Performance Tips

In the following sections, we briefly go through a few techniques that can help make your Julia code run as fast as possible.

Avoid global variables

A global variable might have its value, and therefore its type, change at any point. This makes it difficult for the compiler to optimize code using global variables. Variables should be local, or passed as arguments to functions, whenever possible.

We find that global names are frequently constants, and declaring them as such greatly improves performance:

const DEFAULT_VAL = 0

Uses of non-constant globals can be optimized by annotating their types at the point of use:

global x
y = f(x::Int + 1)

Type declarations

In many languages with optional type declarations, adding declarations is the principal way to make code run faster. In Julia, the compiler generally knows the types of all function arguments and local variables. However, there are a few specific instances where declarations are helpful.

Declare specific types for fields of composite types

Given a user-defined type like the following:

type Foo

the compiler will not generally know the type of foo.field, since it might be modified at any time to refer to a value of a different type. It will help to declare the most specific type possible, such as field::Float64 or field::Array{Int64,1}.

Annotate values taken from untyped locations

It is often convenient to work with data structures that may contain values of any type, such as the original Foo type above, or cell arrays (arrays of type Array{Any}). But, if you’re using one of these structures and happen to know the type of an element, it helps to share this knowledge with the compiler:

function foo(a::Array{Any,1})
    x = a[1]::Int32
    b = x+1

Here, we happened to know that the first element of a would be an Int32. Making an annotation like this has the added benefit that it will raise a run-time error if the value is not of the expected type, potentially catching certain bugs earlier.

Declare types of named arguments

Named arguments can have declared types:

function with_named(x; name::Int = 1)

Functions are specialized on the types of named arguments, so these declarations will not affect performance of code inside the function. However, they will reduce the overhead of calls to the function that include named arguments.

Functions with named arguments have near-zero overhead for call sites that pass only positional arguments.

Passing dynamic lists of named arguments, as in f(x; names...), can be slow and should be avoided in performance-sensitive code.

Break functions into multiple definitions

Writing a function as many small definitions allows the compiler to directly call the most applicable code, or even inline it.

Here is an example of a “compound function” that should really be written as multiple definitions:

function norm(A)
    if isa(A, Vector)
        return sqrt(real(dot(x,x)))
    elseif isa(A, Matrix)
        return max(svd(A)[2])
        error("norm: invalid argument")

This can be written more concisely and efficiently as:

norm(A::Vector) = sqrt(real(dot(x,x)))
norm(A::Matrix) = max(svd(A)[2])

Write “type-stable” functions

When possible, it helps to ensure that a function always returns a value of the same type. Consider the following definition:

pos(x) = x < 0 ? 0 : x

Although this seems innocent enough, the problem is that 0 is an integer (of type Int) and x might be of any type. Thus, depending on the value of x, this function might return a value of either of two types. This behavior is allowed, and may be desirable in some cases. But it can easily be fixed as follows:

pos(x) = x < 0 ? zero(x) : x

There is also a one function, and a more general oftype(x,y) function, which returns y converted to the type of x. The first argument to any of these functions can be either a value or a type.

Avoid changing the type of a variable

An analogous “type-stability” problem exists for variables used repeatedly within a function:

function foo()
    x = 1
    for i = 1:10
        x = x/bar()
    return x

Local variable x starts as an integer, and after one loop iteration becomes a floating-point number (the result of the / operator). This makes it more difficult for the compiler to optimize the body of the loop. There are several possible fixes:

  • Initialize x with x = 1.0
  • Declare the type of x: x::Float64 = 1
  • Use an explicit conversion: x = one(T)

Separate kernel functions

Many functions follow a pattern of performing some set-up work, and then running many iterations to perform a core computation. Where possible, it is a good idea to put these core computations in separate functions. For example, the following contrived function returns an array of a randomly-chosen type:

function strange_twos(n)
    a = Array(randbool() ? Int64 : Float64, n)
    for i = 1:n
        a[i] = 2
    return a

This should be written as:

function fill_twos!(a)
    for i=1:length(a)
        a[i] = 2

function strange_twos(n)
    a = Array(randbool() ? Int64 : Float64, n)
    return a

Julia’s compiler specializes code for argument types at function boundaries, so in the original implementation it does not know the type of a during the loop (since it is chosen randomly). Therefore the second version is generally faster since the inner loop can be recompiled as part of fill_twos! for different types of a.

The second form is also often better style and can lead to more code reuse.

This pattern is used in several places in the standard library. For example, see hvcat_fill in abstractarray.jl, or the fill! function, which we could have used instead of writing our own fill_twos!.

Functions like strange_twos occur when dealing with data of uncertain type, for example data loaded from an input file that might contain either integers, floats, strings, or something else.


These are some minor points that might help in tight inner loops.

  • Use size(A,n) when possible instead of size(A).
  • Avoid unnecessary arrays. For example, instead of sum([x,y,z]) use x+y+z.