By Yair M. Altman
The MATLAB® programming setting is frequently perceived as a platform compatible for prototyping and modeling yet no longer for "serious" functions. one of many major lawsuits is that MATLAB is simply too gradual.
Accelerating MATLAB Performance goals to right this belief by way of describing a number of how one can significantly enhance MATLAB software pace. choked with hundreds of thousands of worthy suggestions, it leaves no stone unturned, discussing each element of MATLAB.
Ideal for rookies and execs alike, the e-book describes MATLAB functionality in a scale and intensity by no means sooner than released. It takes a finished method of MATLAB functionality, illustrating a number of how you can reach the specified speedup.
The booklet covers MATLAB, CPU, and reminiscence profiling and discusses quite a few tradeoffs in functionality tuning. It describes the application in MATLAB of general tuning strategies utilized in the software program undefined, in addition to equipment which are particular to MATLAB resembling utilizing diversified facts kinds or integrated functions.
The e-book discusses MATLAB vectorization, parallelization (implicit and explicit), optimization, reminiscence administration, chunking, and caching. It explains MATLAB's reminiscence version and information the way it could be leveraged. It describes using GPU, MEX, FPGA, and other kinds of compiled code, in addition to strategies for dashing up deployed functions. It info particular information for MATLAB GUI, images, and I/O. It additionally reports a large choice of utilities, libraries, and toolboxes that may aid to enhance performance.
Sufficient info is equipped to permit readers to right away practice the feedback to their very own MATLAB courses. vast references also are integrated to permit those that desire to extend the therapy of a selected subject to take action easily.
Supported by way of an lively site and diverse code examples, the ebook may also help readers speedily reach major rate reductions in improvement charges and application run occasions.
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Extra info for Accelerating MATLAB Performance: 1001 Tips to Speed Up MATLAB Programs
One way around this problem is to display an actual timer displaying the elapsed time. 1. Another solution is to simply find a way to reduce the run-time variability by reducing WCET, even if it means to increase the average run time! 42 In our example above, we could perhaps reduce the maximal run time from 10 to 7 s at the expense of increasing the average run time from 5 to 6 s. Most users would see this as an improvement to the overall program run time, reporting that the run time has drastically improved from 8–9 s to 6–7 s.
Performance optimization thus enables better functional optimization. This highlights the dual meaning of the term “optimization”: in the context of performance it means improving speed, throughput, or latency, without ever achieving an optimal result; in the context of functionality, optimization means finding an optimal set of parameters for best functional outcome. 5). In a recent poll of Java programmers,6 72% felt that performance was important or critical for their application’s success, and only 7% believed that it did not matter that much for their applications.
Maintainability versus performance — Performance-tuned code is sometimes harder to understand and maintain. For example, mex C-code is harder to maintain than the equivalent m-file; complex vectorization is sometimes harder to maintain than simple loops. The future maintenance cost may not be worth the current run-time saving. As a reader on Loren’s blog* commented:28 “Pay a LOT of attention to code clarity and code design. ” Comments have zero performance impact, yet are incredibly valuable.
Accelerating MATLAB Performance: 1001 Tips to Speed Up MATLAB Programs by Yair M. Altman