High Performance Computing Solutions Research Articles

ScRNAbox: empowering single-cell RNA sequencing on high performance computing systems

BackgroundSingle-cell RNA sequencing (scRNAseq) offers powerful insights, but the surge in sample sizes demands more computational power than local workstations can provide. Consequently, high-performance computing (HPC) systems have become imperative. Existing web apps designed to analyze scRNAseq data lack scalability and integration capabilities, while analysis packages demand coding expertise, hindering accessibility.ResultsIn response, we introduce scRNAbox, an innovative scRNAseq analysis pipeline meticulously crafted for HPC systems. This end-to-end solution, executed via the SLURM workload manager, efficiently processes raw data from standard and Hashtag samples. It incorporates quality control filtering, sample integration, clustering, cluster annotation tools, and facilitates cell type-specific differential gene expression analysis between two groups. We demonstrate the application of scRNAbox by analyzing two publicly available datasets.ConclusionScRNAbox is a comprehensive end-to-end pipeline designed to streamline the processing and analysis of scRNAseq data. By responding to the pressing demand for a user-friendly, HPC solution, scRNAbox bridges the gap between the growing computational demands of scRNAseq analysis and the coding expertise required to meet them.

Perspectives on AI Architectures and Codesign for Earth System Predictability

Abstract Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper. Significance Statement This study aims to provide perspectives on AI architectures and codesign approaches for Earth system predictability. Such visionary perspectives are essential because AI-enabled model-data integration has shown promise in improving predictions associated with climate change, perturbations, and extreme events. Our forward-looking ideas guide what is next in codesign to enhance Earth system models, observations, and theory using state-of-the-art and futuristic computational infrastructure.

Investigating performance metrics for container-based HPC environments using x86 and OpenPOWER systems

Container-based High-Performance Computing (HPC) is changing the way computation is performed and reproduced without sacrificing the raw performance compared to hypervisor-assisted virtualization technologies. It primarily supports continuously evolving data-intensive applications such as computational fluid dynamics, seismic tomography, molecular biology, and Proteomics. OpenPOWER systems, unlike the x86 systems, use the POWER-compliant processor to exploit instruction-level and thread-level parallelism heavily. In our previous work, we designed and developed a Containerized HPC environment (cHPCe) from the scratch using Linux namespaces on OpenPOWER systems. This paper aims to provide an in-depth performance analysis of the Containerized HPC environment using x86 systems and Containerized HPC environment using the OpenPOWER system, on systems’ subcomponents, processor, memory, interconnect, and IO. This sub-component analysis provides an insight on several aspects of the system performance. To the best of our knowledge, no research has been reported yet for such a comparative analysis that designs cHPCe for both x86 and OpenPOWER systems. The performance of the developed cHPCe is compared with BareMetals, and VMs using the benchmarks HPCC, and IOZone. Our experimental results achieve 0.13% less compute performance penalty at its peak performance on cHPCe compared to the BareMetal-based solution for x86 systems. In contrast, a VM-based solution introduces an overhead of 20% and 4.83% in x86 and OpenPOWER cases, respectively. Moreover, the x86 and OpenPOWER systems observe inconsistent behavior for memory performance with a worst-case penalty of 9.68% and 6.64% compared to achieved peak performance, respectively. However, similar behavior is reported for cHPCe with an overhead of less than 3% and 2% in the worst case for the latency and bandwidth, respectively, compared to the BareMetal for network and disk performance. Our experimental results reveal that the containerized OpenPOWER environment represents a viable alternative to the counterpart x86 environment for the HPC solution.

Breaking the traditional: a survey of algorithmic mechanism design applied to economic and complex environments.

The mechanism design theory can be applied not only in the economy but also in many fields, such as politics and military affairs, which has important practical and strategic significance for countries in the period of system innovation and transformation. As Nobel Laureate Paul said, the complexity of the real economy makes it difficult for "Unorganized Markets" to ensure supply-demand balance and the efficient allocation of resources. When traditional economic theory cannot explain and calculate the complex scenes of reality, we require a high-performance computing solution based on traditional theory to evaluate the mechanisms, meanwhile, get better social welfare. The mechanism design theory is undoubtedly the best option. Different from other existing works, which are based on the theoretical exploration of optimal solutions or single perspective analysis of scenarios, this paper focuses on the more real and complex markets. It explores to discover the common difficulties and feasible solutions for the applications. Firstly, we review the history of traditional mechanism design and algorithm mechanism design. Subsequently, we present the main challenges in designing the actual data-driven market mechanisms, including the inherent challenges in the mechanism design theory, the challenges brought by new markets and the common challenges faced by both. In addition, we also comb and discuss theoretical support and computer-aided methods in detail. This paper guides cross-disciplinary researchers who wish to explore the resource allocation problem in real markets for the first time and offers a different perspective for researchers struggling to solve complex social problems. Finally, we discuss and propose new ideas and look to the future.

Applications of Machine Learning and High-Performance Computing in the Era of COVID-19

During the ongoing pandemic of the novel coronavirus disease 2019 (COVID-19), latest technologies such as artificial intelligence (AI), blockchain, learning paradigms (machine, deep, smart, few short, extreme learning, etc.), high-performance computing (HPC), Internet of Medical Things (IoMT), and Industry 4.0 have played a vital role. These technologies helped to contain the disease’s spread by predicting contaminated people/places, as well as forecasting future trends. In this article, we provide insights into the applications of machine learning (ML) and high-performance computing (HPC) in the era of COVID-19. We discuss the person-specific data that are being collected to lower the COVID-19 spread and highlight the remarkable opportunities it provides for knowledge extraction leveraging low-cost ML and HPC techniques. We demonstrate the role of ML and HPC in the context of the COVID-19 era with the successful implementation or proposition in three contexts: (i) ML and HPC use in the data life cycle, (ii) ML and HPC use in analytics on COVID-19 data, and (iii) the general-purpose applications of both techniques in COVID-19’s arena. In addition, we discuss the privacy and security issues and architecture of the prototype system to demonstrate the proposed research. Finally, we discuss the challenges of the available data and highlight the issues that hinder the applicability of ML and HPC solutions on it.

Investigating the Dirac Operator Evaluation with FPGAs

In recent years the computational capacity of single Field Programmable Gate Arrays (FPGA) devices as well as their versatility has increased significantly. Adding to that the High Level Synthesis frameworks allowing to program such processors in a high level language like C++, makes modern FPGA devices a serious candidate as building blocks of a general purpose High Performance Computing solution. In this contribution we describe benchmarks which we performed using a Lattice QCD code, a highly compute-demanding HPC academic code for elementary particle simulations. We benchmark the performance of a single FPGA device running in two modes: using the external or embedded memory. We discuss both approaches in detail using the Xilinx U250 device and provide estimates for the necessary memory throughput and the minimal amount of resources needed to deliver optimal performance depending on the available hardware platform.

Techniques of medical image processing and analysis accelerated by high-performance computing: a systematic literature review

Techniques of medical image processing and analysis play a crucial role in many clinical scenarios, including in diagnosis and treatment planning. However, immense quantities of data and high complexity of the algorithms often used are computationally demanding. As a result, there now exists a wide range of techniques of medical image processing and analysis that require the application of high-performance computing solutions in order to reduce the required runtime. The main purpose of this review is to provide a comprehensive reference source of techniques of medical image processing and analysis that have been accelerated by high-performance computing solutions. With this in mind, the articles available in the Scopus and Web of Science electronic repositories were searched. Subsequently, the most relevant articles found were individually analyzed in order to identify: (a) the metrics used to evaluate computing performance, (b) the high-performance computing solution used, (c) the parallel design adopted, and (d) the task of medical image processing and analysis involved. Hence, the techniques of medical image processing and analysis found were identified, reviewed, and discussed, particularly in terms of computational performance. Consequently, the techniques reviewed herein present the progress made so far in reducing the computational runtime involved, and the difficulties and challenges that remain to be overcome.

Introduction to the Special Issue on High Performance Computing Solutions for Complex Problems

Introduction to the Special Issue on High Performance Computing Solutions for Complex Problems

Multilevel Data Processing Using Parallel Algorithms for Analyzing Big Data in High-Performance Computing

The growing gap between users and the Big Data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze such massive volume of data. Moreover, advancements in the field of Big Data application and data science poses additional challenges, where High-Performance Computing solution has become a key issue and has attracted attention in recent years. However, these systems are either memoryless or computational inefficient. Therefore, keeping in view the aforementioned needs, there is a requirement for a system that can efficiently analyze a stream of Big Data within their requirements. Hence, this paper presents a system architecture that enhances the working of traditional MapReduce by incorporating parallel processing algorithm. Moreover, complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed parallel processing algorithm. The proposed system architecture both read and writes operations that enhance the efficiency of the Input/Output operation. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce. MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes to generate and process graphs with near real-time. Moreover, the system is evaluated in terms of efficiency by considering the system throughput and processing time. The results show that the proposed system is more scalable and efficient.

Care HPS: A high performance simulation tool for parallel and distributed agent-based modeling

Parallel and distributed simulation is a powerful tool for developing complex agent-based simulation. Complex simulations require parallel and distributed high performance computing solutions. It is necessary because their sequential solutions are not able to give answers in a feasible total execution time. Therefore, for the advance of computing science, it is important that High Performance Computing (HPC) techniques and solutions be proposed and studied. In literature, we can find some agent-based modeling and simulation tools that use HPC. However, none of these tools are designed to enable the HPC expert to be able to propose new techniques and solutions without great effort. In this paper, we introduce Care High Performance Simulation (HPS), which is a scientific instrument that enables researchers to: (1) develop techniques and solutions of high performance distributed simulations for agent-based models; and, (2) study, design and implement complex agent-based models that require HPC solutions. Care HPS was designed to easily and quickly develop new agent-based models. It was also designed to extend and implement new solutions for the main issues of parallel and distributed solutions such as: synchronization, communication, load and computing balancing, and partitioning algorithms. We conducted some experiments with the aim of showing the completeness and functionality of Care HPS. As a result, we show that Care HPS can be used as a scientific instrument for the advance of the agent-based parallel and distributed simulations field.

Needles: Toward Large-Scale Genomic Prediction with Marker-by-Environment Interaction.

Genomic prediction relies on genotypic marker information to predict the agronomic performance of future hybrid breeds based on trial records. Because the effect of markers may vary substantially under the influence of different environmental conditions, marker-by-environment interaction effects have to be taken into account. However, this may lead to a dramatic increase in the computational resources needed for analyzing large-scale trial data. A high-performance computing solution, called Needles, is presented for handling such data sets. Needles is tailored to the particular properties of the underlying algebraic framework by exploiting a sparse matrix formalism where suited and by utilizing distributed computing techniques to enable the use of a dedicated computing cluster. It is demonstrated that large-scale analyses can be performed within reasonable time frames with this framework. Moreover, by analyzing simulated trial data, it is shown that the effects of markers with a high environmental interaction can be predicted more accurately when more records per environment are available in the training data. The availability of such data and their analysis with Needles also may lead to the discovery of highly contributing QTL in specific environmental conditions. Such a framework thus opens the path for plant breeders to select crops based on these QTL, resulting in hybrid lines with optimized agronomic performance in specific environmental conditions.

Introduction to the Special Issue on High Performance Computing Solutions for Complex Problems

In the last decades, the complexity of the current and upcoming scientific/engineering problems has increased considerably. Computations involved in numerical simulations, molecular dynamics, computational fluid dynamics, bio-informatics, image processing, linear algebra, deep-learning, information retrieval, or big-data computing are just a few examples of such problems. At the same time, improvements in high performance computing (HPC) systems are mainly associated with an important increasing in the complexity of computer architectures, making difficult the code optimization. This results in a greater gap between the general scientific / engineering user community (in need of easy access to efficient high performance computations) and the HPC programmers community (who design codes for narrow sub-classes of problems). The development of user-friendly codes for non-HPC-trained user community becomes a big challenge. As consequence, effective use of HPC centers requires specialized / individual training for each user group. Today, programmers of HPC or/and scientific applications have to deal with numerous details regarding computer architectures at low level to take advantage of the last features of the current and upcoming computing systems. It makes difficult the efficient and optimized software development. Thus, strategies and tools that can help us to adapt our codes over different computing architectures is of vital importance. However, previously, we must know and identify what are most efficient programming strategies and architectonic features. This is a difficult task as both, strategies and features, depend on the particular problem to be dealt. Portability is other important issue today. Multiples kind of processors have arisen in the last years. Most of these current platforms use their own compilers, languages, etc., being a very complex task to implement portable codes. This special issue provides several studies, which involve different applications and strategies to improve performance and to achieve better usage of current computing systems. It is composed by three works. The work Performance Optimizations for an Automatic Target Generation Process in Hyperspectral Analysis by Fernando Sierra-Pajuelo, Abel Paz-Gallardo, and Antonio Plaza presents several optimizations for hyperspectral image processing algorithms intended to detect targets in hyperspectral images. The algorithm used is the automated target generation process (ATGP) and the optimizations comprise parallel versions of the algorithm developed using open multi-processing (OpenMP, including Intel Xeon Phi) and message passing interface (MPI). The study Using Computational Geometry to Improve Process Rescheduling on Round-Based Parallel Applications by R. da Rosa-Righi, V. Magalhaes-Guerreiro, G. Rostirolla, V. Facco-Rodrigues, C. Andre da Costa, and L. Dagnino-Chiwiacowsky, proposes two novel heuristics applied to process rescheduling, named MigCube and MigHull, to choose the candidate processes for migration and their destination. Both heuristics consider the use of computational geometry for plotting computation, communication and migration costs metrics in a 3D graph without any user intervention. Finally, the work Many-Task Computing on Many-Core Architectures by Pedro Valero-Lara, Poorima Nookala, Fernando L. Pelayo, Johan Jansoon, Serapheim Dimitropoulos, and Ioan Raicu, studies what are the Many-Task Computing (MTC) programming mechanisms to take advantages of the massively parallel features of current hardware accelerators for the particular target of MTC. Also, the hardware features of the two dominant many-core platforms (NVIDIA GPUs and Intel Xeon Phi) are also analyzed for our specific framework. This study consisted of comparing the time consumed for computing in parallel several tasks one by one (the whole computational resources are used just to compute one task at a time) with the time consumed for computing in parallel the same set of tasks simultaneously (the whole computational resources are used for computing the set of tasks at very same time). Finally, both software-hardware scenarios were compared to identify the most relevant computer features in each of our many-core architectures. We would like to thank the editorial board of SCPE and reviewers for their effort and time, which is very appreciated.

Extending Amdahl’s law and Gustafson’s law by evaluating interconnections on multi-core processors

Multicore chips are emerging as the mainstream solution for high performance computing. Generally, communication overheads cause large performance degradation in multi-core collaboration. Interconn...

Java in the High Performance Computing arena: Research, practice and experience

The rising interest in Java for High Performance Computing (HPC) is based on the appealing features of this language for programming multi-core cluster architectures, particularly the built-in networking and multithreading support, and the continuous increase in Java Virtual Machine (JVM) performance. However, its adoption in this area is being delayed by the lack of analysis of the existing programming options in Java for HPC and thorough and up-to-date evaluations of their performance, as well as the unawareness on current research projects in this field, whose solutions are needed in order to boost the embracement of Java in HPC.This paper analyzes the current state of Java for HPC, both for shared and distributed memory programming, presents related research projects, and finally, evaluates the performance of current Java HPC solutions and research developments on two shared memory environments and two InfiniBand multi-core clusters. The main conclusions are that: (1) the significant interest in Java for HPC has led to the development of numerous projects, although usually quite modest, which may have prevented a higher development of Java in this field; (2) Java can achieve almost similar performance to natively compiled languages, both for sequential and parallel applications, being an alternative for HPC programming; (3) the recent advances in the efficient support of Java communications on shared memory and low-latency networks are bridging the gap between Java and natively compiled applications in HPC. Thus, the good prospects of Java in this area are attracting the attention of both industry and academia, which can take significant advantage of Java adoption in HPC.

Reusable software components for accelerator-based clusters

Abstract The emerging accelerator-based heterogeneous clusters, comprising specialized processors such as the IBM Cell and GPUs, have exhibited excellent price to performance ratio as well as high energy-efficiency. However, developing and maintaining software for such systems is fraught with challenges, especially for modern high-performance computing (HPC) applications that can benefit the most from leveraging accelerators. If accelerator-based clusters are to deliver on their initial promise to provide a viable and cost-effective HPC solution to researchers and practitioners, one must find a software solution to lower the barrier to entry for the average user. In this paper, we investigate how a software component based approach can be used to provide a reusable and adaptable architecture for executing HPC tasks on accelerator-based clusters. In our implementation, we leverage the lessons from the software engineering research for component-based layered architectures. Our results indicate that the complexity of developing and maintaining accelerator-based cluster software can be as effectively tamed by solid software engineering approaches as that of software in more traditional domains. Specifically, we were able to reuse 83.6% of our implementation code across different architectures and resource configurations, while achieving the overall execution performance only 1.5% off that of an optimally hand-tuned, albeit non-reusable version.

A dynamic and scalable performance monitoring toolkit for cluster and grid environments

The low-cost and availability of network of workstations have made them an attractive solution for high performance computing. Striking progress of network technology is enabling high-performance global computing in which large sets of diverse and geographically distributed resources are transparently and coordinately employed to solve large-scale problems. In this paper, Visuel toolkit for performance measurement and analysis of parallel applications in cluster and grid environments is presented. It is a web-based interface designed not only to provide dynamic monitoring view and management information, but also performance activity of all computing nodes of computing platform involved in the execution of a parallel application. Evaluations using this toolkit show the effectiveness in easing the process of investigation and implementation of parallel applications.

Advancing High Performance Computing solutions for neuroinformatics applications

Event Abstract Back to Event Advancing High Performance Computing solutions for neuroinformatics applications Andrey Semin1* 1 Intel, Germany Neuroinformatics applications for many years are among the most demanding for computation performance, and scientists foresee continued demands for large computational capacity for wide range of medical and life sciences applications. However there are number of challenges in software and hardware development that needed to be solved in order to provide scientists with scalable supercomputers capable to deliver high sustained performance for large-scale simulations. We first review the history of supercomputer hardware developments, and then outline trends highlighting limiting factors which designers foresee on the way of building large scale supercomputers, usually involved power and cooling requirements of the supercomputers, their size and cost. Though hardware difficulties can be overcome with continued technology development and we’ll likely see continued increase in the peak performance of large-scale systems, but it is important that these supercomputers continue deliver high sustained performance for challenging scientific applications. And this is where the software starts paying the bigger role. By software we understand not just the application itself, but the whole stack of software, starting with programming paradigm and language, spanning to middleware and system software. Many of the previously developed software paradigms and programming languages have not been designed to take the full advantage of hardware of the future. Thus many companies have started research and development of new programming languages and paradigms better suited for the supercomputers we’ll likely see in several years, so we devote significant part of discussion to the software aspects of supercomputing, and impacts that these software paradigm shifts have on software development practices. Conference: Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009. Presentation Type: Oral Presentation Topic: Workshop 4 - High performance computing and grid infrastructure for neuroinformatics applications Citation: Semin A (2019). Advancing High Performance Computing solutions for neuroinformatics applications. Front. Neuroinform. Conference Abstract: Neuroinformatics 2009. doi: 10.3389/conf.neuro.11.2009.08.134 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 11 Jun 2009; Published Online: 09 May 2019. * Correspondence: Andrey Semin, Intel, Munich, Germany, andrey.semin@intel.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Andrey Semin Google Andrey Semin Google Scholar Andrey Semin PubMed Andrey Semin Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Performance modeling and prediction of nondedicated network computing

The low cost and wide availability of networks of workstations have made them an attractive solution for high performance computing. However, while a network of workstations may be readily available, these workstations may be privately owned and the owners may not want others to interrupt their priority in using the computer. Assuming machine owners have a preemptive priority, in this paper, we study the parallel processing capacity of a privately owned network of workstations. A mathematical model is developed to predict performance for nondedicated network computing. It also considers systems with heterogeneous machine utilization and heterogeneous service distribution. This model separates the influence of machine utilization, sequential job service rate, and parallel task allocation on the parallel completion time. It is simple and valuable for guiding task scheduling in a nondedicated environment.

A scalable high-performance computing solution for networks on chips

The Eclipse network-on-a-chip architecture uses a sophisticated parallel programming model, realized through multithreaded processors, interleaved memory modules, and a high-capacity interconnection network to support system-on-a-chip designs.

PARMON: a portable and scalable monitoring system for clusters

Workstation/PC clusters have become a cost-effective solution for high performance computing. C-DAC's PARAM 10000 (or OpenFrame, internal code name) is a large cluster of high-performance workstations interconnected through low-latency and high bandwidth networks. The management and control of such a huge system is a tedious and challenging task since workstations/PCs are typically designed to work as a standalone system rather than part of a cluster. We have designed and developed a tool called PARMON that allows effective monitoring and control of large clusters. It supports the monitoring of critical system resource activities and their utilization at three different levels: entire system, node and component level. It also allows the monitoring of multiple instances of the same component; for instance, multiple processors in SMP type cluster nodes. PARMON is a portable, flexible, interactive, scalable, location-transparent, and comprehensive environment based on client–server technology. The major components of PARMON are parmon-server—system resource activities and utilization information provider and parmon-client—a GUI based client responsible for interacting with parmon-server and users for data gathering in real-time and presenting information graphically for visualization. The client is developed as a Java application and the server is developed as a multithreaded server using C and POSIX/Solaris threads since Java does not support interfaces to access system internals. PARMON is regularly used to monitor PARAM 10000 supercomputer, a cluster of 48+ Ultra-4 workstations powered by the Solaris operating system. The recent popularity of Beowulf-class clusters (dedicated Linux clusters) in terms of price–performance ratio has motivated us to port PARMON to Linux (accomplished by porting system dependent portions of parmon-server). This enables management/monitoring of both Solaris and Linux-based clusters (federated clusters) through a single user interface. Copyright © 2000 John Wiley & Sons, Ltd.