![]() As a general rule, complicated functions speed up more than simple functions. The operation is not memory-bound processing time is not dominated by memory access time. For example, most functions speed up only when the array contains several thousand elements or more. The data size is large enough so that any advantages of concurrent execution outweigh the time required to partition the data and manage separate execution threads. They should require few sequential operations. These sections must be able to execute with little communication between processes. MATLAB supports (and encourages) vectorized operations on vectors and matrices.įor example, suppose we have A and B, two n-by- m matrices and we want C to be the element-wise product of the corresponding elements (i.e., C(i,j) = A(i,j)*B(i,j)).The function performs operations that easily partition into sections that execute concurrently. implicit array expansion (opens new window). A comparison between the speed of computing using bsxfun vs.NumPy's Broadcasting 1 (opens new window), 2 (opens new window).MATLAB documentation on " Compatible Array Sizes for Basic Operations (opens new window)". ![]() If such a concern does not exist, MATLAB R2016 release notes (opens new window) encourage users to switch from bsxfun:Ĭompared to using bsxfun, implicit expansion offers faster speed of execution, better memory usage, and improved readability of code. Therefore, the explicit invocation of bsxfun 1 (opens new window), 2 (opens new window) (which achieves the same effect) should be used if code needs to run on older MATLAB versions. N-D arrays that have the right amount of singleton dimensions but they're in the wrong order (#2).Ĭode relying on this convention is NOT backward-compatible with any older versions of MATLAB. N-D arrays that have the right amount of singleton dimensions but they're in the wrong order (#1). Vectors where a dimension is a multiple of the same dimension in the other array.Īrrays with dimensions that are multiples of each other. # Examples of incompatible sizes: Description In other words - dimensions that appear in one array and do not appear in the other, are implicitly fit for automatic expansion.įor example, in MATLAB versions before R2016b this would happen:Īns = 9 3 9 4 7 10 5 11 5 # Examples of compatible sizes: Description Note that trailing singleton (that is, of size 1) dimensions are omitted by MATLAB, even though there's theoretically an infinite amount of them. ![]() Sizes are considered "compatible" when each dimension in one array is either exactly equal to the same dimension in the other array, or is equal to 1. The aforementioned binary operations are allowed between arrays, as long as they have "compatible sizes".
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