Logo

BIG CPU, BIG DATA: Solving the World's Toughest Problems with Parallel Computing

Small book cover: BIG CPU, BIG DATA: Solving the World's Toughest Problems with Parallel Computing

BIG CPU, BIG DATA: Solving the World's Toughest Problems with Parallel Computing
by

Publisher: Rochester Institute of Technology
Number of pages: 424

Description:
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.

Home page url

Download or read it online for free here:
Download link
(12MB, PDF)

Similar books

Book cover: Distributed Systems for Fun and ProfitDistributed Systems for Fun and Profit
by - mixu.net
This text provides a more accessible introduction to distributed systems. The book brings together the ideas behind many of the more recent distributed systems - such as Amazon's Dynamo, Google's BigTable and MapReduce, Apache's Hadoop and so on.
(11912 views)
Book cover: Programming on Parallel MachinesProgramming on Parallel Machines
by - 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.
(8750 views)
Book cover: Parallel Programming with Microsoft .NETParallel Programming with Microsoft .NET
by - Microsoft Press
A book that introduces .NET programmers to patterns for including parallelism in their applications. Examples of these patterns are parallel loops, parallel tasks and data aggregation with map-reduce. Each pattern has its own chapter.
(14688 views)
Book cover: A Framework for Enabling Distributed Applications on the InternetA Framework for Enabling Distributed Applications on the Internet
by - arXiv
Internet distributed applications (IDAs) are internet applications with which many users interact simultaneously. In this paper the author provides a basis for a framework that combines IDAs collectively within a single context.
(8666 views)