Networking and Parallel/ Distributed Computing

This book gathers 14 of the most promising papers presented at the 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computi

Topics in Parallel and Distributed Computing provides resources and guidance for those learning PDC as well as those teaching students new to the discipline.The pervasiveness of computing devices cont

This book constitutes the refereed proceedings of the First International Conference on Advances in Parallel, Distributed Computing Technologies and Applications, PDCTA 2011, held in Tirunelveli, Indi

From the leading minds in the field, Distributed and Cloud Computing is the first modern, up-to-date distributed systems textbook. Starting with an overview of modern distributed models, the book expo

This book constitutes the refereed proceedings of the International conference on Parallel and Distributed Computing and Networks, PDCN 2011, held in Chongqing, China, in December 2010. The 19 revised

This book constitutes the refereed proceedings of the 11th International Conference on Distributed Computing and Networking, ICDCN 2010, held in Kolkata, India, during January 3-6, 2010. There were 16

The practical, real-world guide to Java networking and distributed component development.-- In-depth coverage includes servlets, RMI, Java IDL, CORBA, JavaMail, and much more.-- Packed with examples -

This book constitutes the refereed proceedings of the 21st International Conference on Parallel and Distributed Computing, Euro-Par 2015, held in Vienna, Austria, in August 2015. The 51 revised full p

This book constitutes the refereed proceedings of the 14th International Conference on Distributed Computing and Networking, ICDCN 2013, held in Mumbai, India, during January 3-6, 2013. The 27 revised

This book constitutes the refereed proceedings of the 13th International Conference on Distributed Computing and Networking, ICDCN 2012, held in Hong Kong, China, during January 3-6, 2012.The 36 revis

This book constitutes the refereed proceedings of the 10th International Conference on Distributed Computing and Networking, ICDCN 2009, held in Hyderabad, India, during January 3-6, 2009.The 20 paper

Advancements in microprocessor architecture, interconnection technology, and software development have fueled rapid growth in parallel and distributed computing. However, this development is only of p

This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algo

The emphasis of this text is on data networking, internetworking and distributed computing issues. The material surveys recent work in the area of satellite networks, introduces certain state-of-the-a

In this text, students of applied mathematics, science and engineering are introduced to fundamental ways of thinking about the broad context of parallelism. The authors begin by giving the reader a deeper understanding of the issues through a general examination of timing, data dependencies, and communication. These ideas are implemented with respect to shared memory, parallel and vector processing, and distributed memory cluster computing. Threads, OpenMP, and MPI are covered, along with code examples in Fortran, C, and Java. The principles of parallel computation are applied throughout as the authors cover traditional topics in a first course in scientific computing. Building on the fundamentals of floating point representation and numerical error, a thorough treatment of numerical linear algebra and eigenvector/eigenvalue problems is provided. By studying how these algorithms parallelize, the reader is able to explore parallelism inherent in other computations, such as Monte Carlo