Prof. Alan Kaminsky
Rochester Institute of Technology — Department of Computer Science
Copyright © 2016 by Alan Kaminsky.
In the twenty-first century, scientists and engineers are tackling the world's
toughest computational problems with parallel computing. Using multiple
processor cores running simultaneously, parallel computers are solving these
problems in less time and with greater accuracy than ever before. Even desktop
PCs nowadays are powerful parallel computers. To take full advantage of the
capabilities of these machines, programmers must learn to write parallel
BIG CPU, BIG DATA teaches you how to write parallel programs for multicore machines, compute clusters, GPU accelerators, and big data map-reduce jobs, in the Java language, with the free, easy-to-use, object-oriented Parallel Java 2 Library. The book also covers how to measure the performance of parallel programs and how to design the programs to run as fast as possible.
With 39 years of industrial and academic computing experience, Alan Kaminsky has been teaching parallel computing since 2004. He is a professor in the Department of Computer Science at the Rochester Institute of Technology.
Read the Preface and Table of Contents.
Order the book from CreateSpace.com. Also available from other online booksellers.
To reduce costs, the book is printed in black and white. An archive of the book's full-color illustrations in PNG format is available.
(Note: Pre-publication versions of the book dated August 2015 or earlier were free, Creative Commons licensed. The published book is not free and is not licensed under the Creative Commons license or any other license.)
|Part II. Tightly Coupled Multicore||
Part III. Loosely Coupled Cluster
Chapter 14. Massively Parallel
Chapter 15. Hybrid Parallel
Chapter 17. Cluster Parallel Loops
Chapter 18. Cluster Parallel Reduction
Chapter 19. Cluster Load Balancing
Chapter 20. File Output on a Cluster
Chapter 21. Interacting Tasks
Chapter 22. Cluster Heuristic Search
Chapter 23. Cluster Work Queues
Part IV. GPU Acceleration
Chapter 29. GPU Heuristic Search
– Class WV
– WV.cu structure
– Interface KnapsackProblem
– Class KnapsackSC
– Class SubsetSum
– Class WalkSackGpu
– WalkSackGpu.cu kernel
Part V. Big Data
Chapter 32. Big Data Analysis