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Accelerating MATLAB with GPU Computing: A Primer with Examples by [Suh, Jung W., Kim, Youngmin]
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Accelerating MATLAB with GPU Computing: A Primer with Examples 1st Edition, Kindle Edition

4.0 out of 5 stars 1 customer review

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Review

"This truly is a practical primer. It is well written and delivers what it promises. Its main contribution is that it will assist “naive” programmers in advancing their code optimization capabilities for graphics processing units (GPUs) without any agonizing pain."--Computing Reviews,July 2 2014

"Suh and Kim show graduate students and researchers in engineering, science, and technology how to use a graphics processing unit (GPU) and the NVIDIA company's Compute Unified Device Architecture (CUDA) to process huge amounts of data without losing the many benefits of MATLAB. Readers are assumed to have at least some experience programming MATLAB, but not sufficient background in programming or computer architecture for parallelization."--ProtoView.com, February 2014

From the Back Cover

Beyond simulation and algorithm development, many developers increasingly use MATLAB even for product deployment in computationally heavy fields. This often demands that MATLAB codes run faster by leveraging the distributed parallelism of Graphics Processing Units (GPUs). While MATLAB successfully provides high-level functions as a simulation tool for rapid prototyping, the underlying details and knowledge needed for utilizing GPUs make MATLAB users hesitate to step into it.Accelerating MATLAB with GPUs offers a primer on bridging this gap.

Starting with the basics, setting up MATLAB for CUDA (in Windows, Linux and Mac OS X) and profiling, it then guides users through advanced topics such as CUDA libraries. The authors share their experience developing algorithms using MATLAB, C++ and GPUs for huge datasets, modifying MATLAB codes to better utilize the computational power of GPUs, and integrating them into commercial software products. Throughout the book, they demonstrate many example codes that can be used as templates of C-MEX and CUDA codes for readers’ projects. Download example codes from the publisher's website: http://booksite.elsevier.com/9780124080805/


Product details

  • Format: Kindle Edition
  • File Size: 5076.0 KB
  • Print Length: 238 pages
  • Publisher: Morgan Kaufmann; 1 edition (18 Nov. 2013)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • ASIN: B00GWMD6JW
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Enhanced Typesetting: Enabled
  • Average Customer Review: 4.0 out of 5 stars 1 customer review
  • Amazon Bestsellers Rank: #1,668,209 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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5 December 2014
Format: Paperback|Verified Purchase
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Amazon.com: 3.7 out of 5 stars 6 reviews
Björn Skatt
4.0 out of 5 starsGood primer
6 February 2014 - Published on Amazon.com
Verified Purchase
3 people found this helpful.
Vladimir Gorokhovsky
5.0 out of 5 starsFive Stars
23 February 2015 - Published on Amazon.com
Format: Paperback|Verified Purchase
One person found this helpful.
Chicago Cyclist
1.0 out of 5 starsPoor follow through by author.
20 February 2017 - Published on Amazon.com
Format: Paperback|Verified Purchase
One person found this helpful.
Buyer
4.0 out of 5 starsWorth it!
1 January 2014 - Published on Amazon.com
Format: Paperback
5 people found this helpful.
Josephine Bentham
5.0 out of 5 starsthe operation is also very convenient.
6 June 2017 - Published on Amazon.com
Format: Paperback
One person found this helpful.
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