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 G.C. Fox, R.D. Williams, P.C. Messina - Morgan Kaufmann Publishers
A clear illustration of how parallel computers can be successfully applied to large-scale scientific computations. The book demonstrates how various applications in physics, biology and other sciences were implemented on real parallel computers.
by Sergio Barbarossa, Stefania Sardellitti, Paolo Di Lorenzo - arXiv
We consider the problems of distributed detection and estimation in wireless sensor networks. We provide a general framework aimed to show how an efficient design of a sensor network requires a joint organization of in-network communication.
by Alan Kaminsky - Rochester Institute of Technology
With the book BIG CPU, BIG DATA, my goal is to teach you how to write parallel programs that take full advantage of the vast processing power of modern multicore computers, compute clusters, and graphics processing unit (GPU) accelerators.
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.