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ISBN : B004IPPIP0
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Author Peter Pacheco uses a tutorial approach to show students how to develop effective parallel programs with MPI, Pthreads, and OpenMP. The first undergraduate text to directly address compiling and running parallel programs on the new multi-core and cluster architecture, An Introduction to Parallel Programming explains how to design, debug, and evaluate the performance of distributed and shared-memory programs. User-friendly exercises teach students how to compile, run and modify example programs.
Key features:
Takes a tutorial approach, starting with small programming examples and building progressively to more challenging examplesFocuses on designing, debugging and evaluating the performance of distributed and shared-memory programsExplains how to develop parallel programs using MPI, Pthreads, and OpenMP programming modelsDirect download links available for Free An Introduction to Parallel Programming [Kindle Edition]
- File Size: 3516 KB
- Print Length: 392 pages
- Publisher: Morgan Kaufmann; 1 edition (February 17, 2011)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B004IPPIP0
- Text-to-Speech: Enabled
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- Lending: Not Enabled
- Amazon Best Sellers Rank: #319,944 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
Free An Introduction to Parallel Programming
This book is a good start for anyone that had done quite a bit of programming and is confident in their abilities to program traditional non-parallel programs and wants to learn parallel programming.
Good intro and review of material you will have learned in any computer architecture course in first chapters and reasons why you should learn parallel programming.
In later chapters you learn about task-parallelism vs data-parallelism in learning to rewrite serial program for parallel. The two main types of parallel systems, shared-memory, and distributed-memory are covered and explained.
MPI is used for the distributed-memory programming. Pthreads and OpenMP is used for the Shared-Memory programming examples. Examples like the Trapezoid Rule and sorting algorithms that lend themselves to parallel programming are demonstrated using MPI, Pthreads and OpenMP. There is also the pi approximation example using alternating series that I remember coding in my first programming course and wondering why it was so slow. It's used in the book to warn of the dangers of improperly scoping variables in parallel blocks. MPI does seem to be favored over the other two for some reason that I didn't get though. OpenMP seems to be the easiest to use and works out of the box on Windows, Linux, and Mac OS X with little to no extra work too so I don't know why the author didn't start off with OpenMP first. You'll need to be running Linux to be able to easily follow along with all the examples in the book if you are a hands on person like myself and want to actually compile and run all the example yourself. On Windows,to build MPI programs,you need the Windows HPC Server 2008 SDK installed. You can run the Pthreads examples on the Mac too but on Windows you'll need to install the Unix for Windows.
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