Read e-book online Accelerating MATLAB with GPU Computing: A Primer with PDF

By Jung W. Suh, Youngmin Kim

ISBN-10: 0124080804

ISBN-13: 9780124080805

Past simulation and set of rules improvement, many builders more and more use MATLAB even for product deployment in computationally heavy fields. This usually calls for that MATLAB codes run speedier through leveraging the disbursed parallelism of pics Processing devices (GPUs). whereas MATLAB effectively offers high-level capabilities as a simulation device for speedy prototyping, the underlying information and information wanted for using GPUs make MATLAB clients hesitate to step into it. Accelerating MATLAB with GPUs deals a primer on bridging this gap.

Starting with the fundamentals, constructing MATLAB for CUDA (in home windows, Linux and Mac OS X) and profiling, it then courses clients via complex subject matters equivalent to CUDA libraries. The authors percentage their event constructing algorithms utilizing MATLAB, C++ and GPUs for big datasets, editing MATLAB codes to raised make the most of the computational energy of GPUs, and integrating them into advertisement software program items. through the publication, they exhibit many instance codes that may be used as templates of C-MEX and CUDA codes for readers' initiatives. obtain instance codes from the publisher's site: http://booksite.elsevier.com/9780124080805/

• indicates easy methods to speed up MATLAB codes during the GPU for parallel processing, with minimum knowledge
• Explains the comparable heritage on undefined, structure and programming for ease of use
• offers basic labored examples of MATLAB and CUDA C codes in addition to templates that may be reused in real-world tasks

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Lecture Notes in Business Information Processing, vol. 48, pp. 391–392. Springer, Heidelberg (2010) 7. : Management Challenges to Implementing Agile Processes in Traditional Development Organizations. IEEE Software, 30–39 (2005) 8. : Lean Software Development: An Agile Toolkit. Addison-Wesley, Reading (2003) 9. com/ 10. : Implementing Lean Software Development: From Concept to Cash. Addison-Wesley, Reading (2006) 11. : Complexity vs. Lean, the Big Showdown. In: Lean Software and Systems Conference 2010, Helsinki (2010) 12.

One year after starting the agile transformation, the Developer team was most advanced in all the practices with 100% utilization of the basic practices [28] and over 40% of the more technical. Based on this result the Developer team is above the Intermediate level of agile adoption. This is an expected result as the agile practices are well suited for the programming tasks. SLV team implemented well the Team level and Daily work practices having almost 100% utilization of all the basic practices.

Pino acknowledges to the University of Cauca where he works as Assistant Professor. References 1. : CMMI or Agile: Why Not Embrace Both! CMU/SEI-2008-TN-003 (2008) 2. : Using Scrum to Guide the Execution of Software Process Improvement in Small Organizations. Journal of Systems and Software 83(10), 1662–1677 (2010) 3. : Empirical studies of agile software development: A systematic review. Inform. Softw. Technol. (2008) 4. : Agile software development, the people factor. Computer 34(11), 131–133 (2001) 5.

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Accelerating MATLAB with GPU Computing: A Primer with Examples by Jung W. Suh, Youngmin Kim


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