By Yair M. Altman
The MATLAB® programming surroundings is usually perceived as a platform compatible for prototyping and modeling yet no longer for "serious" functions. one of many major court cases is that MATLAB is too sluggish.
Accelerating MATLAB Performance goals to right this notion by way of describing a number of how you can vastly increase MATLAB application velocity. jam-packed with hundreds of thousands of important counsel, it leaves no stone unturned, discussing each element of MATLAB.
Ideal for newbies and pros alike, the booklet describes MATLAB functionality in a scale and intensity by no means earlier than released. It takes a finished method of MATLAB functionality, illustrating a number of how one can reach the specified speedup.
The publication covers MATLAB, CPU, and reminiscence profiling and discusses a number of tradeoffs in functionality tuning. It describes the application in MATLAB of usual tuning innovations utilized in the software program undefined, in addition to tools which are particular to MATLAB comparable to utilizing assorted facts varieties or integrated functions.
The publication discusses MATLAB vectorization, parallelization (implicit and explicit), optimization, reminiscence administration, chunking, and caching. It explains MATLAB's reminiscence version and information the way it will be leveraged. It describes using GPU, MEX, FPGA, and other kinds of compiled code, in addition to concepts for dashing up deployed functions. It information particular counsel for MATLAB GUI, pics, and I/O. It additionally experiences a wide selection of utilities, libraries, and toolboxes which could support to enhance performance.
Sufficient info is equipped to permit readers to instantly follow the feedback to their very own MATLAB courses. broad references also are integrated to permit those that desire to extend the therapy of a selected subject to take action easily.
Supported by means of an energetic web site and diverse code examples, the ebook might help readers speedily reach major rate reductions in improvement expenses and application run occasions.
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Extra info for Accelerating MATLAB Performance: 1001 Tips to Speed Up MATLAB Programs
If an hour of time invested now will save many hours later, it may be worth the investment. If the code is used by many others, it can be a good investment to save even a few seconds per run, multiplied by thousands or millions of uses into a real benefit. We need to consider the total potential savings when deciding whether to tune: after all, it would not make much sense to spend an hour to tune an algorithm that would save a single noncritical second in run time, if the function would only run 60 times (a total savings of only 1 min).
6. It is quite possible that in any particular case, one or another of these tradeoffs will dictate a different tuning-limit criterion. 8. Introduction to Performance Tuning 9 Finally, we should stop tuning when we reach the limit in which platform differences could come into play. We might well over-optimize our code so that it runs extremely fast on our specific development workstation, but fails miserably on other computers, due to targeted use of hardware-specific techniques such as CPU cache size.
Streamlining the application means that we should reduce the requirements for user interaction to a minimum. The fewer user clicks needed, the better. Numerous usability studies have shown that users drop off with each additional click. This is the reason why professional websites reduce the purchase experience to as few clicks as possible. 40 We should attach callbacks to any GUI control used in your GUI, so that user interaction with any of them will automatically update the rest of the display.
Accelerating MATLAB Performance: 1001 Tips to Speed Up MATLAB Programs by Yair M. Altman