BIG CPU, BIG DATA: Solving the World's Toughest Problems with Parallel Computing
by Alan Kaminsky
Publisher: Rochester Institute of Technology 2015
Number of pages: 424
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.
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by Earl T. Campbell, Joseph Fitzsimons - arXiv
This review provides a gentle introduction to one-way quantum computing in distributed architectures. One-way quantum computation shows significant promise as a model for distributed systems, particularly probabilistic entangling operations.
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 Henri Casanova, et al. - CRC Press
This book provides a rigorous yet accessible treatment of parallel algorithms, including theoretical models of parallel computation, parallel algorithm design for homogeneous and heterogeneous platforms, complexity and performance analysis, etc.
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.