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

By Jung W. Suh, Youngmin Kim

Past simulation and set of rules improvement, many builders more and more use MATLAB even for product deployment in computationally heavy fields. This frequently calls for that MATLAB codes run speedier by way of leveraging the allotted parallelism of photographs Processing devices (GPUs). whereas MATLAB effectively offers high-level capabilities as a simulation instrument 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, constructing MATLAB for CUDA (in home windows, Linux and Mac OS X) and profiling, it then courses clients via complicated themes resembling CUDA libraries. The authors proportion their event constructing algorithms utilizing MATLAB, C++ and GPUs for big datasets, editing MATLAB codes to raised make the most of the computational strength of GPUs, and integrating them into advertisement software program items. in the course of the ebook, 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/

• exhibits tips on how to speed up MATLAB codes throughout the GPU for parallel processing, with minimum knowledge
• Explains the similar history on undefined, structure and programming for ease of use
• offers uncomplicated labored examples of MATLAB and CUDA C codes in addition to templates that may be reused in real-world initiatives

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

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Random search is a direct search method as it does not require derivatives to search a continuous domain. 3). 2 Strategy The strategy of Random Search is to sample solutions from across the entire search space using a uniform probability distribution. Each future sample is independent of the samples that come before it. 1 provides a pseudocode listing of the Random Search Algorithm for minimizing a cost function. 1: Pseudocode for Random Search. 4 Heuristics ❼ Random search is minimal in that it only requires a candidate solution construction routine and a candidate solution evaluation routine, both of which may be calibrated using the approach.

Mills et al. elaborated on the approach, devising an ‘Extended Guided Local Search’ (EGLS) technique that added ‘aspiration criteria’ and random moves to the procedure [4], work which culminated in Mills’ PhD dissertation [3]. Lau and Tsang further extended the approach by integrating it with a Genetic Algorithm, called the ‘Guided Genetic Algorithm’ (GGA) [2], that also culminated in a PhD dissertation by Lau [1]. 7 Bibliography [1] L. T. Lau. Guided Genetic Algorithm. PhD thesis, Department of Computer Science, University of Essex, 1999.

A. Bekey, and F. B. Safford. Global optimization algorithm using adaptive random search. Applied Mathematics and Computation, 7(4):353–376, 1980. [4] L. A. Rastrigin. The convergence of the random search method in the extremal control of a many parameter system. Automation and Remote Control, 24:1337–1342, 1963. [5] G. Schrack and M. Choit. Optimized relative step size random searches. Mathematical Programming, 10(1):230–244, 1976. [6] M. Schumer and K. Steiglitz. Adaptive step size random search.

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