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

By Yair M. Altman

The MATLAB® programming atmosphere is usually perceived as a platform appropriate for prototyping and modeling yet now not for "serious" functions. one of many major court cases is that MATLAB is simply too sluggish.

Accelerating MATLAB Performance goals to right this belief by way of describing a number of how you can enormously enhance MATLAB software pace. full of millions of important counsel, it leaves no stone unturned, discussing each element of MATLAB.

Ideal for newcomers and pros alike, the ebook describes MATLAB functionality in a scale and intensity by no means ahead of released. It takes a entire method of MATLAB functionality, illustrating quite a few 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 suggestions utilized in the software program undefined, in addition to equipment which are particular to MATLAB comparable to utilizing diversified info varieties or integrated functions. 

The ebook discusses MATLAB vectorization, parallelization (implicit and explicit), optimization, reminiscence administration, chunking, and caching. It explains MATLAB's reminiscence version and info the way it could be leveraged. It describes using GPU, MEX, FPGA, and other kinds of compiled code, in addition to suggestions for dashing up deployed functions. It info particular suggestions for MATLAB GUI, pics, and I/O. It additionally experiences a wide selection of utilities, libraries, and toolboxes that could aid to enhance performance.

Sufficient details is equipped to permit readers to instantly follow the feedback to their very own MATLAB courses. vast references also are incorporated to permit those that desire to extend the remedy of a specific subject to take action easily.

Supported through an lively site and diverse code examples, the booklet might help readers swiftly reach major mark downs in improvement expenditures and software run instances.

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2). 2 again). • Rushed optimization — This happens when performance is not treated as an important application feature, and is not allocated reasonable resources and priority by the project manager. 20 The result is often ineffective off-hand optimization at the last moment before delivery. 2 Performance Goals • Missing metrics — Without clear quantifiable measurable goals, we will likely spend too much time tuning yet still fall short of our targets. • Irrelevant or unrealistic metrics — Using incorrect performance goals is worse than not having any goals at all.

We have slashed the run time of these 40% in ideal conditions by half (the two halves being run in parallel by the P = 2 processors), so that it now takes only 20%. 25). 5 Speedup 15 10 5 32768 16384 8192 4096 2048 1024 512 256 128 64 32 16 8 4 2 1 2 1 # of processors (P) Ideal Amdahl's law of parallelization efficiency * That is, no distribution/communication/assembly overheads; optimal work distribution, etc. 5 2048 1024 512 256 128 64 32 16 8 4 A more realistic Amdahl's law 2 9 8 7 6 5 4 3 2 1 0 1 Speedup The higher the relative portion of parallelizable code 1 – α, the higher the potential speedup, but even extremely high 1 – α values have a speedup limit that is based on the non-parallelizable portion α.

5%–10% or less) than the initial improvement. We can keep track of these values by recording the measurement results in each tuning cycle. Examples can be found of 20× or 100× speedups, but in real life there is often no need for such speedups — 3–10× is more than enough for most practical cases. Another indication that source-code tuning has reached its usefulness limit, is when the top time-hoggers only use 5%–10% of the total program time. This indicates a breakup of the Pareto principle upon which effective tuning is based.

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