By Yair M. Altman
The MATLAB® programming surroundings is usually perceived as a platform appropriate for prototyping and modeling yet now not for "serious" purposes. one of many major proceedings is that MATLAB is simply too sluggish.
Accelerating MATLAB Performance goals to right this notion via describing a number of how one can vastly increase MATLAB software pace. jam-packed with millions of necessary guidance, it leaves no stone unturned, discussing each element of MATLAB.
Ideal for newcomers and execs alike, the ebook describes MATLAB functionality in a scale and intensity by no means ahead of released. It takes a finished method of MATLAB functionality, illustrating various how one 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 average tuning options utilized in the software program undefined, in addition to tools which are particular to MATLAB resembling utilizing assorted facts varieties 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 will be leveraged. It describes using GPU, MEX, FPGA, and other kinds of compiled code, in addition to recommendations for rushing up deployed purposes. It information particular suggestions for MATLAB GUI, portraits, and I/O. It additionally reports a wide selection of utilities, libraries, and toolboxes which can 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 remedy of a selected subject to take action easily.
Supported via an lively site and diverse code examples, the e-book may help readers quickly reach major mark downs in improvement charges and application run instances.
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Additional resources for Accelerating MATLAB Performance: 1001 Tips to Speed Up MATLAB Programs
When asked to describe the duration of such tasks, users tend to describe feedbacked-tasks as taking a shorter time than the exact same task without feedback. The bottom line is that the simple modification of presenting feedback to the user for ongoing tasks will improve the program’s perceived performance, even if the actual run-time remains unchanged. MATLAB has many different ways by which we can present feedback:* We can send a message to the MATLAB console (Command Window); or modify the figure window’s title or color; or display a pop-up message window; or update some graphics.
1 Pareto’s Principle and the Law of Diminishing Returns In each step of the tuning cycle, we should use Pareto’s principle12 (also known as the “80–20 rule”*) to concentrate our energy on the 20% of the code that accounts for 80% of the application’s time. , altogether just 4%–5% of the total program code) accounts for 80% of the code that can actually be tuned to improve performance. In practice this means that we should concentrate on the top 3–5 time hogging functions (as reported by the profiling — see Chapter 2), and within them only on the top 3–5 hogging lines or code segments.
The application needs delicate coordination to prevent task contention between nodes. * In addition, horizontal scaling involves significant overhead in terms of hardware resources: We usually need a central node to run the main program and distribute/assemble jobs; and we often need each of the nodes to have similar memory/disks/CPU, even if they do not make full utilizations of these resources (that can sometimes be shared). We also need to invest in fast network connections and efficient hardware oversight monitoring.