By Yair M. Altman
The MATLAB® programming surroundings is frequently perceived as a platform compatible for prototyping and modeling yet now not for "serious" purposes. one of many major lawsuits is that MATLAB is too sluggish.
Accelerating MATLAB Performance goals to right this belief by means of describing a number of how one can tremendously increase MATLAB software velocity. choked with millions of beneficial assistance, it leaves no stone unturned, discussing each point of MATLAB.
Ideal for rookies and execs alike, the booklet describes MATLAB functionality in a scale and intensity by no means earlier than released. It takes a accomplished method of MATLAB functionality, illustrating a variety of how one 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 ordinary tuning suggestions utilized in the software program undefined, in addition to tools which are particular to MATLAB equivalent to utilizing various facts kinds or integrated functions.
The booklet discusses MATLAB vectorization, parallelization (implicit and explicit), optimization, reminiscence administration, chunking, and caching. It explains MATLAB's reminiscence version and info the way it might be leveraged. It describes using GPU, MEX, FPGA, and other kinds of compiled code, in addition to ideas for dashing up deployed purposes. It information particular information for MATLAB GUI, pictures, and I/O. It additionally stories a large choice of utilities, libraries, and toolboxes which can aid to enhance performance.
Sufficient details is supplied 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 means of an energetic web site and various code examples, the ebook might help readers swiftly reach major discount rates in improvement expenditures and application run instances.
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Extra info for Accelerating MATLAB Performance: 1001 Tips to Speed Up MATLAB Programs
Repeatability versus performance — Speedup is sometimes achieved by adapting the order of execution of code sections to external conditions, sacrificing results repeatability at the expense of performance. In fact, sometimes the order of execution is more important than the speed of execution. 6, variability of the performance results directly affects the perceived performance. It is often better to reduce the performance variability, at the expense of slower average execution time, for the benefit of a faster maximal run time.
Whenever we modify our code, there is a good chance that we will inadvertently introduce bugs into the code. A well-known rule-of-thumb is that a single bug is introduced for every 10 new or modified lines of code. Therefore, we should also take into consideration additional time required for debugging, verification, and fixing bugs introduced during the tuning process, and the possibility that some additional bugs will not be detected. ” So, if our code runs fast enough, leave it alone. Do not spend time optimizing something that does not really require optimization.
14 Accelerating MATLAB® Performance inheritances). Unfortunately, abstraction layers very often have a devastating effect on run-time performance. In a related matter, some performance optimizations can be platform dependent. 27 We should decide whether we optimize for a specific platform, or for a variety of different systems. • 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.
Accelerating MATLAB Performance: 1001 Tips to Speed Up MATLAB Programs by Yair M. Altman