE, the TeraGrid, and currently carry out participant observation with the management groups of the XSEDE project. By understanding the barriers to adoption of XSEDE by new researchers and new scientific domains, I hope to explore the linkage between resources (in this case computational resources) and scientific outputs.

We report on a scale determination with gradient-flow techniques on the $N_f=2+1+1$ highly improved staggered quark ensembles generated by the MILC Collaboration. The ensembles include four lattice spacings, ranging from approximately 0.15 to 0.06 fm, and both physical and unphysical values of the quark masses. The scales $\sqrt{t_0}/a$ and $w_0/a$ and their tree-level improvements, $\sqrt{t_{0,{\rm imp}}}$ and $w_{0,{\rm imp}}$, are computed on each ensemble using Symanzik flow and the cloverleaf definition of the energy density $E$. Using a combination of continuum chiral-perturbation theory and a Taylor-series ansatz for the lattice-spacing and strong-coupling dependence, the results are simultaneously extrapolated to the continuum and interpolated to physical quark masses. We determine the scales $\sqrt{t_0} = 0.1416({}_{-5}^{+8})$ fm and $w_0 = 0.1714({}_{-12}^{+15})$ fm, where the errors are sums, in quadrature, of statistical and all systematic errors. The precision of $w_0$ and $\sqrt{t_0}$ is comparable to or more precise than the best previous estimates, respectively. We then find the continuum mass dependence of $\sqrt{t_0}$ and $w_0$, which will be useful for estimating the scales of new ensembles. We also estimate the integrated autocorrelation length of $\langle E(t) \rangle$. For long flow times, the autocorrelation length of $\langle E \rangle$ appears to be comparable to that of the topological charge., 50 pages, 12 pdf figures, 10 tables, v2: Figure 11 and Table 9 updated to include additional results by other groups, v3: official PRD release with revised charm mass dependence, autocorrelation sections, and other miscellaneous changes, v4: fixed one typo

Under this SciDAC-2 grant the project’s goal w a s t o stimulate new discoveries by providing scientists with effective and dependable access to an unprecedented national distributed computational facility: the Open Science Grid (OSG). We proposed to achieve this through the work of the Open Science Grid Consortium: a unique hands-on multi-disciplinary collaboration of scientists, software developers and providers of computing resources. Together the stakeholders in this consortium sustain and use a shared distributed computing environment that transforms simulation and experimental science in the US. The OSG consortium is an open collaboration that actively engages new research communities. We operate an open facility that brings together a broad spectrum of compute, storage, and networking resources and interfaces to other cyberinfrastructures, including the US XSEDE (previously TeraGrid), the European Grids for ESciencE (EGEE), as well as campus and regional grids. We leverage middleware provided by computer science groups, facility IT support organizations, and computing programs of application communities for the benefit of consortium members and the US national CI.

We present a Grid solution to a grand challenge problem, the simulation and visualization of the human arterial system. We implemented our simulation and visualization system on the National Science Foundation's TeraGrid and demonstrated it at the iGrid 2005 conference in San Diego, California. We discuss our experience in running on a computational Grid and present observations and suggestions for improving similar experiences.

We present DiPerF, a DIstributed PERformance evaluation Framework, aimed at simplifying and automating performance evaluation of networked services. DiPerF coordinates a pool of machines that access a target service and collect performance measurements, aggregates these measurements, and generates performance statistics. The aggregate data collected provide information on service throughput, service response time, service ‘fairness’ when serving multiple clients concurrently, and on the impact of network connectivity on service performance. We have used DiPerF in various environments (PlanetLab, Grid3, TeraGrid, and a cluster) and with a large number of services. This paper provides data that demonstrates that DiPerF is accurate: The aggregate client view matches the tested service view within a few percents, and scalable: DiPerF can handle more than 10,000 clients and 100,000 transactions per second. Moreover, rapid adoption and extensive use demonstrate that the ability to automate performance characteristics extraction makes DiPerF a valuable tool.

The TeraGrid's collective computing resources can help researchers perform very-large-scale simulations in computational fluid dynamics (CFD) applications, but doing so requires tightly coupled communications among different sites. The authors examine a scaled-down turbulent flow problem, investigating the feasibility and scalability of cross-site simulation paradigms, targeting grand challenges such as blood flow in the entire human arterial tree.