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

By Yair M. Altman

Accelerating MATLAB functionality goals to right this conception by means of describing a number of how you can enormously increase MATLAB software velocity. filled with hundreds of thousands of beneficial assistance, it leaves no stone unturned, discussing each point of MATLAB.
Ideal for rookies and pros alike, the booklet describes MATLAB functionality in a scale and intensity by no means prior to released. It takes a entire method of MATLAB functionality, illustrating various how you can reach the specified speedup.
The booklet covers MATLAB, CPU, and reminiscence profiling and discusses numerous tradeoffs in functionality tuning. It describes either the appliance of ordinary suggestions in MATLAB, in addition to equipment which are particular to MATLAB reminiscent of utilizing assorted facts varieties or integrated functions.
The ebook covers MATLAB vectorization, parallelization (implicit and explicit), optimization, reminiscence administration, chunking, and caching. It explains MATLAB’s reminiscence version and information the way it could be leveraged. It describes using GPU, MEX, FPGA, and other kinds of compiled code, in addition to recommendations for dashing up deployed purposes. It info particular information for MATLAB GUI, portraits, and I/O. It additionally experiences a wide selection of utilities, libraries, and toolboxes that could aid to enhance performance.
Sufficient info is equipped to permit readers to right away practice the feedback to their very own MATLAB courses. wide references also are integrated to permit those that desire to extend the remedy of a selected 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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Actual real-life speedups never even reach Amdahl’s theoretical limits. 5 2048 1024 512 256 128 64 32 16 8 4 2 1 Speedup A more realistic Amdahl's law 9 8 7 6 5 4 3 2 1 0 # 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?

Introduction to Performance Tuning 9 Finally, we should stop tuning when we reach the limit in which platform differences could come into play. We might well over-optimize our code so that it runs extremely fast on our speciic development workstation, but fails miserably on other computers, due to targeted use of hardware-speciic techniques such as CPU cache size. 3 Periodic Performance Maintenance Every now and then, we should recheck our application’s performance and decide whether the conditions suggest re-tuning the application.

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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