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Written as a step-by-step guide, Getting Started with Hazelcast will teach you all you need to know to make your application data scalable. This book is a great introduction for Java developers, software architects, or developers looking to enable scalable and agile data within their applications. You should have programming knowledge of Java and a general familiarity with concepts like data caching and clustering.

A guide to advanced features of MPI, reflecting the latest version of the MPI standard, that takes an example-driven, tutorial approach. This book offers a practical guide to the advanced features of the MPI (Message-Passing Interface) standard library for writing programs for parallel computers. It covers new features added in MPI-3, the latest version of the MPI standard, and updates from MPI-2. Like its companion volume, Using MPI, the book takes an informal, example-driven, tutorial approach. The material in each chapter is organized according to the complexity of the programs used as examples, starting with the simplest example and moving to more complex ones. Using Advanced MPI covers major changes in MPI-3, including changes to remote memory access and one-sided communication that simplify semantics and enable better performance on modern hardware; new features such as nonblocking and neighborhood collectives for greater scalability on large systems; and minor updates to parallel I/O and dynamic processes. It also covers support for hybrid shared-memory/message-passing programming; MPI_Message, which aids in certain types of multithreaded programming; features that handle very large data; an interface that allows the programmer and the developer to access performance data; and a new binding of MPI to Fortran.

According to many social thinkers it is not possible to quantify the performance of organizations on the basis of the values produced. One initial reply to this critique is that the axiological approach in systems theory aims to fulfil a dual function. On one side, it takes a whole set of universal reference values into consideration which in the end spur human motivation and action justifying life in society, among them the own survival of Homo sapiens which could be in danger today, On the other side, this book proposes to measure this axiological efficiency in operational statistical terms and consequently looking for verifiable results. Therefore, the first aim of this book is to present, define and measure a new concept of "organisational efficiency" which is not limited to known economic aspects or related to neoliberal premises or other ideological misconceptions. On the contrary, for the authors organisational efficiency must address the entire system of values, projected or attained. Duly substantiated criticism can and must be levelled only against any society's or organisation's system of values. More specifically, the texts hereunder the seven works in this book constitute a preliminary attempt to set up an operational quantitative methodology for that purpose. The second aim is to introduce different approaches to measure efficiency applied to specific problems within organisations. On the whole, all the articles identify a number of ways of addressing organisational efficiency, providing a better understanding and critique of this concept.

The major research results from the Scalable Input/Output Initiative, exploring software and algorithmic solutions to the I/O imbalance.As we enter the "decade of data," the disparity between the vast amount of data storage capacity (measurable in terabytes and petabytes) and the bandwidth available for accessing it has created an input/output bottleneck that is proving to be a major constraint on the effective use of scientific data for research. Scalable Input/Output is a summary of the major research results of the Scalable I/O Initiative, launched by Paul Messina, then Director of the Center for Advanced Computing Research at the California Institute of Technology, to explore software and algorithmic solutions to the I/O imbalance. The contributors explore techniques for I/O optimization, including: I/O characterization to understand application and system I/O patterns; system checkpointing strategies; collective I/O and parallel database support for scientific applications; parallel I/O libraries and strategies for file striping, prefetching, and write behind; compilation strategies for out-of-core data access; scheduling and shared virtual memory alternatives; network support for low-latency data transfer; and parallel I/O application programming interfaces.

Introduction to Parallel Computing, 2e provides a basic, in-depth look at techniques for the design and analysis of parallel algorithms and for programming them on commercially available parallel platforms. The book discusses principles of parallel algorithms design and different parallel programming models with extensive coverage of MPI, POSIX threads, and Open MP. It provides a broad and balanced coverage of various core topics such as sorting, graph algorithms, discrete optimization techniques, data mining algorithms, and a number of other algorithms used in numerical and scientific computing applications.

Parallel algorithms Made Easy

The complexity of today's applications coupled with the widespread use of parallel computing has made the design and analysis of parallel algorithms topics of growing interest. This volume fills a need in the field for an introductory treatment of parallel algorithms—appropriate even at the undergraduate level, where no other textbooks on the subject exist. It features a systematic approach to the latest design techniques, providing analysis and implementation details for each parallel algorithm described in the book. Introduction to Parallel Algorithms covers foundations of parallel computing; parallel algorithms for trees and graphs; parallel algorithms for sorting, searching, and merging; and numerical algorithms. This remarkable book:

• Presents basic concepts in clear and simple terms
• Incorporates numerous examples to enhance students' understanding
• Shows how to develop parallel algorithms for all classical problems in computer science, mathematics, and engineering
• Employs extensive illustrations of new design techniques
• Discusses parallel algorithms in the context of PRAM model
• Includes end-of-chapter exercises and detailed references on parallel computing.

This book enables universities to offer parallel algorithm courses at the senior undergraduate level in computer science and engineering. It is also an invaluable text/reference for graduate students, scientists, and engineers in computer science, mathematics, and engineering.

This book provides an in-depth study concerning a claqss of problems in the general area of load sharing and balancing in parallel and distributed systems. The authors present the design and analysis of load distribution strategies for arbitrarily divisible loads in multiprocessor/multicomputer systems subjects to the system constraints in the form of communication delays. In particular, two system architecture-single-level tree or star network, and linear network-are thoroughly analyzed.
The text studies two different cases, one of processors with front-ends and the other without. It concentrates on load distribution strategies and performance analysis, and does not cover issues related to implementation of these strategies on a specific system. The book collates research results developed mainly by two groups at the Indian Institute of Science and the State University of New York at Stony Brook. It also covers results by other researchers that have either appeared or are due to appear in computer science literature. The book also provides relevant but easily understandable numerical examples and figures to illustrate important concepts. It is the first book in this area and is intended to spur further research enabling these ideas to be applied to a more general class of loads. The new methodology introduced here allows a close examination of issues involving the integration of communication and computation. In fact, what is presented is a new "calculus" for load sharing problems.

Foreword by Bjarne Stroustrup Software is generally acknowledged to be the single greatest obstacle preventing mainstream adoption of massively-parallel computing. While sequential applications are routinely ported to platforms ranging from PCs to mainframes, most parallel programs only ever run on one type of machine. One reason for this is that most parallel programming systems have failed to insulate their users from the architectures of the machines on which they have run. Those that have been platform-independent have usually also had poor performance.Many researchers now believe that object-oriented languages may offer a solution. By hiding the architecture-specific constructs required for high performance inside platform-independent abstractions, parallel object-oriented programming systems may be able to combine the speed of massively-parallel computing with the comfort of sequential programming.Parallel Programming Using C++ describes fifteen parallel programming systems based on C++, the most popular object-oriented language of today. These systems cover the whole spectrum of parallel programming paradigms, from data parallelism through dataflow and distributed shared memory to message-passing control parallelism.For the parallel programming community, a common parallel application is discussed in each chapter, as part of the description of the system itself. By comparing the implementations of the polygon overlay problem in each system, the reader can get a better sense of their expressiveness and functionality for a common problem. For the systems community, the chapters contain a discussion of the implementation of the various compilers and runtime systems. In addition to discussing the performance of polygon overlay, several of the contributors also discuss the performance of other, more substantial, applications.For the research community, the contributors discuss the motivations for and philosophy of their systems. As well, many of the chapters include critiques that complete the research arc by pointing out possible future research directions. Finally, for the object-oriented community, there are many examples of how encapsulation, inheritance, and polymorphism can be used to control the complexity of developing, debugging, and tuning parallel software.

Parallel distributed processing is transforming the field of cognitive science. Microcognition provides a clear, readable guide to this emerging paradigm from a cognitive philosopher's point of view. It explains and explores the biological basis of PDP, its psychological importance, and its philosophical relevance.

In quantum computing, and because all of the states of the quantum system can exist simultaneously, all of the paths of the quantum computations tree from the root to the leaves occur in parallel and only after measurement a single path will be observed as the whole system's composite state will collapse into that single path. From a computation perspective, each path in the tree of quantum computing is a single processing, and thus a massive computational parallelism exists with massive number of calculations performed simultaneously. Systolic devices provide inexpensive but massive calculation power, and are cost-effective, high-performance, and special-purpose systems that have wide range of implementations such as in solving several regular and compute-bound problems containing repetitive multiple operations on large arrays of data. This book presents research in the study of parallel computing.

Computational clusters have long provided a mechanism for the acceleration of high performance computing (HPC) applications. With today's supercomputers now exceeding the petaflop scale, however, they are also exhibiting an increase in heterogeneity. Thisheterogeneity spans a range of technologies, from multiple operating systems to hardware accelerators and novel architectures. Because of the exceptional acceleration some of these heterogeneous architectures provide, they are being embraced as viable tools for HPC applications. Given the scale of today's supercomputers, it is clear that scientists must consider the use of fault-tolerance in their applications. This is particularly true as computational clusters with hundreds and thousands of processors become ubiquitous in large-scale scientific computing, leading to lower mean-times-to-failure. This forces the systems to effectively deal with the possibility of arbitrary and unexpected node failure. In this book the address the issue of fault-tolerance via checkpointing. They discuss the existing strategies to provide rollback recovery to applications -- both via MPI at the user level and through application-level techniques. Checkpointing itself has been studied extensively in the literature, including the authors' own works. Here they give a general overview of checkpointing and how it's implemented. More importantly, they describe strategies to improve the performance of checkpointing, particularly in the case of distributed systems.

The latest techniques and principles of parallel and grid database processing The growth in grid databases, coupled with the utility of parallel query processing, presents an important opportunity to understand and utilize high-performance parallel database processing within a major database management system (DBMS). This important new book provides readers with a fundamental understanding of parallelism in data-intensive applications, and demonstrates how to develop faster capabilities to support them. It presents a balanced treatment of the theoretical and practical aspects of high-performance databases to demonstrate how parallel query is executed in a DBMS, including concepts, algorithms, analytical models, and grid transactions. High-Performance Parallel Database Processing and Grid Databases serves as a valuable resource for researchers working in parallel databases and for practitioners interested in building a high-performance database. It is also a much-needed, self-contained textbook for database courses at the advanced undergraduate and graduate levels.

The field of parallel and distributed computing is undergoing changes at a breathtaking pace. Networked computers are now omnipresent in virtually every application, from games to sophisticated space missions. The increasing complexity, heterogeneity, largeness, and dynamism of the emerging pervasive environments and associated applications are challenging the advancement of the parallel and distributed computing paradigm. Many novel infrastructures have been or are being created to provide the necessary computational fabric for realising parallel and distributed applications from diverse domains. New models and tools are also being proposed to evaluate and predict the quality of these complicated parallel and distributed systems. Current and recent past efforts, made to provide the infrastructures and models for such applications, have addressed many underlying complex problems and have thus resulted in new tools and paradigms for effectively realising parallel and distributed systems. This book showcases these novel tools and approaches with inputs from relevant experts.

Presenting the main recent advances made in parallel processing, this volume focuses on the design and implementation of systolic algorithms as efficient computational structures that encompass both multiprocessing and pipelining concepts.;While the architecture of present-day parallel supercomputers is largely based on the concept of a shared memory, with its attendant limitations of common access, advances in semiconductor technology have led to the development of highly parallel computer architectures with decentralized storage and limited connections in which each processor possesses high bandwidth local memory connected to a small number of neighbours. Systolic arrays are a typical and highly efficient example of such architectures, enabling cost effective, high-speed parallel processing for large volumes of data, with ultra-high throughput rates. Algorithms suitable for implementation on systolic arrays find applications in areas such as signal and image processing, pattern matching, linear algebra, recurrence algorithms and graph problems.