By Yair M. Altman
The MATLAB® programming atmosphere is usually perceived as a platform appropriate for prototyping and modeling yet now not for "serious" functions. one of many major proceedings is that MATLAB is too sluggish.
Accelerating MATLAB Performance goals to right this notion via describing a number of how one can vastly increase MATLAB application pace. choked with millions of necessary information, it leaves no stone unturned, discussing each point of MATLAB.
Ideal for newbies and execs alike, the e-book describes MATLAB functionality in a scale and intensity by no means ahead of released. It takes a entire method of MATLAB functionality, illustrating a variety of how you can reach the specified speedup.
The booklet covers MATLAB, CPU, and reminiscence profiling and discusses numerous tradeoffs in functionality tuning. It describes the application in MATLAB of regular tuning ideas utilized in the software program undefined, in addition to equipment which are particular to MATLAB similar to utilizing various information forms 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 info the way it could be leveraged. It describes using GPU, MEX, FPGA, and different kinds of compiled code, in addition to options for dashing up deployed functions. It info particular counsel for MATLAB GUI, portraits, and I/O. It additionally experiences a wide selection of utilities, libraries, and toolboxes that may aid to enhance performance.
Sufficient details is equipped to permit readers to right away follow the feedback to their very own MATLAB courses. wide 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 energetic site and various code examples, the ebook can assist readers swiftly reach major mark downs in improvement expenses and software run occasions.
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Extra info for Accelerating MATLAB Performance: 1001 Tips to Speed Up MATLAB Programs
6 for a discussion of common performance-tuning tradeoffs. Introduction to Performance Tuning apply to the end user, and lull us into a false sense of security that the performance is suitable. 21 • Partial metrics — Performance goals that cover only part of the application can also be worse than not having any goal at all. Such “coin under the lamp” goals tend to focus our attention on the measured code sections, not necessarily the real hotspots. This could mislead us to tune only part of the application, and to believe that (following the tuning) it is fully optimized.
The distinction between these two motivations is not at all theoretical. In fact, it can have a dramatic effect on what and how we will tune our code. Perceived performance36 is the art of tailoring an application to appear faster, although in practice it would not in fact run faster. This may sound like an oxymoron, but is in fact based on deeply rooted psychological effects that have been extensively studied by usability experts over the past decades. Whenever we performance-tune our code, we should consider whether it would be more cost effective to improve the perceived rather than the actual performance.
In fact, it can have a dramatic effect on what and how we will tune our code. Perceived performance36 is the art of tailoring an application to appear faster, although in practice it would not in fact run faster. This may sound like an oxymoron, but is in fact based on deeply rooted psychological effects that have been extensively studied by usability experts over the past decades. Whenever we performance-tune our code, we should consider whether it would be more cost effective to improve the perceived rather than the actual performance.