Is Parallel Programming Hard, And, If So, What Can You Do About It?
by Paul E. McKenney
Number of pages: 413
The purpose of this book is to help you understand how to program shared-memory parallel machines without risking your sanity. By describing the algorithms and designs that have worked well in the past, we hope to help you avoid at least some of the pitfalls that have beset parallel projects.
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by Guy Blelloch - The MIT Press
Vector Models for Data-Parallel Computing describes a model of parallelism that extends and formalizes the Data-Parallel model on which the Connection Machine and other supercomputers are based. It presents many algorithms based on the model.
by Norm Matloff - University of California, Davis
This book is aimed more on the practical end of things, real code is featured throughout. The emphasis is on clarity of the techniques and languages used. It is assumed that the student is reasonably adept in programming and linear algebra.
by Hank Dietz - The Aggregate
This document discusses the basic approaches to parallel processing available to Linux users: SMP Linux systems, clusters of networked Linux systems, parallel execution using multimedia instructions, and attached processors hosted by a Linux system.
by C. Bischof, at al. - John von Neumann Institute for Computing
The book gives an overview of the developments, applications and future trends in high performance computing for all platforms. It addresses all aspects of parallel computing, including applications, hardware and software technologies.