By Yair M. Altman
The MATLAB® programming surroundings is usually perceived as a platform appropriate for prototyping and modeling yet no longer for "serious" functions. one of many major court cases is that MATLAB is too gradual.
Accelerating MATLAB Performance goals to right this conception by way of describing a number of how one can enormously enhance MATLAB software velocity. jam-packed with hundreds of thousands of worthy counsel, it leaves no stone unturned, discussing each point of MATLAB.
Ideal for rookies and execs alike, the publication describes MATLAB functionality in a scale and intensity by no means earlier than released. It takes a complete method of MATLAB functionality, illustrating a variety of how one can reach the specified speedup.
The publication covers MATLAB, CPU, and reminiscence profiling and discusses a number of tradeoffs in functionality tuning. It describes the application in MATLAB of normal tuning concepts utilized in the software program undefined, in addition to tools which are particular to MATLAB similar to utilizing assorted info 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 information the way it might be leveraged. It describes using GPU, MEX, FPGA, and other kinds of compiled code, in addition to options for dashing up deployed purposes. It info particular advice for MATLAB GUI, snap shots, and I/O. It additionally experiences a wide selection of utilities, libraries, and toolboxes which may aid to enhance performance.
Sufficient info 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 via an lively web site and various code examples, the publication can assist readers speedily reach major savings in improvement charges and application run occasions.
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Extra resources for Accelerating MATLAB Performance: 1001 Tips to Speed Up MATLAB Programs
So do not assume, profile. 22 For example, it is easy to blame Microsoft Excel for the long time it takes to update an Excel file using xlswrite. We can even “prove” it using profiling. * • Using inappropriate tools — A surprising number of MATLAB users have never used the built-in Profiler tool, basing their performance tuning on tic/toc or debug printouts. † • Ignoring the differences between CPU and wall-clock profiling — It makes no sense to tune CPU-intensive segments of I/O-bound applications.
Therefore, solving the underlying reason will automatically improve all these items together, in one fell swoop, making this an ideal tuning candidate. For instance, in the following simplified example, blindly following Pareto’s principle might lead us to tune mainAlgo, whereas in fact it would make more sense to optimize the legend first, since the three legend-related functions together outweigh mainAlgo’s run time:† Function mainAlgo createLegend initLegend updateLegend (all others) Relative Run Time (%) 35 25 15 10 15 When deciding what to tune, we should NEVER rely on guesses or intuition.
A 20× speedup is of course impressive, but considering the fact that it needs a massive investment in parallelization code and in computer nodes, the picture is not as bright as might appear at first glance. Actual real-life speedups never even reach Amdahl’s theoretical limits. 34 # of processors (P) Realistic Amdahl's law of parallelization efficiency The good news is that parallelism improves performance better for larger data sizes. 8 Perceived versus Actual Performance Before starting to performance-tune any code, we should ask ourselves what is the ultimate goal of this tuning: Are we really interested in the program running faster, or do we only wish it to appear faster and more responsive?
Accelerating MATLAB Performance: 1001 Tips to Speed Up MATLAB Programs by Yair M. Altman