![]() The original code to perform this filtering is in code/imagefilter/processimage.m. You can see the original and filtered images here: We’ll do a simple greyscale conversion, except light grey and white will be converted to shades of yellow. ![]() We will do a simple image processing task on a large image taken from NASA of the Orion Nebula, taken by the Hubble space telescope. ![]() Let’s look at a typical example of code that can benefit from vectorization. This can affect memory requirements and potentially performance as well. It also requires temporary data to be stored in large matrices, rather than being generated inside a loop and immediately thrown away. It can be difficult to use this approach with complex loops that often do not lend themselves to vectorization. On the other hand, vectorizing code is limited to operations that can be performed on a matrix instead of individual elements in a for loop. The vectorized parallel workers are threaded, which allows workers to share data in variables without making copies of it. The way that MATLAB creates parallel workers for vectorized matrix operations is different than how parfor works. In fact, MATLAB will implicitly run some vectorized operations in parallel to use more than one CPU core without the need for an explicit parfor statement. Also it is usually fast because Mathworks implements highly optimized vectorized operations. The resulting vectorized code can be more compact, making it easier to read. This can replace loops that apply the operations to each matrix element individually. Many operators and functions in MATLAB can be applied to an entire matrix. This is a large subject, and we will only scratch the surface in this lesson. Demonstrate MATLAB vectorization as an alternative form of parallelization.Įxplain the pros and cons of vectorization vs parfor.Īnother technique for improving MATLAB code performance is vectorization.
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