Accelerating MATLAB with GPU Computing: A Primer with by Jung W. Suh, Youngmin Kim 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 quicker by way of leveraging the disbursed parallelism of pics Processing devices (GPUs). whereas MATLAB effectively offers high-level capabilities as a simulation software for quick prototyping, the underlying information and data 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, establishing MATLAB for CUDA (in home windows, Linux and Mac OS X) and profiling, it then publications clients via complicated themes resembling CUDA libraries. The authors proportion their adventure constructing algorithms utilizing MATLAB, C++ and GPUs for enormous 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 e-book, they display many instance codes that may be used as templates of C-MEX and CUDA codes for readers' tasks. obtain instance codes from the publisher's site: http://booksite.elsevier.com/9780124080805/

• indicates how you can speed up MATLAB codes in the course of the GPU for parallel processing, with minimum knowledge
• Explains the similar heritage on undefined, structure and programming for ease of use
• presents uncomplicated labored examples of MATLAB and CUDA C codes in addition to templates that may be reused in real-world tasks

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Extra info for Accelerating MATLAB with GPU Computing: A Primer with Examples

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The fact that param will be a new object motivates the rules that govern how T is deduced from expr: 1. As before, if expr’s type is a reference, ignore the reference part. 2. If, after ignoring expr’s reference-ness, expr is const, ignore that, too. If it’s volatile, also ignore that. (volatile objects are uncommon. They’re generally used only for implementing device drivers. ) Hence: int x = 27; const int cx = x; const int& rx = x; // as before // as before // as before f(x); // T's and param's types are both int f(cx); // T's and param's types are again both int f(rx); // T's and param's types are still both int Note that even though cx and rx represent const values, param isn’t const.

Instead, it returns a brand new object. The whys and hows of this situation are explored in 24 | Item 3 Item 6, but what’s important here is that the type returned by a container’s opera tor[] depends on the container. decltype makes it easy to express that. Here’s a first cut at the template we’d like to write, showing the use of decltype to compute the return type. The template needs a bit of refinement, but we’ll defer that for now: template auto authAndAccess(Container& c, Index i) -> decltype(c[i]) { authenticateUser(); return c[i]; } // works, but // requires // refinement The use of auto before the function name has nothing to do with type deduction.

Item 4 | 35 CHAPTER 2 auto In concept, auto is as simple as simple can be, but it’s more subtle than it looks. Using it saves typing, sure, but it also prevents correctness and performance issues that can bedevil manual type declarations. Furthermore, some of auto’s type deduc‐ tion results, while dutifully conforming to the prescribed algorithm, are, from the perspective of a programmer, just wrong. When that’s the case, it’s important to know how to guide auto to the right answer, because falling back on manual type declarations is an alternative that’s often best avoided.

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


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