Also include a copy of all MATLAB code written and a copy of all the plots and images created. As robince said, not all loops are slow in matlab nowadays, though you do need to know when they work, and when they don't.Īnd in any way you always need a loop, it is just implemented in matlab built-in functions / BLAS instead of the users matlab code. Write a paragraph summarizing what you have learned in this Lab. Which is even more misleading since those functions might be significantly slower than native matlab loops.
To add to the confusion, sometimes people even use the word to say that some loop has been implemented using a built-in function, such as arrayfun, or bsxfun. It is just a word used to signify the lack of a loop implemented in MATLAB. So, matlab 'vectorization' is not equivalent to using CPU vector instructions. This code on the other hand A = rand(1000) Ĭan take advantage of SSE instructions and streaming instructions, since we operate on two adjacent columns at a time. However, since matlab stores matrices column-wise (columns are contiguous in memory), this operation is not trivially changed into operations on SSE vectors: since we perform the operations row-wise the data you need to load into the vectors is not stored contiguously in the memory. This code computes mean values for two adjacent matrix rows. Consider the following code: A = rand(1000) This produces a vector ‘su’ whose elements are square of the element of vector u. Then we take a dot product (.) of vector u with itself. Let us consider one example in this example, we can create one vector, ‘u’. The max value is assigned to s in the 7th line of code. The findmax function has been modified to return m, the maximum value, and p, the position of the maximum value. Matlab 'vectorized' code is not necessarily expressed as vectorized CPU instructions. Given below are the examples of Matlab Magnitude of Vector: Example 1. In this code, the findmax function written in a previous tutorial is being used to find the maximum value rather than cutting and pasting that code in this m-file. Using vectorization, you can write a much more concise MATLAB process.
#PARAGRAPH VECTOR CODE MATLAB MOD#
Matlab 'vectorization', as the word is commonly used, is only about expressing loops in the form of (vectors of) matrix indices, and sometimes about writing things in terms of basic matrix/vector operations (BLAS), instead of writing the loop itself. This code computes the cumulative sum of a vector at every fifth element: x 1:10000 ylength (length (x) - mod (length (x),5))/5 y (1:ylength) 0 for n 5:5:length (x) y (n/5) sum (x (1:n)) end. This is a common misunderstanding between two groups of people: matlab programmers and C/asm programmers. Matlab 'vectorization' concept is completely different than the vector instructions concept, such as SSE.
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