![]() ![]() Even vectorized MATLAB code usually runs slower than unoptimized C or Fortran code, and I can usually write looped Fortran code faster than I can think about how to vectorize something since it is a direct translation of my existing code. If I can’t find an obvious way to vectorize it, I will just write a Fortran program to do it. If it runs slowly, then I will think for a little bit about vectorizing it (like 15 minutes maximum). If there isn’t something obvious, I’ll just write it as a loop and run it to see how long it takes. If there is a really obvious way to vectorize something, I do so from the beginning. ![]() My strategy for optimizing MATLAB code is really quite lazy. This lab looks at some ways that you can optimize MATLAB code. Figuring out ways to vectorize code is often more art than science you either need to learn some of the non-obvious tricks that people have figured out over the years, or come up with them yourself. One way that MATLAB code can be sped up is by doing what is referred to as “vectorization,” which essentially means that instead of using loops, we attempt to write computations as matrix or array operations on an entire vector/matrix that have been optimized for speed by the programmers that develop MATLAB. If you want to see the difference that the JIT compiler makes, type feature accel off into the command line ( feature accel on turns it back on). However, this has become less true in recent years, as the JIT compiler speeds up a lot of loops that MATLAB used to take a long time to execute. While MATLAB has what is called a “Just In Time” compiler (JIT) known as the “accelerate” feature, the fact that MATLAB is interpreted sometimes slows it down. This is in contrast to a compiled language, which converts your entire code into a format suitable for execution by your computer’s processer and then executes it. MATLAB is an interpreted language, in the sense that you can type a command and it executes immediately. ![]()
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