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High-Performance MATLAB with GPU Acceleration
In this post, I will discuss techniques you can use to maximize the performance of your GPU-accelerated MATLAB® code. First I explain how to write MATLAB code which is inherently parallelizable.
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This technique, known as vectorization, benefits all your code whether or not it uses the GPU. Then I present a family of function wrappers—, , and that take advantage of GPU hardware, yet require no specialist parallel programming skills.
The most advanced function, , allows you to write your own custom kernels in the MATLAB language.
If these techniques do not provide the performance or flexibility you were after, you can still write custom CUDA code in C or C++ that you can run from MATLAB, as discussed in our earlier Parallel Forall posts on MATLAB CUDA Kernels and MEX functions.
All of the features described here are available out of the box with MATLAB and Parallel Computing Toolbox™.
Mobile phone signal strength example
Throughout this post, I will use an example to help illustrate the techniques.
A cellular phone network w
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