By Yair M. Altman
The MATLAB® programming surroundings is usually perceived as a platform compatible for prototyping and modeling yet no longer for "serious" purposes. one of many major proceedings is that MATLAB is too sluggish.
Accelerating MATLAB Performance goals to right this notion through describing a number of how one can vastly enhance MATLAB software velocity. filled with hundreds of thousands of valuable counsel, it leaves no stone unturned, discussing each element of MATLAB.
Ideal for beginners and pros alike, the booklet describes MATLAB functionality in a scale and intensity by no means sooner than released. It takes a accomplished method of MATLAB functionality, illustrating a variety of how one can reach the specified speedup.
The ebook covers MATLAB, CPU, and reminiscence profiling and discusses numerous tradeoffs in functionality tuning. It describes the application in MATLAB of regular tuning suggestions utilized in the software program undefined, in addition to equipment which are particular to MATLAB equivalent to utilizing various information kinds 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 should be leveraged. It describes using GPU, MEX, FPGA, and other kinds of compiled code, in addition to thoughts for dashing up deployed functions. It information particular assistance for MATLAB GUI, pictures, and I/O. It additionally stories a large choice of utilities, libraries, and toolboxes that may aid to enhance performance.
Sufficient details is equipped to permit readers to instantly observe 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 web site and diverse code examples, the ebook can help readers swiftly reach major savings in improvement charges and software run instances.
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Extra resources for Accelerating MATLAB Performance: 1001 Tips to Speed Up MATLAB Programs
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). However, if the same function would be run 15000 times, the total run-time saving would be over 4 hours, so tuning makes sense. Do not optimize any code before it is functionally stable. Tuning buggy code is useless.
This gives the user an impression that the tasks complete faster, and that the program is more responsive. , re-enable the GUI controls) immediately after updating the tables and graphs, so that the user could continue interaction with the GUI while the program is updating the Excel file (something that could take many seconds). Users would get the feeling that the program has processed the request very quickly since they could continue their GUI interaction, and would not be bothered by the background update of the Excel file.
A variant of this idea is to delay long-running initializations or data-load until after the main program GUI is presented to the user, in cases where these initializations are not immediately required for the presentation. The actual long-running initialization or data load could be done in the background, after the GUI is presented. In some cases it could even be delayed until the user actually needs the data. ‡ Another incarnation of this technique is not to delay data loading, but rather to advance it.