By Yair M. Altman

Accelerating MATLAB functionality goals to right this belief by means of describing a number of how you can enormously enhance MATLAB software velocity. choked with hundreds of thousands of valuable information, it leaves no stone unturned, discussing each point of MATLAB.
Ideal for beginners and pros alike, the publication describes MATLAB functionality in a scale and intensity by no means sooner than released. It takes a finished method of MATLAB functionality, illustrating various how one can reach the specified speedup.
The ebook covers MATLAB, CPU, and reminiscence profiling and discusses numerous tradeoffs in functionality tuning. It describes either the appliance of normal options in MATLAB, in addition to equipment which are particular to MATLAB akin to utilizing diverse information forms or integrated functions.
The booklet covers MATLAB vectorization, parallelization (implicit and explicit), optimization, reminiscence administration, chunking, and caching. It explains MATLAB’s reminiscence version and info the way it should be leveraged. It describes using GPU, MEX, FPGA, and other kinds of compiled code, in addition to thoughts for rushing up deployed purposes. It information particular guidance for MATLAB GUI, snap shots, and I/O. It additionally experiences a wide selection of utilities, libraries, and toolboxes that could support to enhance performance.
Sufficient info is supplied to permit readers to instantly practice the feedback to their very own MATLAB courses. wide references also are incorporated to permit those that desire to extend the therapy of a specific subject to take action simply.

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Additional info for Accelerating MATLAB Performance 1001 Tips to Speed Up MATLAB Programs

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Conventional knowledge is that the median (rather than mean) time measurement should be reported during performance proiling, in order to eliminate spurious long-running measurements. However, in practice it is actually beneicial to over-weigh such measurements, or at least to report the mean (not median) time in order to not under-weigh them. 43 This allows elimination of the really outlandish outlier measurements, while remaining close to the WCET perception. 44 When monitoring, be careful to measure the actual end-to-end time duration, rather than just memory usage or CPU load, which are related but do not present the full picture.

7. 2. 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 speciic 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-ile; complex vectorization is sometimes harder to maintain than simple loops.

We can reduce run-time variability by other means: We could use a local disk rather than network I/O that is subject to unexpected latencies. We could also cache or pre-compute all possible data, rather than just the requested data. The precise mechanism is not very important, as long as we achieve the aim of reducing the maximal run time and achieving more-consistent run times (these are usually aspects of the same thing). Conventional knowledge is that the median (rather than mean) time measurement should be reported during performance proiling, in order to eliminate spurious long-running measurements.

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Accelerating MATLAB Performance 1001 Tips to Speed Up MATLAB Programs by Yair M. Altman


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