Topics covered by this book include: facilitating management, debugging, migration, and disaster recovery through virtualization; clustered systems for research or ecommerce applications; designing systems as web services; and social networking systems using peer-to-peer computing. The principles of cloud computing are discussed using examples from open-source and commercial applications, along with case studies from the leading distributed computing vendors such as Amazon, Microsoft, and Google. Each chapter includes exercises and further reading, with lecture slides and more available online.
This book will be ideal for students taking a distributed systems or distributed computing class, as well as for professional system designers and engineers looking for a reference to the latest distributed technologies including cloud, P2P and grid computing.Complete coverage of modern distributed computing technology including clusters, the grid, service-oriented architecture, massively parallel processors, peer-to-peer networking, and cloud computing Includes case studies from the leading distributed computing vendors: Amazon, Microsoft, Google, and more Explains how to use virtualization to facilitate management, debugging, migration, and disaster recovery Designed for undergraduate or graduate students taking a distributed systems course—each chapter includes exercises and further reading, with lecture slides and more available online
The 50 revised full papers presented together with abstracts of 11 invited contributions were carefully reviewed and selected. The papers are organized in topical sections on Corss Grid, Par Sim, application using MPI and PVM, parallel algorithms using message passing, programming tools for MPI and PVM, implementations of MPI and PVM, extensions of MPI and PVM, and performance analysis and optimization.
The papers in this volume cover a broad range of research topics presented in four groups. The first one introduces cluster tools and techniques, especially the issues of load balancing and migration. Another six papers deal with grid and global computing including grid infrastructure, tools, applications and mobile computing. The next nine papers present general questions of distributed development and applications. The last four papers address a crucial issue in distributed computing: fault tolerance and dependable systems.
This volume will be useful to researchers and scholars interested in all areas related to parallel and distributed computing systems.
The 41 revised full papers presented were carefully selected during two rounds of reviewing and revision. Also included are six invited papers and introductory chapter surveys. The papers are organized in sections on eigenvalue problems and solutions of linear systems; computational fluid dynamics, structural analysis, and mesh partitioning; computing in education; computer organization, programming and benchmarking; image analysis and synthesis; parallel database servers; and nonlinear problems.
Researchers at the Forefront Reveal Results from Their Own State-of-the-Art Work
Edited by some of the top researchers in the field and with contributions from a variety of international experts, Scientific Computing with Multicore and Accelerators focuses on the architectural design and implementation of multicore and manycore processors and accelerators, including graphics processing units (GPUs) and the Sony Toshiba IBM (STI) Cell Broadband Engine (BE) currently used in the Sony PlayStation 3. The book explains how numerical libraries, such as LAPACK, help solve computational science problems; explores the emerging area of hardware-oriented numerics; and presents the design of a fast Fourier transform (FFT) and a parallel list ranking algorithm for the Cell BE. It covers stencil computations, auto-tuning, optimizations of a computational kernel, sequence alignment and homology, and pairwise computations. The book also evaluates the portability of drug design applications to the Cell BE and illustrates how to successfully exploit the computational capabilities of GPUs for scientific applications. It concludes with chapters on dataflow frameworks, the Charm++ programming model, scan algorithms, and a portable intracore communication framework.
Explores the New Computational Landscape of Hybrid Processors
By offering insight into the process of constructing and effectively using the technology, this volume provides a thorough and practical introduction to the area of hybrid computing. It discusses introductory concepts and simple examples of parallel computing, logical and performance debugging for parallel computing, and advanced topics and issues related to the use and building of many applications.
The 25 revised full papers presented in this book were carefully reviewed and selected from 60 submissions. The papers cover the following topics: Autotuning and Thread Mapping; Data Locality and Decomposition; Scalable Applications; Machine Learning; Datacenters andCloud; Communication Runtime; Intel Xeon Phi; Manycore Architectures; Extreme-scale Computations; and Resilience.
The lack of portability and interoperability between cloud platforms at different service levels is the main issue affecting cloud-based services today. The brokering, negotiation, management, monitoring and reconfiguration of cloud resources are challenging tasks for developers and users of cloud applications due to the different business models associated with resource consumption, and to the variety of services and features offered by different cloud providers.
In chapter 1 the concepts of cloud portability and interoperability are introduced, together with the issues and limitations arising when such features are lacking or ignored. Subsequently, chapter 2 provides an overview of the state-of-the-art methodologies and technologies that are currently used or being explored to enable cloud portability and interoperability. Chapter 3 illustrates the main cross-platform cloud APIs and how they can solve interoperability and portability issues. In turn, chapter 4 presents a set of ready-to-use solutions which, either because of their broad-scale use in cloud computing scenarios or because they utilize established or emerging standards, play a fundamental part in providing interoperable and portable solutions. Lastly, chapter 5 presents an overview of emerging standards for cloud Interoperability and portability.
Researchers and developers of cloud-based services will find here a brief survey of the relevant methodologies, APIs and standards, illustrated by case studies and complemented by an extensive reference list for more detailed descriptions of every topic covered.
This book provides an overview of the ongoing academic research, development, and uses of heterogeneous parallel and distributed computing in the context of scientific computing. Presenting the state of the art in this challenging and rapidly evolving area, the book is organized in five distinct parts:
Heterogeneous Platforms: Taxonomy, Typical Uses, and Programming Issues
Performance Models of Heterogeneous Platforms and Design of¿Heterogeneous Algorithms
Performance: Implementation and Software
High Performance Heterogeneous Computing is a valuable¿reference for researchers and practitioners in the area of high performance heterogeneous computing. It also serves as an excellent supplemental text for graduate and postgraduate courses in related areas.
The 101 papers presented were carefully reviewed and improved during two rounds of reviewing and revision. The book offers topical sections on distributed and grid architectures, scheduling and load balancing, performance analysis and prediction, parallel non-numerical algorithms, parallel programming, tools and environments, parallel numerical algorithms, applications, and evolutionary computing and neural networks.
The book begins with a summary of the nontechnical aspects of interviewing, such as common mistakes, strategies for a great interview, perspectives from the other side of the table, tips on negotiating the best offer, and a guide to the best ways to use EPI.
The technical core of EPI is a sequence of chapters on basic and advanced data structures, searching, sorting, broad algorithmic principles, concurrency, and system design. Each chapter consists of a brief review, followed by a broad and thought-provoking series of problems. We include a summary of data structure, algorithm, and problem solving patterns.