We study exascale parallel algorithms for the selection of intervention or monitoring strategies in massive realistic socio-technical networks through scalable Influence Maximization (InfMax) algorithms.We employ novel techniques to enable efficient scaling on up to 8k nodes of OLCF Frontier, with 65k AMD GPUs and 458k AMD CPU cores.Current state-of-the-art InfMax tools are limited to networks with only a few million actors (vertices) and a few hundred million interactions (edges).By overcoming these limitations, ICS '25, June 08-11, 2025, Salt Lake City, UT, USA Minutoli et al.we show that our approach is capable of processing a realistic social contact network of the United States with 285 million nodes and about 8 billion edges.This two ordersof-magnitude improvement over the previous state-of-theart is obtained by leveraging algorithmic advancements for the InfMax problem and designing several problem-specific approaches to overlap communication with computation, improve GPU efficiency, and lower the application's memory requirements.We evaluate strong scaling for computing 10k most influential seeds using up to 8k nodes of an exascale system, and weak scaling from 128 to 8k system nodes for seed sets ranging from 625 to 40k seeds.We achieve the fastest-known runtime of 25 minutes while performing 48 million diffusion simulations totaling 2.31 petabytes to identify 40k influential seeds using 8k nodes, and take 5.75 minutes to identify 10k seeds while using 4k nodes.

In this paper, we summarize our effort to create and utilize an integrated framework to coordinate computational AI analytics tasks with the help of a task and experiment management workflow system. Our design is based on a minimalistic approach while at the same time allowing access to hybrid computational resources offered through the owner's computer, HPC computing centers, cloud resources, and distributed systems in general. Access to this framework includes a GUI for monitoring and managing the workflow, a REST service, a command line interface, as well as a Python interface. It uses a template-based batch management system that, through configuration files, easily allows for the generation of reproducible experiments while creating permutations over selected experiment parameters as typical in deep learning applications. The resulting framework was developed for analytics workflows targeting MLCommons benchmarks of AI applications on hybrid computing resources, as well as an educational tool for teaching scientists and students sophisticated concepts to execute computations on resources ranging from a single computer to many thousands of computers as part of on-premise and cloud infrastructure. We demonstrate the usefulness of the tool while creating FAIR principle-based application accuracy benchmark generation for the MLCommons Science Working Group Cloudmask application. The code is available as an open-source project in GitHub and is based on an easy-to-enhance framework called Cloudmesh. It can be applied to other applications easily.

Data-intensive applications impact many domains, and their steadily increasing size and complexity demands highperformance, highly usable environments. We integrate a set of ideas developed in various data science and data engineering frameworks. They employ a set of operators on specific data abstractions that include vectors, matrices, tensors, graphs, and tables. Our key concepts are inspired from systems like MPI, HPF (High-Performance Fortran), NumPy, Pandas, Spark, Modin, PyTorch, TensorFlow, RAPIDS(NVIDIA), and OneAPI (Intel). Further, it is crucial to support different languages in everyday use in the Big Data arena, including Python, R, C++, and Java. We note the importance of Apache Arrow and Parquet for enabling language agnostic high performance and interoperability. In this paper, we propose High-Performance Tensors, Matrices and Tables (HPTMT), an operator-based architecture for data-intensive applications, and identify the fundamental principles needed for performance and usability success. We illustrate these principles by a discussion of examples using our software environments, Cylon and Twister2 that embody HPTMT.

Today’s problems require a plethora of analytics tasks to be conducted to tackle state-of-the-art computational challenges posed in society impacting many areas including health care, automotive, banking, natural language processing, image detection, and many more data analytics-related tasks. Sharing existing analytics functions allows reuse and reduces overall effort. However, integrating deployment frameworks in the age of cloud computing are often out of reach for domain experts. Simple frameworks are needed that allow even non-experts to deploy and host services in the cloud. To avoid vendor lock-in, we require a generalized composable analytics service framework that allows users to integrate their services and those offered in clouds, not only by one, but by many cloud compute and service providers.We report on work that we conducted to provide a service integration framework for composing generalized analytics frame-works on multi-cloud providers that we call our Generalized AI Service (GAS) Generator. We demonstrate the framework’s usability by showcasing useful analytics workflows on various cloud providers, including AWS, Azure, and Google, and edge computing IoT devices. The examples are based on Scikit learn so they can be used in educational settings, replicated, and expanded upon. Benchmarks are used to compare the different services and showcase general replicability.

Designing low latency applications that can process large volumes data with higher efficiency is a challenging problem. With the limited time to process data, usage of online algorithms are becoming important in the big-data applications. Stream processing is a well-known area that has been studied for a long time. In this research, our objective is to use state of the art big-data analytic engines to implement online algorithms and compare the strengths and weaknesses in each system. We use a streaming version of Support Vector Machines (SVM) and KMeans to do the analysis. Apache Flink, Apache Storm and Twister2 streaming frameworks are used to implement these algorithms. Our study focuses on the efficiency of online training of these algorithms and the results show higher performance in Twister2 framework for these algorithms.

We present a framework that compares the publication impact based on a comprehensive peer analysis of papers produced by scientists using XSEDE and NCAR resources. The analysis is introducing a percentile ranking based approach of citations of the XSEDE and NCAR papers compared to peer publications in the same journal that do not use these resources. This analysis is unique in that it evaluates the impact of the two facilities by comparing the reported publications from them to their peers from within the same journal issue. From this analysis, we can see that papers that utilize XSEDE and NCAR resources are cited statistically significantly more often. Hence we find that reported publications indicate that XSEDE and NCAR resources exert a strong positive impact on scientific research.

SUMMARY The important role high‐performance computing (HPC) resources play in science and engineering research, coupled with its high cost (capital, power and manpower), short life and oversubscription, requires us to optimize its usage – an outcome that is only possible if adequate analytical data are collected and used to drive systems management at different granularities – job, application, user and system. This paper presents a method for comprehensive job, application and system‐level resource use measurement, and analysis and its implementation. The steps in the method are system‐wide collection of comprehensive resource use and performance statistics at the job and node levels in a uniform format across all resources, mapping and storage of the resultant job‐wise data to a relational database, which enables further implementation and transformation of the data to the formats required by specific statistical and analytical algorithms. Analyses can be carried out at different levels of granularity: job, user, application or system‐wide. Measurements are based on a new lightweight job‐centric measurement tool ‘TACC_Stats’, which gathers a comprehensive set of resource use metrics on all compute nodes and data logged by the system scheduler. The data mapping and analysis tools are an extension of the XDMoD project. The method is illustrated with analyses of resource use for the Texas Advanced Computing Center's Lonestar4, Ranger and Stampede supercomputers and the HPC cluster at the Center for Computational Research. The illustrations are focused on resource use at the system, job and application levels and reveal many interesting insights into system usage patterns and also anomalous behavior due to failure/misuse. The method can be applied to any system that runs the TACC_Stats measurement tool and a tool to extract job execution environment data from the system scheduler. Copyright © 2014 John Wiley & Sons, Ltd.

Heterogeneous parallel systems with multi processors and accelerators are becoming ubiquitous due to better cost-performance and energy-efficiency. These heterogeneous processor architectures have different instruction sets and are optimized for either task-latency or throughput purposes. Challenges occur in regard to programmability and performance when running SPMD tasks on heterogeneous devices. In order to meet these challenges, we implemented a parallel runtime system that used to co-process SPMD computation on CPUs and GPUs clusters. Furthermore, we are proposing an analytic model to automatically schedule SPMD tasks on heterogeneous clusters. Our analytic model is derived from the roofline model, and therefore it can be applied to a wider range of SPMD applications and hardware devices. The experimental results of the C-means, GMM, and GEMV show good speedup in practical heterogeneous cluster environments.

SUMMARY This paper describes XSEDE Metrics on Demand, a comprehensive auditing framework for use by high‐performance computing centers, which provides metrics regarding resource utilization, resource performance, and impact on scholarship and research. This role‐based framework is designed to meet the following objectives: (1) provide the user community with a tool to manage their allocations and optimize their resource utilization; (2) provide operational staff with the ability to monitor and tune resource performance; (3) provide management with a tool to monitor utilization, user base, and performance of resources; and (4) provide metrics to help measure scientific impact. Although initially focused on the XSEDE program, XSEDE Metrics on Demand can be adapted to any high‐performance computing environment. The framework includes a computationally lightweight application kernel auditing system that utilizes performance kernels to measure overall system performance. This allows continuous resource auditing to measure all aspects of system performance including filesystem performance, processor and memory performance, and network latency and bandwidth. Metrics that focus on scientific impact, such as publications, citations and external funding, will be included to help quantify the important role high‐performance computing centers play in advancing research and scholarship. Copyright © 2012 John Wiley & Sons, Ltd.

Cloud computing has become an important driver for delivering infrastructure as a service (IaaS) to users with on-demand requests for customized environments and sophisticated software stacks. Within the FutureGrid (FG) project, we offer different IaaS frameworks as well as high performance computing infrastructures by allowing users to explore them as part of the FG testbed. To ease the use of these infrastructures, as part of performance experiments, we have designed an image management framework, which allows us to create user defined software stacks based on abstract image management and uniform image registration. Consequently, users can create their own customized environments very easily. The complex processes of the underlying infrastructures are managed by our sophisticated software tools and services. Besides being able to manage images for IaaS frameworks, we also allow the registration and deployment of images onto bare-metal by the user. This level of functionality is typically not offered in a HPC (high performance computing) infrastructure. However, our approach provides users with the ability to create their own environments changing the paradigm of administrator-controlled dynamic provisioning to user-controlled dynamic provisioning, which we also call raining. Thus, users obtain access to a testbed with the ability to manage state-of-the-art software stacks that would otherwise not be supported in typical compute centers. Security is also considered by vetting images before they are registered in a infrastructure. In this paper, we present the design of our image management framework and evaluate two of its major components. This includes the image creation and image registration. Our design and implementation can support the current FG user community interested in such capabilities.

Today, many cloud Infrastructure as a Service(IaaS) frameworks exist. Users, developers, and administrators have to make a decision about which environment is best suited for them. Unfortunately, the comparison of such frameworks is difficult because either users do not have access to all of them or they are comparing the performance of such systems on different resources, which make it difficult to obtain objective comparisons. Hence, the community benefits from the availability of a testbed on which comparisons between the IaaS frameworks can be conducted. FutureGrid aims to offer a number of IaaS including Nimbus, Eucalyptus, OpenStack, and OpenNebula. One of the important features that FutureGrid provides is not only the ability to compare between IaaS frameworks, but also to compare them in regards to bare-metal and traditional high performance computing services. In this paper, we outline some of our initial findings by providing such a testbed. As one of our conclusions, we also present our work on making access to the various infrastructures on FutureGrid easier.

As Cloud computing emerges as a dominant paradigm in distributed systems, it is important to fully understand the underlying technologies that make Clouds possible. One technology, and perhaps the most important, is virtualization. Recently virtualization, through the use of hyper visors, has become widely used and well understood by many. However, there are a large spread of different hyper visors, each with their own advantages and disadvantages. This paper provides an in-depth analysis of some of today's commonly accepted virtualization technologies from feature comparison to performance analysis, focusing on the applicability to High Performance Computing environments using Future Grid resources. The results indicate virtualization sometimes introduces slight performance impacts depending on the hyper visor type, however the benefits of such technologies are profound and not all virtualization technologies are equal. From our experience, the KVM hyper visor is the optimal choice for supporting HPC applications within a Cloud infrastructure.

Future Grid (FG) is an experimental, high-performance test bed that supports HPC, cloud and grid computing experiments for both application and computer scientist. Future Grid includes the use of virtualization technology to allow the support of a wide range of operating systems in order to include a test bed for various cloud computing infrastructure as a service frameworks. Therefore, efficient management of a variety of virtual machine images becomes a key issue. Current cloud frameworks do not provide a way to manage images for different IaaS frameworks. They typically provide their own image repositories, but in general they do not allow us to store the needed metadata to handle other IaaS images. We present a generic catalog and image repository to store images of any type. Our image repository has a convenient interface that distinguishes image types. Therefore, it is not only useful for Future Grid, but also for any application that needs to manage images.

Abstract Electronic Health Records (EHRs) have many potential advantages over traditional paper records, such as wide scale access, error checking, and protection from physical damage to a record. As with any medical record, paper or electronic, both the patient's privacy and the document's integrity must be guaranteed. With initiatives such as Integrating the Healthcare Enterprise (IHE), computerized healthcare systems are able to share EHRs on a large scale, while protecting the patient's privacy rights. However, IHE does not yet meet the needs of all healthcare systems, as we will show with the eMOLST project. The eMOLST project delivers software in support of Medical Order for Life Sustaining Treatment (MOLST) forms and uses IHE specifications for cross enterprise document storage and sharing, patient identification, and user authentication and authorization. The Web‐based system provides secure access to electronic MOLST documents regardless of the patient's or healthcare provider's location. The eMOLST project allows a user to have Single Sign On (SSO) access to the system from either the user's associated enterprise, or through a Web portal shared amongst all users across all enterprises. In this paper, we show a security solution to allow SSO from multiple access points for IHE compliant systems. Copyright © 2011 John Wiley & Sons, Ltd.

Virtual machines offer unique advantages to the scientific computing community, such as Quality of Service(QoS) guarantee, performance isolation, easy resource management, and the on-demand deployment of computing environments. Using virtual machines as a computing resource within a distributed environment, such as Service Oriented Architecture (SOA), creates a variety of new issues and challenges that must be overcome. Traditionally, parallel task scheduling algorithms only focus on handling CPU resources. Using of a virtual machine, however, requires the monitoring and management of additional resource properties. Additionally, CPU, memory, storage, and software licenses must also be considered within the scheduling algorithm. The objective of this paper is to address these challenges of a multi-dimensional scheduling algorithm for virtual machines within a SOA. To do this, we deploy a testbed SOA environment composed of virtual machines which are capable of being registered, indexed, allocated, accessed, and controlled by our new parallel task scheduling algorithm.

Reducing energy consumption for high end computing can bring various benefits such as, reduce operating costs, increase system reliability, and environment respect. This paper aims to develop scheduling heuristics and to present application experience for reducing power consumption of parallel tasks in a cluster with the Dynamic Voltage Frequency Scaling (DVFS) technique. In this paper, formal models are presented for precedence-constrained parallel tasks, DVFS enabled clusters, and energy consumption. This paper studies the slack time for non-critical jobs, extends their execution time and reduces the energy consumption without increasing the task’s execution time as a whole. Additionally, Green Service Level Agreement is also considered in this paper. By increasing task execution time within an affordable limit, this paper develops scheduling heuristics to reduce energy consumption of a tasks execution and discusses the relationship between energy consumption and task execution time. Models and scheduling heuristics are examined with a simulation study. Test results justify the design and implementation of proposed energy aware scheduling heuristics in the paper.

It has been widely known that various benefits can be achieved by reducing energy consumption for high end computing. This paper aims to develop power aware scheduling heuristics for parallel tasks in a cluster with the DVFS technique. In this paper, formal models are presented for precedence-constrained parallel tasks, DVFS enabled clusters, and energy consumption. This paper studies the slack time for non-critical jobs, extends their execution time and reduces the energy consumption without increasing the task's execution time as a whole. This paper develops a power aware task clustering algorithm for parallel task scheduling Simulation results justify the design and implementation of proposed energy aware scheduling heuristics in the paper.

The Software as a Service (SaaS) methodology is a key paradigm of Cloud computing. In this paper, we focus on an interesting topic - to implement a Cloud computing functionality, the SaaS model, on existing production Grid infrastructures. In general, production Grids employ a Job-Submission-Execution (JSE) model with rigid access interfaces. In this paper we develop the Cyberaide onServe, a lightweight middleware with a virtual appliance. The Cyberaide onServe implements the SaaS methodology on production Grids by translating the SaaS model to the JSE model. The Cyberaide onServe virtual appliance is deployed on demand, hosts applications as Web services, accepts Web service invocations, and finally the Cyberaide onServe executes them on production Grids. We have deployed the Cyberaide onServe on the TeraGrid infrastructure and test results show Cyberaide onServe can provide the SaaS functionality with good performance.

Distributed virtual machines can help to build scalable, manageable, and efficient grid infrastructures. The work proposed in this paper focuses on employing virtual machines for grid computing. In order to efficiently run grid applications, virtual machine resource information should be provided. This paper first discusses the system architecture of virtual machine pools and the process of information retrieval from virtual machines. Based on the characterization of the system model, this paper presents the work on how to retrieve resource information from Xen/VMware virtual machines via VMware Common Information Model Software Development Kit and lightweight Java agents. The resource information is integrated into a grid information service. The work is implemented in a test bed with Xen/VMware virtual machines and the Globus Toolkit. With a performance evaluation and discussion on a real test bed, it is declared that the design and implementation of information services for virtual-machine-based grid systems are feasible, efficient, and scalable.

In recent years the power of Grid computing has grown exponentially through the development of advanced middleware systems. While usage has increased, the penetration of Grid computing in the scientific community has been less than expected by some. This is due to a steep learning curve and high entry barrier that limit the use of Grid computing and advanced cyberinfrastructure. In order for the scientists to focus on actual scientific tasks, specialized tools and services need to be developed to ease the integration of complex middleware. Our solution is Cyberaide Shell, an advanced but simple to use systemshell which provides access to the powerful cyberinfrastructure available today. Cyberaide Shell provides a dynamic interface that allows access to complex cyberinfrastructure in an easy and intuitive fashion on an ad-hoc basis. This is accomplished by abstracting the complexities of resource, task, and application management through a scriptable command line interface. Through a service integration mechanism, the shellpsilas functionality is exposed to a wide variety of frameworks and programming languages. Cyberaide Shell includes specialized experiment management and workflow commands that, with the scriptable nature of a shell, provide a set of services which where previously unavailable. The usability of Cyberaide Shell is demonstrated using a Water Threat Management application deployed on the TeraGrid.

With the advent of Cloud computing, large-scale virtualized compute and data centers are becoming common in the computing industry. These distributed systems leverage commodity server hardware in mass quantity, similar in theory to many of the fastest Supercomputers in existence today. However these systems can consume a cities worth of power just to run idle, and require equally massive cooling systems to keep the servers within normal operating temperatures. This produces CO2emissions and significantly contributes to the growing environmental issue of Global Warming. Green computing, a new trend for high-end computing, attempts to alleviate this problem by delivering both high performance and reduced power consumption, effectively maximizing total system efficiency. This paper focuses on scheduling virtual machines in a compute cluster to reduce power consumption via the technique of Dynamic Voltage Frequency Scaling (DVFS). Specifically, we present the design and implementation of an efficient scheduling algorithm to allocate virtual machines in a DVFS-enabled cluster by dynamically scaling the supplied voltages. The algorithm is studied via simulation and implementation in a multi-core cluster. Test results and performance discussion justify the design and implementation of the scheduling algorithm.

Data centers now play an important role in modern IT infrastructures. Related research has shown that the energy consumption for data center cooling systems has recently increased significantly. There is also strong evidence to show that high temperatures with in a data center will lead to higher hardware failure rates and thus an increase in maintenance costs. This paper devotes itself in the field of thermal aware resource management for data centers. This paper proposes an analytical model, which describes data center resources with heat transfer properties and workloads with thermal features. Then a thermal aware task scheduling algorithm with backfilling is presented which aims to reduce power consumption and temperatures in a data center. A simulation study is carried out to evaluate the performance of the algorithm. Simulation results show that our algorithm can significantly reduce temperatures in data centers by introducing endurable decline in performance.

Like many modern techniques for scientific analysis, flow cytometry produces massive amounts of data that must be analyzed and clustered intelligently to be useful. Current manual binning techniques are cumbersome and limited in both the quality and quantity of analysis produced. To address the quality of results, a new framework applying two different sets of clustering algorithms and inference methods are implemented. The two methods investigated are fuzzy c-means with minimum description length inference and k-medoids with BIC. These approaches lend themselves to large scale parallel processing. To address the computational demands, the Nvidia CUDA framework and Tesla architecture are utilized. The resulting performance demonstrated 1-2 orders of magnitude improvement over an equivalent sequential version. The quality of results is promising and motivates further research and development in this direction.

Abstract We review the efforts of the Open Grid Computing Environments collaboration. By adopting a general three‐tiered architecture based on common standards for portlets and Grid Web services, we can deliver numerous capabilities to science gateways from our diverse constituent efforts. In this paper, we discuss our support for standards‐based Grid portlets using the Velocity development environment. Our Grid portlets are based on abstraction layers provided by the Java CoG kit, which hide the differences of different Grid toolkits. Sophisticated services are decoupled from the portal container using Web service strategies. We describe advance information, semantic data, collaboration, and science application services developed by our consortium. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract We introduce a framework for measuring the use of Grid services and exposing simple summary data to an authorized set of Grid users through a JSR168‐enabled portal. The sensor framework has been integrated into the Globus Toolkit and allows Grid administrators to have access to a mechanism helping with report and usage statistics. Although the original focus was the reporting of actions in relationship to GridFTP services, the usage service has been expanded to report also on the use of other Grid services. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract The Knowledge Environment for Collaborative Science (KnECS) is an open‐source informatics toolkit designed to enable knowledge Grids that interconnect science communities, unique facilities, data, and tools. KnECS features a Web portal with team and data collaboration tools, lightweight federation of data, provenance tracking, and multi‐level support for application integration. We identify the capabilities of KnECS and discuss extensions from the Collaboratory for Multi‐Scale Chemical Sciences (CMCS) which enable diverse combustion science communities to create and share verified, documented data sets and reference data, thereby demonstrating new methods of community interaction and data interoperability required by systems science approaches. Finally, we summarize the challenges we encountered and foresee for knowledge environments. Copyright © 2007 John Wiley & Sons, Ltd.

This workshop will focus on projects and technologies that are adopting scientific portals and gateways. These technologies are characterized by delivering well-established mechanisms for providing familiar interfaces to secure Grid resources, services, applications, tools, and collaboration services for communities of scientists. In most cases access is enabled through a web browser without the need to download or install any specialized software or worry about networks and ports. As a result, the science application user is isolated from the complex details and infrastructure needed to operate an application on the Grid. Additional information about this workshop is available at http://www.cogkit.org/GCE06

This workshop will focus on projects and technologies that are adopting scientific portals and gateways. These technologies are characterized by delivering well-established mechanisms for providing familiar interfaces to secure Grid resources, services, applications, tools, and collaboration services for communities of scientists. In most cases access is enabled through a web browser without the need to download or install any specialized software or worry about networks and ports. As a result, the science application user is isolated from the complex details and infrastructure needed to operate an application on the Grid. Additional information about this workshop is available at http://www.cogkit.org/GCE06

The Grid approach provides the ability to access and use distributed resources as part of virtual organizations. The emerging Grid infrastructure gives rise to a class of scientific applications and services to support collaborative and distributed resource-sharing requirements as part of teleimmersion, visualization, and simulation services. Because such applications operate in a collaborative mode, data must be stored and delivered in timely manner to meet deadlines. Hence, this class of applications has stringent real-time constraints and quality-of-service (QoS) requirements. A QoS management approach is required to orchestrate and guarantee the interaction between such applications and services. In this paper we discuss the design and prototype implementation of a QoS system and show how we enable Grid applications to become QoS compliant. We validate this approach through a case study of nanomaterials. Our approach enhances the current Open Grid Services Architecture. We demonstrate the usefulness of the approach on a nanomaterials application.

The research described in this paper is performed as part of the Globus Project. It introduces a new grid service called InfoGram that combines the ability of serving as information service and as a job execution service. Previously, both services were architected and implemented within the Globus Toolkit as two different services with different wire protocols. Our service demonstrates a significant simplification of the architecture while treating job submissions and information queries alike. The advantage of our service is that it provides backwards compatibility to existing grid services, while at the same time providing forwards compatibility to the emerging Web services world. Part of the work conducted within this effort is already reused by the current open grid services architecture prototype implementation.

Abstract In this paper we report on the features of the Java Commodity Grid Kit (Java CoG Kit). The Java CoG Kit provides middleware for accessing Grid functionality from the Java framework. Java CoG Kit middleware is general enough to design a variety of advanced Grid applications with quite different user requirements. Access to the Grid is established via Globus Toolkit protocols, allowing the Java CoG Kit to also communicate with the services distributed as part of the C Globus Toolkit reference implementation. Thus, the Java CoG Kit provides Grid developers with the ability to utilize the Grid, as well as numerous additional libraries and frameworks developed by the Java community to enable network, Internet, enterprise and peer‐to‐peer computing. A variety of projects have successfully used the client libraries of the Java CoG Kit to access Grids driven by the C Globus Toolkit software. In this paper we also report on the efforts to develop serverside Java CoG Kit components. As part of this research we have implemented a prototype pure Java resource management system that enables one to run Grid jobs on platforms on which a Java virtual machine is supported, including Windows NT machines. Copyright © 2002 John Wiley & Sons, Ltd.

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.

Abstract The Perl Commodity Grid Toolkit (Perl CoG Kit) is a software project aimed at bringing the complexities and power of the computational Grid to developers of Perl applications. In this paper we describe the history and motivation of the project, the benefits of bringing the Grid to Perl developers, the architecture and status of the Perl CoG Kit and the future plans for the project. Copyright © 2002 John Wiley & Sons, Ltd.

Abstract This paper reports on an ongoing research project aimed at designing and deploying a Common Object Resource Broker Architecture (CORBA) (ww.omg.org) Commodity Grid (CoG) Kit. The overall goal of this project is to enable the development of advanced Grid applications while adhering to state‐of‐the‐art software engineering practices and reusing the existing Grid infrastructure. As part of this activity, we are investigating how CORBA can be used to support the development of Grid applications. In this paper, we outline the design of a CORBA CoG Kit that will provide a software development framework for building a CORBA ‘Grid domain’. We also present our experiences in developing a prototype CORBA CoG Kit that supports the development and deployment of CORBA applications on the Grid by providing them access to the Grid services provided by the Globus Toolkit. Copyright © 2002 John Wiley & Sons, Ltd.

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.

The advent of national-scale "Computational Grid" infrastructures has helped deploy advanced services, beyond those taken for granted in today's Internet, such as: remote access to computers, wide area resource management, authentication, and directory services, thus enabling access and utilization of a variety of heterogeneous resources distributed over multiple domains. The availability of these services represents an opportunity to implement advanced services utilizing these basic Grid services. In this paper, we explore issues related to dening services that are based on economy and nancial models in order to encourage further resource sharing among the administrative domains while also considering commodity PC's that are part of todays Internet. We propose an extendable architecture, The Grid Economic Engine Directive (Greed), that allows integration of various economy models within the same framework, exposing the services through secure protocols and policies. The services provided by this framework include, for example, a bartering service, a bidding service, and a trading service. We intend to develop components that can be integrated within a customizable Portal simplifying access to many of the services and propose to integrate the Greed economic middleware into the existing Globus metacomputing toolkit, thus enabling the application of economic paradigm to the Globus Computational and Data Grids. We further illustrate the applicability of the proposed Greed services by building a prototype business model as a higher-level application, using the economic middleware. Keywords: Grid, Computational Economy, Commodity Computing, Data Grids

Abstract Developing advanced applications for the emerging national‐scale ‘Computational Grid’ infrastructures is still a difficult task. Although Grid services are available that assist the application developers in authentication, remote access to computers, resource management, and infrastructure discovery, they provide a challenge because these services may not be compatible with the commodity distributed‐computing technologies and frameworks used previously. The Commodity Grid project is working to overcome this difficulty by creating what we call Commodity Grid Toolkits (CoG Kits) that define mappings and interfaces between Grid and particular commodity frameworks. In this paper, we explain why CoG Kits are important, describe the design and implementation of a Java CoG Kit, and use examples to illustrate how CoG Kits can enable new approaches to application development based on the integrated use of commodity and Grid technologies. Copyright © 2001 John Wiley & Sons, Ltd.

This paper introduces a general metacomputing framework for submitting jobs across a variety of distributed computational resources. A first-come, first-served scheduling algorithm distributes the jobs across the computer resources. Because dependencies between jobs are expressed via a dataflow graph, the framework is more than just a uniform interface to the independently running queuing systems and interactive shells on each computer system. Using the dataflow approach extends the concept of sequential batch and interactive processing to running programs across multiple computers and computing sites in co-operation. We present results from a Grand Challenge case study showing that the turnaround time was dramatically reduced by having access to several supercomputers at runtime. The framework is applicable to other complex scientific problems that are coarse grained. Copyright © 1999 John Wiley & Sons, Ltd.

The potential for faults in distributed computing systems is a significant complicating factor for application developers. While a variety of techniques exist for detecting and correcting faults, the implementation of these techniques in a particular context can be difficult. Hence, we propose a fault detection service designed to be incorporated, in a modular fashion, into distributed computing systems, tools, or applications. This service uses well-known techniques based on unreliable fault detectors to detect and report component failure, while allowing the user to tradeoff timeliness of reporting against false positive rates. We describe the architecture of this service, report on experimental results that quantify its cost and accuracy, and describe its use in two applications, monitoring the status of system components of the GUSTO computational grid testbed and as part of the NetSolve network-enabled numerical solver.

High-performance execution in distributed computing environments often requires careful selection and configuration not only of computers, networks, and other resources but also of the protocols and algorithms used by applications. Selection and configuration in turn require access to accurate, up-to-date information on the structure and state of available resources. Unfortunately, no standard mechanism exists for organizing or accessing such information. Consequently, different tools and applications adopt ad hoc mechanisms, or they compromise their portability and performance by using default configurations. We propose a solution to this problem: a Metacomputing Directory Service that provides efficient and scalable access to diverse, dynamic, and distributed information about resource structure and state. We define an extensible data model to represent the information required for distributed computing, and we present a scalable, high-performance, distributed implementation. The dat...

The authors present the parallelization of blocked algorithms for LU factorization. They isolate problems inherent in sequential blocked algorithms and provide approaches to overcome them on distributed memory architectures. The performances of the parallelized versions of three blocked algorithms suited to column oriented Fortran are compared. Experiments are performed on the iPSC/860 hypercube. It is shown that it is not intuitively clear which algorithm might perform best on a given architecture; this is dependent on the problem size and the number of available parameters.>