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
field
end
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
...
end
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)
...
end
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])
else
error("norm: invalid argument")
end
end
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()
end
return x
end
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
withx = 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
end
return a
end
This should be written as:
function fill_twos!(a)
for i=1:length(a)
a[i] = 2
end
end
function strange_twos(n)
a = Array(randbool() ? Int64 : Float64, n)
fill_twos!(a)
return a
end
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.
Tweaks¶
These are some minor points that might help in tight inner loops.
- Use
size(A,n)
when possible instead ofsize(A)
. - Avoid unnecessary arrays. For example, instead of
sum([x,y,z])
usex+y+z
.