If this is not what weĪre after, we can convert the matrix into a vector using the (:) operator, pass it to the function, and reshape the outputīack into the original size with the reshape() function. Some functions, like mvnpdf() for example, interpret an n-by-d matrix, not as n-times-d elements but as n, d-dimensional vectors. Non-vectorized version tic % time the codeīnv = zeros(size(A)) % We preallocate to level the playing field for i=1:size(A,1) Let us compare this vectorized version to calling log on each element.Ī = rand(200,200) % We will use this as our data ![]() For example, to take the log of every number in an array A, we simply executeī = log(A). Most functions in Matlab are already vectorized. Using built-in functions which are vectorized We give various examples below which should give you some ThisĬan make your code much faster and is an essential skill to learn. Vectorization is the process of making your code work on array-structured data in parallel, rather than using for-loops. The larger the matrices, the more importantī = zeros(30000,1) % Preallocate B with the zeros command. Timing the two loops with the tic() and toc() commands we see that preallocating in this case speeds up the code by about 30 times. Below we see two simple loops in which we store the numbers 1 to 30 000. ![]() The zeros() command is the most common way to do this. It can therefore significantly speed up yourĬode by preallocating a chunk of memory before entering into a loop. This process may have to be repeated over and over again causing huge delays. When the size of a matrix changes, Matlab, if it has not preallocatedĮnough space, must find a new chunk of memory large enough and copy the matrix over. Matlab stores matrices in contiguous blocks of memory. W = waitbar(i/500,w,) % update the wait bar each iteration end w = waitbar(0, 'My Progress Bar') % create a new waitbar, w with 0% progress for i=1:500 We can print a graphical bar that indicates how long a certain operation has taken/ will take, as in this example. Has written some flop counting routines as part of his lightspeed package. Matlab used to have a flops command, but it was removed. A more reliable method is the timeit function, which calls tic/toc multiple times and averages. The simplest way to time code is to call tic before your code starts and toc afterwards. The profiler tells you where all the time is being spent, but maybe you just want to know how much time your code is taking. Once profiling is on, execute your code, and then type profile viewer to see the report. ![]() You can turn on profiling with the profile on command and turn it off again with profile off. On a function name to bring up its sourcecode. This is really the statistic you should pay attention to. How long was spent in functions called from here. ![]() Self-time, denoted by a dark blue band, is the the time spent within a function not including The report shows a breakdown of all the functions called from your function, the number of times they were called, and the Generates a web page summarizing the results, which you can click on to 'drill down'. Matlab has an excellent profiler, which can tell you how much time your code spends in each one of its functions. Remember that correctness is more important than speed! The usual pattern is that 80% of the the time is spend in 20% of the code, so you can focus your efforts accordingly. A slightly more complex example of vectorizationīefore spending a lot of time optimizing your code, you should first identify the key bottlenecks using the profiler.Using built-in functions which are vectorized.
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