Concurrent Programming

Are you maximizing the processing power in your multi-core computer systems? With this resource, you discover how to do exactly that, while gaining insight into concurrent programming with tasks to c

This 1997 book is a self-contained tutorial on Z, a formal notation for modelling, specifying and designing computer systems and software, for experienced professionals and serious students in programming and software engineering. It presents realistic case studies emphasising safety-critical systems, with examples drawn from embedded controls, real-time and concurrent programming, computer graphics, games, text processing, databases, artificial intelligence, and object-oriented programming. It motivates the use of formal methods and discusses practical issues concerning how to apply them in real projects. It also teaches how to apply formal program derivation and verification to implement Z specifications in real programming languages with examples in C. The book includes exercises with solutions, reference materials, and a guide to further reading.

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

Clojure is a relatively new version of Lisp that runs on the Java Virtual Machine. It's a functional language, which means that it's ideal for the growing need for concurrent programming and for speci

Learn You Some Erlang for Great Good! is a hilariously illustrated guide to the concurrent functional programming language you've heard so much about. You'll start out with the basics and learn all ab

Examines iOS application development, discussing the fundamentals of concurrent programming, debugging techniques, and adding augmented reality experiences in game and social media apps.

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

A programmer's manual for creating a variety of iPhone applications covers such topics as core data, peer-to-peer networking, MapKit, Web file access, concurrent programming, and debugging.

The Constraint Handling Rules (CHR) language is a declarative concurrent committed-choice constraint logic programming language consisting of guarded rules that transform multisets of relations called

Concurrency provides a thoroughly updated approach to the basic concepts and techniques behind concurrent programming. Concurrent programming is complex and demands a much more formal approach than se

Master the essentials of concurrent programming,including testing and debuggingThis textbook examines languages and libraries for multithreaded programming. Readers learn how to create threads in Java

This book gives applications of the theory of process algebra, or Algebra of Communicating Processes (ACP), that is the study of concurrent or communicating processes studied using an algebraic framework. The approach is axiomatic; the authors consider structures that are some set of mostly equational axioms, which are equipped with several operators. Thus the term 'algebra' is used in the model-theoretic sense. The axiomatic approach enables one to organize the field of process theories. The theory is applied systematically to a number of situations, including systolic algorithms, semantics of an object-oriented language, and protocols. It will be welcomed by computer scientists working in parallel programming.

Starting from the premise that understanding the foundations of concurrentprogramming is key to developing distributed computing systems, this book first presents thefundamental theories of concurrent

"Concurrent Aggregates" addresses a key problem in programming languages for massively parallel machines: how to write programs that are both modular and highly concurrent. It describes a novel approa

本書是一本Java2物件導向相關技術專題的入門書，書中從Java語言的入門語法及物件導向觀念特質開始介紹，然後進入「再利用—reuse技術」的Design Pattern課題，到「並行性—concurrent programming」，以及「Multithread—多執行緒」等專題，嘗試從Java程式設計的角度，說明如何提高軟體品質的概念和技巧。期望讀者將來正式使用Java語言開發程式系統時，本書

"Concurrent Prolog" brings together for the first time descriptions of the major concurrent logic programming languages proposed so far for future parallel computer systems. In particular, it describe

Fifteen original contributions from experts in high-speed computation on multi-processor architectures, concurrent programming and parallel algorithms. Experts in high-speed computation agree that the