First on that list is the variable annotations that litter this entire function. For the integrity of this experiment, I will be removing each type of annotation and then observing the resulting performance using the same methods prior. My first test in this regard was to remove the annotations that I suspect have the biggest impact on performance. It certainly makes sense that the annotated version would be faster - but why is it faster? Which annotations specifically are contributing to this? The good news is, we learned that annotating everything is certainly a lot better than annotating nothing. Here we were testing whether annotations have an impact on performance, but not whether specific annotations do. As discussed, however, the problem with this experiment is that many variables were changed at once, or rather - we might be testing the wrong thing. 9373144, which is actually quite a significant jump in speed from the 0.990927 runtime we saw earlier in the non-annotated version. This brings our average compile and run time for the fully annotated version to. function mean_and_inc(x::Array) newarray::Array(a::Float64, b::Float64, predict::Function) end endĪs recommended by the Julia Performance Documentation, I have also typed the fields of this constructor in a very non-ambiguous way, opting to use sub-types as parameters for the constructor. For example, here I will simply try and return a mean and an incremented version of a vector. We can also apply this same syntax to pretty much any value inside of the function. This also opens the method up for multiple dispatch, all we need to do is write a new function and annotate with different types. In this case, only 64-bit integers can be passed through this function because this is annotated. Type annotations are denoted with two colons before a type name, for example, we can use an annotation in the arguments of a function in order to tell the compiler the types for each argument. In Julia, we can communicate the types of different variables, arguments, elements, and returns using type annotations. Not only is this required so the compiler knows the types, but also so that our methods have some logical correspondence to the types that will be put through them. In order to write Julia in the most ideal fashion, we need to work with types constantly and furthermore communicate to the compiler what we are trying to do in terms of typing when applicable. If you would like to view the notebook associated with this article, you may do so on Github here:Īs I touched on prior, types are important in Julia. Today we will test using different forms of annotations in different applications, as well as reveal what the Julia performance documentation says on this topic. Naturally, we also need to assume that there are more appropriate times to do so and less appropriate times to do so. The question then becomes how we actually communicate our types to the compiler in Julia. All of this being said, it is probably a good idea to pay attention to the types of the different values that we work with whenever we are using Julia. This is because the compiler needs to worry about typing constantly, even when it comes to method calls and things of that nature. As Julia is a language centered around the concept, or in Julia’s specific case - the paradigm - of multiple dispatch, types are incredibly important when it comes to the performance of the compiler. In Julia, much like any other programming language, there is a thing called good code and a thing called bad code.
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