Parallel programming for mobile computing

Heterogeneous Distributed Computing (HDC) system consists of Workstations (WSs) and Personal Computers (PCs). In HDC system, each WS/PC may have different processor and performance. In order to take advantage of this diversity of processing power of a system, an effective task partitioning, scheduling, and load balancing are needed to get the optimum performance. This paper examines the effectiveness of task partitioning and scheduling strategies for image (raytracing) processing application on HDC system. The static and dynamic/Run-time Task Scheduling (RTS) strategies are shown inadequate for balancing the load of HDC system. Two adaptive tasks scheduling strategies are proposed for HDC image computing system. These adaptive strategies are: i) Master Initiate Sub-task size (MIS) strategy (based on centralized resources management approach), and ii) Worker Initiated Sub-task size (WIS) strategy (based on semi-decentralized resources management approach). The measured results show that the adaptive (MIS & WIS) strategies dramatically improve the performance of HDC raytracing system and remedy the defects of static and RTS strategies. Performances of the all investigated strategies are evaluated on manager/master and workers model of HDC system. In performance comparisons of MIS and WIS strategies, the WIS strategy shows a slightly better speedup and scalability over MIS strategy.

The overall goal of this research is to improve the 'environment awareness' aspect of personal autonomic computing. Personal computing offers unique challenges for self-management due to its multiequipment, multisituation, and multiuser nature. The aim is to develop a support architecture for multiplatform working, based on autonomic computing concepts and techniques. Of particular interest is collaboration among personal systems to take a shared responsibility for environment awareness. Concepts mirroring human mechanisms, such as 'reflex reactions' and the use of 'vital signs' to assess operational health, are used in designing and implementing the personal computing architecture. A proof of concept self-healing tool is considered and lessons learned used for the requirements specification of the community-based environment awareness prototype environment - PAC-MEN (personal autonomic computing monitor environment).

Real challenges to suppress undesirable fluid-excited acoustics are posed by a wide variety of engineering disciplines. Noise regulations, passenger comfort and component stability are motivators which are continuing to stimulate substantial efforts towards the understanding of aeroacoustic phenomena, and not least to quantify the usability (practicability and value) of traditional and advanced prediction methods. The latter is the primary focus of this thesis, particularly as applied to the transportation industries, aerospace, automotive and rail. Nowadays Computational Fluid Dynamics (CFD) is a tool well integrated into the industrial development and production life-cycles. This is possible now because of two main factors: the increase in the performance of relatively cheap personal computers and network facilities, and the progress made in general purpose CFD software between modeling complexity and practicability within the industrial environment. While CFD methodologies are well established for lots of applications such as aerodynamics, heat exchange, etc., aeroacoustic CFD simulations still represent a challenge, in particular their industrial practicability. In these years this has given rise to heavy investments by the automotive industry in international aeroacoustics consortia, whereby all the major car companies work together to study the limitations and advantages of aeroacoustics CFD. The general aim of these consortia is to develop methodologies which fit into, and improve upon, current design processes. The goal of the present work is to explore the multitude of different CFD modeling approaches for some typical industrial problems such as: cavity noise, vortex shedding noise, propeller and jet noise. Each of these problems has a particular mechanism for noise generation and different methods have been studied and tested, in order to develop and optimize a practical methodology for the analysis of each problem type. Furthermore each of the aeroacoustics problems considered are representative of a variety of industrial applications. Cavity noise is at the origin of phenomena such as sun-roof buffeting in convertibles or door-gap tonal noise. Vortex shedding noise is typical of any flows involving bluff bodies such as automobile antennas or aircraft landing gear. Propeller noise is typical to applications involving rotating machinery, such as fans, pumps and turbines. Different approaches ranging from steady and transient RANS simulations with the acoustic analogy (including porous and solid surface formulations), to Computational Aero Acoustics (CAA) and Large Eddy Simulation (LES) type computations have been studied and applied. Classic theories already exist to predict aerodynamically generated noise, which are both computationally and economically less expensive than CFD methods. However aeroacoustics CFD is the future, beginning as a promising present, for the following reasons:  Industries are interested in modeling complex geometries.  Many classic theories can be applied successfully but very often restrictions exist with respect to the configuration and flow conditions. For example, classic propeller theories cannot be used to model real-world configurations such as a propeller installed on a wing with some prescribed yaw or angle of attack.  The progress of all other Computer Aided Design and Engineering tools, such as linear or non-linear structural codes, are driving design towards a virtual multi-physics approach for the simulation of complex geometries. Due to previous experience and the wide availability of modeling options, it was decided to use the general purpose CFD software package ANSYS FLUENT for CFD investigations in this study.

Power density and energy dissipation of digital IC's has become one of the main concerns during the recent years. With the increased usage of battery powered devices, ubiquitous computing, and increase in implantable biomedical applications, enhancing energy efficiency of digital systems is one of the key research areas in digital IC design. For applications with a low demand on throughput, sub-threshold digital operation is one of the promising techniques for ultra-low energy operation. Moreover, global energy minimum operating point of a digital static CMOS circuit, if exists, is in the sub-threshold regime; thus, realizing global minimum energy operation. This doctoral dissertation presents different energy efficiency enhancement methods for sub-threshold digital CMOS circuits. First, a high level sub-threshold energy model is developed for rapid characterization of digital circuits. Model accuracy is validated with measurements of a circuit that was fabricated in 0.18 µm process, and with simulations for smaller feature sized technologies. Second, this model is applied to compare the energy efficiency of synchronous and asynchronous circuits. It is shown that with a suitable external completion detection mechanism, energy efficiency of asynchronous circuits in the sub-threshold regime is better than the synchronous counterparts. Process selection to minimize energy dissipation is investigated. Moreover, it is shown that with the correct choice of process options, migrating to a smaller feature sized technology increases the energy efficiency of a circuit. Architectural modifications such as parallelism, pipelining, and folding are also explored and applied to reduce energy dissipation. Finally, a current sensing completion detection system is proposed and implemented for sub-threshold asynchronous circuits. Design flows for de-synchronization of synchronous circuits are presented for various cases with detailed explanations of sub-blocks of the completion detection system. As a proof of concept example, a self-timed cardiac event detector is implemented in a 65 nm CMOS process.

All computing platforms, from mobile to supercomputers, are becoming more and more heterogeneous and massively parallel. While they can provide higher power efficiency and computation throughput, effective and confident use of these systems always requires knowledge about low-level programming. The average time necessary to develop and optimize a design on heterogeneous platforms is higher and higher compared to typical homogeneous systems. Dataflow models of computation (MoC) are quickly becoming the common practice in heterogeneous systems development. In domains such as signal processing and multimedia communication, dataflow MoCs have become accepted as standard. However, the shift from a sequential and architecture-specific MoC to a dataflow MoC still uncovers several programming and development challenges. The Cal Actor Language (CAL) is a recently-specified dataflow and actor-based language capable of concisely expressing complex and general purpose parallel applications. However, design tools supporting this language are generally not adequate to fully exploit its features and expressiveness power. In fact, they generally restrict its MoC in order to reduce the design space exploration (DSE) effort. The objective of this thesis is to provide a DSE methodology where all the features of CAL and dynamic dataflow MoCs can be exploited in a more general and effective manner. This dissertation illustrates a novel profiling, analysis and performance estimation methodology for the DSE of dynamic dataflow programs. The main research contributions of this thesis are: the formalization of a graph-based representation of the program execution called an execution trace graph (ETG); the formalization of a systematic methodology for profiling generic dynamic dataflow programs through their code interpretation; the formalization of a complete DSE methodology for dynamic dataflow programs in order to efficiently identify close-to-optimal design points according to various and tailored performance merit functions. In particular, the following design space optimization problems for dynamic dataflow programs are addressed: the analysis of the hotspots and the algorithmic bottlenecks of a parallel program; the bounding and optimization of the buffer size configuration for complex designs; the dynamic power dissipation minimization of programs implemented in multi-clock domain architecture. Furthermore, theoretical concepts like the design space critical path and the potential speedup of a dataflow application have been defined and revisited, respectively. The thesis also presents a DSE framework developed in order to demonstrate the effectiveness of this design methodology.

Mainly designed for simulating the fluid motion, Smooth Particle Hydrodynamics (SPH) SPH has been widely applied to many fields, such as aerospace aircraft design and ship design. First, this paper presents an implementation of serial SPH method algorithm using the reflection method as the boundary condition. This paper describes entire process of SPH and gives detailed descriptions of some important procedures. Second, this paper realizes the corresponding parallel algorithm of SPH, which includes neighbor particle searching and building, physical attributes computation and other main SPH method procedures. Third, details of mapping different parts of the SPH serial algorithm to parallel algorithm on GPU with CUDA C as parallel programming language are also shown in this paper. Taking advantage of powerful computation ability of GPU, the experiment approximately achieves a 8X speedup that shows the high efficiency of GPU-based programming. The parallel algorithm not only achieves a high performance, but also promises a reliability result with a small error rate as the experiment result shows.

Stephen J. Hartley Oxford University Press, New York, 1998, 260 pp. ISBN 0-19-511315-2, $45.00 Concurrent Programming is a thorough treatment of Java multi-threaded programming for both a stand-alone and distributed environment. Designed mostly for students in concurrent or parallel programming classes, the text is also an excellent reference for the practicing professional developing multi-threaded programs or applets. Hartley first provides a complete explanation of the features of Java necessary to write concurrent programs, including topics such as exception handling, interfaces, and packages. He then gives the reader a solid background to write multi-threaded programs and also presents the problems introduced when writing concurrent programs—namely race conditions, mutual exclusion, and deadlock. Hartley also provides several software solutions that do not require the use of common process and thread mechanisms. Once the groundwork is laid for writing concurrent programs, Hartley then takes a different approach than most Java references. Rather than presenting how Java handles mutual exclusion with the synchronized keyword (although it is covered later), he first looks at semaphore-based solutions to classic concurrent problems such as bounded-buffer, readers-writers, and the dining philosophers. Hartley also uses the same approach to develop Java classes for monitors and message passing. This unique approach to introducing concurrency allows the readers to both understand how Java threads are synchronized and how the basic synchronization mechanism can be used to construct more abstract tools such as semaphores. If there is a shortcoming with the text it is with the lack of sufficient coverage of remote method invocation (RMI), although there is a section covering RMI. This is quite understandable as RMI is a fairly recent phenomenon with the Java community. Also, the classes that Hartley provides could easily implement RMI rather than sockets to handle communication. The strengths of the book include its ease in reading, several examples at the end of chapters, a package similar to Xtango that provides algorithm animation, and a supportive web site by the author (see www.mcs.drexel.edu/~shartley/ConcProgJava/index.html ) including compressed source code. As Java becomes more dominant on the server side of multi-tier applications, writing thread-safe concurrent applications becomes even more important. Concurrent Programming is a strong step towards teaching students and professionals such skills. Greg Gagne, Westminster College of Salt Lake City Salt Lake City, Utah

i»? A. A. Helal, A. A. Heddaya, and B. B. Bhargrava Kluwer Academic Publishers, Boston, 1996, 176 pp. ISBN 0-7923-9800-9, $118.50 Computing in the 1990s has reached the state of distributed computing. A basis of this form of computing is a distributed computing system, which is built on the following three components: (a) personal computers, (b) local and fast wide area networks, and (c) system and application software. By amalgamating computers and networks into one single computing system and providing an appropriate system software, a distributed computing system has created the possibility of sharing information and peripheral resources. Furthermore, these systems improved performance of a computing system and individual users through parallel execution of programs, load balancing and sharing, and replication of programs and data. Distributed computing systems are also characterized by enhanced availability, and increased reliability. Replication is the key to providing high availability, fault tolerance, and enhanced performance in a distributed computing system. As companies move toward systems that are more open and distributed, replication is becoming increasingly important in the ability to provide data and services that are current, correct and available, which is a key factor in maintaining a competitive advantage over rivals. However, replication has also generated some serious challenges. For example, when a replica is updated, how do we propagate such an update to other replicas? If multiple replicas are updated simultaneously and some of the updates conflict one another, how do we resolve the conflict? How do we deal with the situation where a replica is down and then subsequently recovered? How do we deal with network partitions? Not surprisingly, considerable research efforts have been directed towards the solution of these challenges. However, most of the research results are scattered among many journals, conference proceedings, theses, and technical reports. The book by Helal, Heddaya and Bhargava has gathered the best material of these research results and formed a coherent collection that includes definitions, theoretical background, algorithms, annotated bibliographies of commercial and experimental prototype systems, and more than 200 references. This book classifies the entities that can be replicated on a distributed computing system into the following categories: (a) data, (b) process, (c) objects, and (d) messages. After the introduction on the goals and main approaches of replication techniques, the book uses individual chapters to describe major methods for each of the above replication categories. The main focus of this part, however, is on the replication of data. The book also contains two chapters on the replication issues in heterogeneous, mobile, and large-scale systems, and on the future of replication techniques. Apart from the normal chapters, the book contains a rich set of appendices that are very useful for further studies of replication techniques. These appendices include brief descriptions of two dozen commercial and experimental systems that use replication techniques, annotated bibliographies of selected literature on various topics of replication, and an introduction on serializability theory. Each annotated bibliography contains an overview of the specific topic and is written by an invited expert in that field. The combination of the normal chapters and appendices cover the entire spectrum of replication, from definitions, concepts, theories, algorithms, techniques, to systems. The book serves as an excellent introduction and roadmap to the issues of replication. Although it does not provide a detailed description of every replication techniques and systems, the book covers most of the fundamental work that allows the reader to understand the roots of these techniques and systems, and to find the ways to search for the details. This book is a valuable reference book for anyone studying replication techniques or implementing replication systems, specifically, it can be very useful to post-graduate students, practitioners, and beginning researchers of this exciting field. Wanlei Zhou, School of Computing and Mathematics, Deakin University

This special issue is based on a collection of selected papers presented at the Fifth International Conference on Advanced Computing held in Chennai (Madras), India in December 97. The International Conference on Advanced Computing is organized annually in India by the Advanced Computing Society (ACS) which is a premier professional society set up in India to cater to the broad interests in the area of Advanced Computing. One of the significant activities of the Society is to organize regular Meetings/Conferences/Symposia. The Conferences are held annually in India during December. Out of the shortlisted papers presented at the Conference held in Chennai (Madras) during December 97, the following six papers were finally selected after a second round of detailed review of the papers. The first paper On designing and implementing collaborative system using Java-RMI by Rajeev R. Raje, Snehasis Mukhopadhyay, Michael Boyles and Nila Patel describe the use of Java-RMI in designing a collaborative information classifier. The principles of RMI, the details of the prototype, and some preliminary results are presented. The paper primarily concentrates on web-based computing paradigms. In the second paper A framework for performance-based program partitioning , Ram Subramanian and Santosh Pande present an efficient and accurate performance model based program-partitioning approach for parallel architectures. The interactions between computation and communication are captured through behavioral edges . The approach yields significant performance improvement. The third paper DDSCHED: a distributed dynamic real-time scheduling algorithm by Anup K. Bhattacharjee, K. Ravindranath, R. Mall and A. Pal presents a dynamic soft real-time scheduling algorithm for distributed systems. The algorithm is based on a local scheduler and a distributed scheduling scheme. The algorithm takes care of the network delays assuming that the communication protocol is CSMA/CD. In the fourth paper Generic operational decomposition for concurrent systems semantics and reflection , Annie Marcoux, Christine Maurel, Frederic Migeon and Patrick Salle discuss about the reflection in concurrent languages, particularly in actor-based languages. The main objective of the paper is to extend concurrent systems to reflection. The authors present a modular and dynamic description of entity operational management, and extend the decomposition to obtain reflective entities. The fifth paper Parallel programming using visual patterns by A. S. M. Sajeev proposes a two level approach to parallel programming within the context of a visual environment. The first level is the specification of patterns encapsulating process communation and synchronization, and the second level is the use of patterns for parallel programming. The approach encourages reuse of patterns. In the sixth paper An application framework for dynamic distributed software architectures , G. R. Ribeiro-Justo and P. R. F. Cunha present an object-oriented application framework for developing evolving (reconfigurable) software architectures for distributed systems. The application framework is called C++CL. Such a requirement is encountered in banking, industrial control and telecommunications applications. We are extremely thankful to the following reviewers who have helped us in reviewing the papers submitted for this special issue: Lawrence Jenkins N. Mohan Ram Nishit Narang Rajeev Shorey Sameer Adhikari K. Balaji K. Rajan Sumit Kasera Subhrajyothi Bhar Pankaj Sethi Ravindranath Kokku Guest Editors: L. M. Patnaik, Ravi Mittal and K. M. Mehta

Recent trends have led hardware manufacturers to place multiple processing cores on a single chip, making parallel programming the intended way of taking advantage of the increased processing power. However, bringing concurrency to average programmers is considered to be one of the major challenges in computer science today. The difficulty lies not only in writing correct parallel programs, but also in achieving the required efficiency and performance. For parallel programs, performance is not only about obtaining low execution times on a fixed number of cores, but also about maintaining efficiency as the number of available cores is increased. Ideally, programmers should have in their toolkit techniques that can be used when designing parallel programs, before any code is available for testing on production hardware, and which are then able to predict scalability once the program is implemented. Existing methods are either unreliable at predicting scalability, such as the case of disjoint-access parallelism, or do not apply to lock-based programs, which currently make up a large part of existing concurrent programs. Furthermore, using some of these techniques is so complicated that it outweighs the time required to implement, debug and test the program on real hardware. In this thesis we study the problem of predicting the scalability of concurrent programs without implementing them. This allows programmers in the design phase of a concurrent algorithm to choose only one or a few promising solutions that will be implemented, debugged and tested on production hardware. We first consider disjoint-access parallelism, an existing property that applies only to a very restricted class of programs. After an extensive practical evaluation spanning across a variety of scenarios, we find it to be ineffective at predicting scalability. For predicting the scalability of more general concurrent algorithms, we propose the obstruction degree, a new scalability metric based on the consistency requirements of algorithms. It applies to programs using locks, invalidation primitives and transactional memory. Our metric allows programmers to compare two given algorithms as well as predict their scalability limit, the maximum number of processors to which they can scale, thus allowing programmers to choose the appropriate size hardware for running their programs. We also examine the composition of relaxed memory transactions in order to combine the ease of programming offered by transactional memory with the increased scalability of transactions that circumvent the traditional transactional model. We present outheritance, a property we show to be both necessary and sufficient for ensuring the correct composition of relaxed transactions, and we show how to calculate the obstruction degree of compositions that use this new property. We use outheritance to build OE-STM, a new software transactional memory algorithm having elastic transactions that correctly compose.

At first thought, one may not realize the need for a special issue of Parallel and Distributed Computing Practices focused on Cluster Computing when our community has already invested years of research and spent a wealth of resources on traditional Big Iron supercomputers, for instance, the SGI/Cray Origin and IBM SP. Academia, industry, and national laboratories are still using, and for the foreseeable future, will continue to use, supercomputers to solve both grand-challenge scale and high-throughput applications. Then why the interest and new popularity of research into clusters for high-performance parallel and distributed applications? Clusters incorporate the tremendous successes that are attributable to our parallel and distributed processing community. Over the next decade, clusters will span the entire range of high-performance computing platforms. Instead of being restricted solely to supercomputers for achieving high-performance computing, the spectrum of available platforms will spread out to Beowulf-class clusters, to superclusters, and on to supercomputers. Moving along this continuum represents increasing computational resources, which in turn allows the capability for solving larger classes of applications or instances of problems. Compute-bound applications on workstations often may take advantage of the extra horsepower provided by symmetric multiprocessors. Applications with even larger computational requirements and a high-degree of concurrency may use Beowulf clusters, that is, clusters that are home-built from mass-market, commercial off-the-shelf workstations and networks, for instance, PC's interconnected by Fast Ethernet. A supercluster may contain commodity SMP nodes interconnected with scalable, low-latency, high-bandwidth networks, and is integrated by a system vendor rather than the computational scientist. Applications with greater complexity, for example, grand challenges from the sciences, demand scalable systems with high-performance, interconnects, perhaps coupled with terascale I/O subsystems, and thus, are appropriate for these superclusters. Finally, supercomputers, the largest of the clusters, may provide the ultimate platform for the highest-fidelity models and simulations and data-intensive applications. At each step up this ladder of cluster architectures, the system cost increases approximately by an order of magnitude, but having the high-performance capability to solve the problem of interest may require an entry point at any of these levels, the Beowulf, or supercluster, or even supercomputer platform. Cluster computing with Beowulf- or superclusters, thus, may be one or two orders of magnitude less expensive than a supercomputer, and provide a cost-effective solution and capability that was not available on a workstation. But this cost comparison measures just the basic system price. What about the applications? As recently as a few years ago, the lack of a canonical high-performance architectural paradigm meant that migrating to a new computer system, even from the same vendor, typically required redesigning applications to efficiently use the high-performance system. Thus, the application must be redesigned with parallel algorithms and libraries that are optimized for each high-performance system. This process of porting, redesigning, optimizing, debugging, and analyzing, the application may in fact be prohibitively expensive, and certainly frustrating when the process is completed just in time to greet the next generation system! That was then, this is now. With the successful standardization of system-level interfaces, many of these issues no longer hinder our expedited use of these new, high-performance clusters. The message-passing interface (MPI) is a clear example of a standard that has contributed significantly to the success of cluster computing. Supercomputer vendors working together with the high-performance computing community embarked on a process to standardize the user-level interface and behavior for passing messages between concurrent tasks. Now, using ordinary sequential compilers and vendor-supplied or freely-available implementations of this message-passing interface, applications can be implemented efficiently on one supercomputer and ported with little or no effort to other parallel machines. The message-passing paradigm, thus, provides a reasonably good canonical high-performance model and methodology for implementing high-performance applications. An algorithm that exhibits efficient execution and scaling on one machine then is likely to perform well on another with little or no additional programming effort. Using this cluster computing paradigm, algorithms typically are optimized by minimizing the number of remote memory accesses (reducing the amount of messages passed), and carefully organizing the data placement to increase memory locality. Thus, high-performance algorithms and applications that have been optimized for supercomputers, using the same cluster paradigm, may be appropriate for Beowulf and superclusters. Cluster computing, realizing the importance of a standard high-performance programming environment that applies equally well to both ends of the architectural spectrum, highly leverages these and other successes from parallel and distributed processing research and development. This is in an exciting, new era for high-performance computing where the canonical architectural paradigm stretches from home-built Beowulf clusters, to superclusters, and on to supercomputers. But cluster computing faces some challenges ahead of it. Continued research is required for designing 1) scalable, high-performance algorithms, applications, and methodologies, that can tolerate the communication overheads, and adapt to hybrid, SMP, and heterogeneous, nodes; 2) high-performance computer architectures, scalable interconnection networks, and I/O subsystems; and 3) scalable systems software, compilers, and monitoring tools. Cluster computing has come of age, and it is high time for the parallel and distributed processing community to lay the groundwork for our own legacy of success stories. David A. Bader University of New Mexico

In 1998, Ian Foster and Carl Kesselman, together with thirty distinguished experts in high-performance computing and networking, laid the foundations of a new computing model called GRID. Their vision was introduced in the book, The GRID: Blueprint for a New Computing Infrastructure (see review, SCPE Vol. 3, No. 3 ). The editors wrote: … A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities… allowing new classes of applications to emerge… as yet, we have only a preliminary understanding of what these new applications will look like. Over the next five years, Grid computing became the hottest R&D field. Academia and national labs built experimental infrastructures, new startups were established to develop Grid technologies, and the HPC giant vendors joined the party. Each one pushed in a different direction based on what and how he believed Grid computing should look. It was only a matter of time until people started to ask what the Grid is all about. A survey of ten Grid experts produced ten different answers. The question remains without a clear answer, leaving many people confused. Ian Foster picked up the gloves. In his article, What is the GRID? A three point checklist (GRIDtoday, July 22, 2002: Vol. 1, No. 6), he redefines the Grid model to reflect the trades and changes in the field during the past five years. According to Foster, a Grid is a system that: Coordinates resources that are not subject to centralized control. Uses standard, open, general-purpose protocols and interfaces. Delivers a nontrivial quality of service. But Foster's new definition could not encompass all of the Grid's approaches. Moreover, Foster explained by example what the Grid is not: A cluster management system such as Sun's Sun Grid Engine, Platform's Load Sharing Facility, or Viridian's Portable Batch System can, when installed on a parallel computer or local area network, deliver quality of service guarantees and thus constitutes a powerful Grid resource. However, such a system is not a Grid itself, due to its centralized control of the hosts that it manages Within a week the responses started to come in. The first one was from Wolfgang Gentzsch, Director of Grid computing at Sun Microsystems (GRIDtoday, August 5, 2002: Vol. 1, No. 8). Gentzsch wrote: I very much like Ian's two earlier definitions. Combined, they may provide a very generic Grid definition. The new checklist, however, reduces the potential of a wide and rich variety of Grids, with different architectures, operational models, and applications, and may miss the chance to becoming widely accepted as a standard definition of the Grid. Why do we need a definition for the Grid? Can the Grid be defined? Is a definition is necessary to the Grid's development? The Grid is an open vision and thus cannot be defined. An attempt to define the Grid can only reach the foreseen technology horizon, and this horizon is only one to two years ahead. Grid computing is a paradigm in shift. New architectures and standards are changing the shape and direction of the Grid's development. In the past year one such prominent architecture was Open Grid Services Architecture (OGSA). The OGSA is the first attempt to bridge the gap between two cyberspace worlds: the Internet and the Grid. The Internet and the Grid are two different computing models. There are people who believe that the Grid is the next generation of the Internet and the World Wide Web, saying that the Great Global Grid is still many years away. Others state that the Grid will complement, not replace, the Internet as we know it. In 1998, when the Grid blueprint was published, a new Internet language appeared called eXtensible Markup Language (XML). XML is a development tool for a new Internet computing model known as Web Services. At that time, nobody understood that there is a relationship between Web Services and the Grid. Three years past before Steve Tuecke from Argonne National Laboratory started to write a spec for Grid Services. Later, people from IBM joined the mission and together they created OGSA. The OGSA specification outlines interfaces to grid computing software that comply with Web Services standards. If adopted, Grid services such as job scheduling, authentication, failure-detection, staging of applications and data, and migration of applications and data, will all be accessed through standard Web Services architecture. OGSA is basically where Grid meets Web Services and maybe the first step towards full integration of the Internet and the Grid. The Internet appeared a decade ago. In the beginning, the Net offered primitive applications: a mailing system, ftp services, and a web of home pages. Nobody asked, What is the Internet? The years passed and new emerging Internet technologies were developed. Today we can talk on the phone over the Net, chat with friends, and video conference with colleagues on the other side of the world. New computing and business models were developed: e-commerce, e-learning, e-science, and e-you-name-it. And nobody asks what the Internet is. The Grid is a super-model, a bag of networking models associated with many types of architectures, software packages, applications, and standards. Today, when people talk about the Grid they mention Data Grids, Science Grids, and Campus Grids. And at the same time they mention Farm computing, Peer-to-peer computing, and Utility computing, among others. Ten years from now many of today's technologies will disappear and new ones will be developed. I don't know how Grid computing will look a decade from now, but I know that we will have g-commerce, g-learning, g-science, and g-chat. Nobody will ask what the Grid is. It will be obvious, just as the Internet is today. Ami Marowka The Computer Aided Design Laboratory School of Engineering and Computer Science Hebrew University of Jerusalem

Stochastic wave models play a central role in our present-day wave modelling capabilities. They are frequently used to compute wave statistics, to generate boundary conditions and to include wave effects in coupled model systems. Historically, such models were developed to predict the wave field evolution in deep water where the conditions of Gaussianity generally hold. However, in recent decades, such models have been applied to the shallower coastal environment where the stochastic representation of the dominant wave physics becomes questionable. This is primarily due to the increased influence of wave nonlinearity and the additional depth-induced wave processes that are dominant in this region.<br/><br/>Unfortunately, the two most dominant wave processes in the surf zone: depth-induced wave breaking and nonlinear triad wave-wave interactions are also the least well represented and understood. This is due to both their complexity and the scarcity of analytical solutions for realistic wave fields. As such, they represent a significant obstacle in the accurate modelling of the wave dynamics in the coastal region. Providing accurate representations of these wave processes is essential to answering the questions demanded from stochastic wave models from coastal engineers for coastal management and design. Such advancements are necessary to improve our understanding of wave-induced processes, to reduce costs in managing the coastal environment and to tackle contemporary issues such as uncertainties with respect to increased sea level rise.<br/><br/>Due to the complexity of depth-induced wave breaking, a complete representation of this wave process does not exist for both stochastic and deterministic modelling frameworks. Although there is extensive literature on the subject of parameterizing depth-induced wave breaking in a stochastic sense, these parameterizations are inconsistent with theory, observations and (deterministic) model predictions. In particular, present-day modelling defaults perform poorly over (near-)horizontal bathymetries with over-enhanced wave dissipation of locally-generated waves and insufficient dissipation of swell waves. Equally, nonlinear triad wave-wave interactions are poorly represented in stochastic wave models due to the problem of closure and the impractical computational expense of more accurate representations. In particular, the most commonly applied parameterization in the wave literature incorrectly predicts the evolution of the spectral shape, and the convergence to an equilibrium high-frequency tail deep in the surf zone. Correctly resolving these issues is essential for the management of many of the activities occurring at the coast; from the design of coastal defenses to feasibility studies for wave energy converters, from port operation and availability to vessel navigation, from understanding the ecology at the coast to the fisheries, and from managing leisure and tourism to safety at the coast.<br/><br/> In this work, we investigate the process of depth-induced wave breaking through a comprehensive analysis of the literature and a comparison of modelling performance. Here, we use an extensive set of wave observations representing a large range of wave conditions and bathymetric profiles. The analysis demonstrates that no currently available depth-induced breaking source term is capable of sufficiently representing the process of depth-induced wave breaking. This is shown to be in agreement with the wave literature with parameterizations either over-predicting wave dissipation for locally generated waves or under-predicting wave dissipation for non-locally generated waves over (near-)horizontal bathymetries. To address this issue, a new joint scaling using both local wave and bathymetric conditions is proposed. Using both the normalized characteristic wave number and local bottom slope unifies two approaches prevalent in the wave literature. This is shown to improve the model performance for the dissipation of both locally and non-locally generated waves over (near-)horizontal bathymetries. <br/><br/>Furthermore, the validity of the assumption that wave dissipation can be modelled as analogous to a 1D dissipative bore is explored. Subsequently, a heuristic directional modification is introduced for depth-induced wave breaking dissipation models. This directionally partitions the 2D spectrum into several directional partitions that are assumed to be unidirectional. Model results demonstrate that the effect of the directional partitioning is to reduce the dissipation of wave energy and to enhance the significant wave height; in agreement with field measurements. Not only is this modification shown to be applicable to the joint wave breaking parameterization proposed in this study, but also for well-established parameterizations. <br/><br/>The effects of both the proposed scaling and directional modification are then reviewed from an operational context and are compared to state-of-the-art source terms, field observations and a hypothetical storm representative of Dutch design conditions. Such design conditions are expected to be representative of design conditions found globally. In an environment where storm intensities may be increasing, for example due to global warming, the results of wave breaking models near the coast under such extreme conditions become of greater relevance. The influence of wave breaking models in coupled model systems is anticipated to provide important new insights in understanding the various wave-driven processes along our coasts. <br/><br/>Next, the representation of the nonlinear triad wave-wave interactions in stochastic wave models is reviewed. In particular, the collinear approximation used to transform 1D triad source terms for implementation in 2D stochastic wave models is revisited. These approximations are necessitated by considerations of computational efficiency. The conventional collinear approximation is shown to be inconsistent at the unidirectional limit and to be a primary source of modelling error. Instead of converging to the values predicted by the 1D triad source terms at the unidirectional limit, the energy transfers as computed by stochastic wave models are shown to become unbounded. This results in a dimensional calibration coefficient which is at least an order of magnitude smaller than that found in the wave literature. Consequently, for directional wave conditions, 1D triad source terms implemented with the conventional collinear approximation insufficiently capture the wave evolution. To address this problem, a new collinear approximation is presented which accounts for the wave energy contained within a finite directional bandwidth. This collinear approximation is shown to converge correctly at the unidirectional limit and to agree well with predictions from a second-order accurate deterministic wave model. In particular, better agreement is shown in the modelling prediction of the spectral shape and related integral parameters, e.g. wave period, under idealized wave conditions. Under certain conditions, these error reductions are shown to be more significant than differences between the underlying triad models.<br/><br/>The contribution of this work demonstrates that while the underlying theory underpinning stochastic wave modelling in the coastal environment still remains questionable, the accurate determination of wave statistics in the coastal zone is tenable. With the advancements presented in this study, the new source terms correspond better with the current wave literature and are shown to provide significant steps forward over existing default source terms. The developments presented here are anticipated to form the foundation for future source term research, and to be used for the representation of the dominant wave physics in the coastal environment in operational wave models.

The objective of this paper is to compare the power consumption, leakage voltage, leakage current and leakage power of 3-T DRAM while maintaining the competitive performance .The MTCOS and SVL technique is used in 3T DRAM to compare the performance. Multi-threshold CMOS(MTCMOS) is a CMOS chip which has a Transistor with multiple threshold voltage and SVL is a technique which used to reduce leakage during both the active and stand-by mode. DRAM was first invented by Dr. Robert Dennard in the year 1966. It is used in many advanced processor for chip instruction and data memory. It majorly contributes in power dissipation in off-state leakage current. In personal computers it is used as the main memory, workstations etc. The basic advantage of using DRAM is its simplicity in its structure. Keyword- DRAM; FinFET; CMOS; Leakage power; Leakage current; Frequency; Access time; MTCMOS; SVL; USVL; LSVL

The data cube and iceberg cube computation problem has been studied by many researchers. There are three major approaches developed in this direction: (1) top-down computation, represented by MultiWay array aggregation (Zhao et. al., 1997) which utilizes shared computation and performs well on dense data sets; (2) bottom-up computation, represented by BUC (Beyer and Ramakrishnan, 1999), which takes advantage of Apriori Pruning and performs well on sparse data sets; and (3) integrated top-down and bottom-up computation, represented by Star-Cubing (Xin, et. al., 2003), which takes advantages of both and has high performance in most cases. However; the performance of Star-Cubing degrades in very sparse data sets due to the additional cost introduced by the tree structure. None of the three approaches achieves uniformly high performance on all kinds of data sets. In this paper; we present a new approach that compute Iceberg Cubes by factorizing the lattice space according to the frequency of values. This approach, different from all the previous dimension-based approaches where the importance of data distribution is not recognized, partitions the cube lattice into one dense subspace and several sparse subspaces. With this approach, a new method called MM-Cubing has been developed. MM-Cubing is highly adaptive to dense, sparse or skewed data sets. Our performance study shows that MM-Cubing is efficient and achieves high performance over all kinds of data distributions.