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Optimize code for multi-core processors with Intel's Parallel Studio Parallel programming is rapidly becoming a'must-know'skill for developers. Yet, where to start? This teach-yourself tutorial is an ideal starting point for developers who already know Windows C and C++ and are eager to add parallelism to their code. With a focus on applying tools, techniques, and language extensions to implement parallelism, this essential resource teaches you how to write programs for multicore and leverage the power of multicore in your programs. Sharing hands-on case studies and real-world examples, the authors examine the challenges of each project and show you how to overcome them. Explores conversion of serial code to parallel Focuses on implementing Intel Parallel Studio Highlights the benefits of using parallel code Addresses error and performance optimization of code Includes real-world scenarios that illustrate the techniques of advanced parallel programming situations Parallel Programming with Intel Parallel Studio dispels any concerns of difficulty and gets you started creating faster code with Intel Parallel Studio.

This is the first in a new series of books presenting research results and developments concerning the theory and applications of parallel computers, including vector, pipeline, array, fifth/future generation computers, and neural computers.All aspects of high-speed computing fall within the scope of the series, e.g. algorithm design, applications, software engineering, networking, taxonomy, models and architectural trends, performance, peripheral devices.Papers in Volume One cover the main streams of parallel linear algebra: systolic array algorithms, message-passing systems, algorithms for parallel shared-memory systems, and the design of fast algorithms and implementations for vector supercomputers.

Parallel Substitution Algorithm (PSA) is a new model for distributed (cellular) computations. It provides a concise mapping of distributed computation processes into cellular arrays. A PSA is specified by a set of parallel substitutions operating over a cellular array.Two concepts make PSA a powerful tool for modelling cellular computations: 1) naming functions which allow the specification of any type of interactions in the computation space, 2) a context which serves to represent control of a computational process in time.The foundation of PSA theory comprises validity conditions of computations in the synchronous and asynchronous modes, space-time, space-space (2D ⇒ 3D) and global-local equivalent transformations of PSAs, composition and decomposition of PSAs and interpretation of PSAs with automata nets.On the basis of the PSA theory, a variety of tools and techniques is developed for designing algorithmic-oriented cellular VLSI and optical architectures.