The Julia programming language is known for being a high-level language that can still compete with C in terms of performance. As such, Julia already has performant data structures built-in, such as arrays. But what if arrays could be even faster? That's where the StaticArrays.jl package comes in.

StaticArrays.jl provides drop-in replacements for Array, the standard Julia array type. These StaticArrays work just like Arrays, but they provide one additional piece of information in the type: the size of the array. Consequently, you can't insert or remove elements of a StaticArray; they are statically sized arrays (hence the name). However, this restriction allows more information to be given to Julia's compiler, which in turn results in more efficient machine code (for example, via loop unrolling and SIMD operations). The resulting speed-up can often be 10x or more!

In this post, we will learn how to use StaticArrays.jl and compare the performance of StaticArrays to that of regular Arrays for several different operations.

Note that the code examples in this post assume StaticArrays.jl has been installed and loaded:

# Press ] to enter the package prompt.
pkg> add StaticArrays

# Press Backspace to return to the Julia prompt.
julia> using StaticArrays

(Check out our post on the Julia REPL for more details about the package prompt and navigating the REPL.)

How to Use StaticArrays.jl

When working with StaticArrays.jl, typically one will use the SVector type or the SMatrix type. (There is also the SArray type for N-dimensional arrays, but we will focus on 1D and 2D arrays in this post.) SVectors and SMatrixes have both static size and static data, meaning the data contained in such objects cannot be modified. For statically sized arrays whose contents can be modified, StaticArrays.jl provides MVector and MMatrix (and MArray). We will stick with SVectors and SMatrixes in this post unless we specifically need mutability.

Constructors

There are three ways to construct StaticArrays.

Convenience constructor SA:

julia> SA[1, 2, 3]
3-element SVector{3, Int64} with indices SOneTo(3):
 1
 2
 3

julia> SA[1 2; 3 4]
2×2 SMatrix{2, 2, Int64, 4} with indices SOneTo(2)×SOneTo(2):
 1  2
 3  4

Normal constructor functions:

julia> SVector(1, 2)
2-element SVector{2, Int64} with indices SOneTo(2):
 1
 2

julia> SMatrix{2,3}(1, 2, 3, 4, 5, 6)
2×3 SMatrix{2, 3, Int64, 6} with indices SOneTo(2)×SOneTo(3):
 1  3  5
 2  4  6

Macros:

julia> @SVector [1, 2, 3]
3-element SVector{3, Int64} with indices SOneTo(3):
 1
 2
 3

julia> @SMatrix [1 2; 3 4]
2×2 SMatrix{2, 2, Int64, 4} with indices SOneTo(2)×SOneTo(2):
 1  2
 3  4

Note that using macros also enables a convenient way to create StaticArrays from common array-creation functions (eliminating the need to create an Array first just to convert it immediately to a StaticArray):

@SVector [10 * i for i = 1:10]
@SVector zeros(5)
@SVector rand(7)
@SMatrix [(i, j) for i = 1:2, j = 1:3]
@SMatrix zeros(2, 2)
@SMatrix randn(6, 6)

Conversion to/from `Array`

It may occasionally be necessary to convert to or from Arrays. To convert from an Array to a StaticArray, use the appropriate constructor function. However, because Arrays do not have size information in the type, we ourselves must provide the size to the constructor:

SVector{3}([1, 2, 3])
SMatrix{4,4}(zeros(4, 4))

To convert back to an Array, use the collect function:

julia> collect(SVector(1, 2))
2-element Vector{Int64}:
 1
 2

Comparing `StaticArray`s to `Array`s

Once a StaticArray is created, it can be operated on in the same way as an Array. To illustrate, we will run a simple benchmark, both to compare the run-time speeds of the two types of arrays and to show that the same code can work with either type of array.

Stopwatch

Here's the benchmark code, inspired by StaticArrays.jl's benchmark:

using BenchmarkTools, StaticArrays, LinearAlgebra, Printf

add!(C, A, B) = C .= A .+ B

function run_benchmarks(N)

    A = rand(N, N); A = A' * A
    B = rand(N, N)
    C = Matrix{eltype(A)}(undef, N, N)
    D = rand(N)
    SA = SMatrix{N,N}(A)
    SB = SMatrix{N,N}(B)
    MA = MMatrix{N,N}(A)
    MB = MMatrix{N,N}(B)
    MC = MMatrix{N,N}(C)
    SD = SVector{N}(D)

    speedup = [
        @belapsed($A + $B) / @belapsed($SA + $SB),
        @belapsed(add!($C, $A, $B)) / @belapsed(add!($MC, $MA, $MB)),
        @belapsed($A * $B) / @belapsed($SA * $SB),
        @belapsed(mul!($C, $A, $B)) / @belapsed(mul!($MC, $MA, $MB)),
        @belapsed(norm($D)) / @belapsed(norm($SD)),
        @belapsed(det($A)) / @belapsed(det($SA)),
        @belapsed(inv($A)) / @belapsed(inv($SA)),
        @belapsed($A \ $D) / @belapsed($SA \ $SD),
        @belapsed(eigen($A)) / @belapsed(eigen($SA)),
        @belapsed(map(abs, $A)) / @belapsed(map(abs, $SA)),
        @belapsed(sum($D)) / @belapsed(sum($SD)),
        @belapsed(sort($D)) / @belapsed(sort($SD)),
    ]

    return speedup

end

function main()

    benchmarks = [
        "Addition",
        "Addition (in-place)",
        "Multiplication",
        "Multiplication (in-place)",
        "L2 Norm",
        "Determinant",
        "Inverse",
        "Linear Solve (A \\ b)",
        "Symmetric Eigendecomposition",
        "`map`",
        "Sum of Elements",
        "Sorting",
    ]
    N = [3, 5, 10, 30]
    speedups = map(run_benchmarks, N)
    fmt_header = Printf.Format("%-$(maximum(length.(benchmarks)))s" * " | %7s"^length(N))
    header = Printf.format(fmt_header, "Benchmark", string.("N = ", N)...)
    println(header)
    println("="^length(header))
    fmt = Printf.Format("%-$(maximum(length.(benchmarks)))s" * " | %7.1f"^length(N))
    for i = 1:length(benchmarks)
        println(Printf.format(fmt, benchmarks[i], getindex.(speedups, i)...))
    end

end

main()

Notice that all the functions called when creating the array speedup in run_benchmarks are the same whether using Arrays or StaticArrays, illustrating that StaticArrays are drop-in replacements for standard Arrays.

Running the above code prints the following results on my laptop (the numbers indicate the speedup of StaticArrays over normal Arrays; e.g., a value of 17.7 means using StaticArrays was 17.7 times faster than using Arrays):

Benchmark                    |   N = 3 |   N = 5 |  N = 10 |  N = 30
====================================================================
Addition                     |    17.7 |    14.5 |     7.9 |     2.0
Addition (in-place)          |     1.6 |     1.3 |     1.4 |     0.7
Multiplication               |     8.2 |     7.0 |     4.2 |     2.6
Multiplication (in-place)    |     1.9 |     5.9 |     3.0 |     1.0
L2 Norm                      |     4.2 |     4.0 |     5.4 |     9.7
Determinant                  |    66.6 |     2.5 |     1.3 |     0.9
Inverse                      |    54.8 |     5.9 |     1.8 |     0.9
Linear Solve (A \ b)         |    65.5 |     3.7 |     1.8 |     0.9
Symmetric Eigendecomposition |     3.7 |     1.0 |     1.0 |     1.0
`map`                        |    10.6 |     8.2 |     4.9 |     2.1
Sum of Elements              |     1.5 |     1.1 |     1.7 |     2.1
Sorting                      |     7.1 |     2.9 |     1.5 |     1.1

There are two main conclusions from this table. First, using StaticArrays instead of Arrays can result in some nice speed-ups! Second, the gains from using StaticArrays tend to diminish as the sizes of the arrays increase. So, you can't expect StaticArrays.jl to always magically make your code faster, but if your arrays are small enough (the recommendation being fewer than about 100 elements) then you can expect to see some good speed-ups.

Of course, the above code timed just individual operations; how much faster a particular application would be is a different matter.

For example, consider a physical simulation where many 3D vectors are manipulated over several time steps. Since 3D vectors are static in size (i.e., are 1D arrays with exactly three elements), such a situation is a prime example of where StaticArrays.jl is useful. To illustrate, here is an example (taken from the field of magnetic resonance imaging) of a physical simulation using Arrays vs using StaticArrays:

using BenchmarkTools, StaticArrays, LinearAlgebra

function sim_arrays(N)

    M = Matrix{Float64}(undef, 3, N)
    M[1,:] .= 0.0
    M[2,:] .= 0.0
    M[3,:] .= 1.0
    M2 = similar(M)

    (sinα, cosα) = sincosd(30)
    R = [1 0 0; 0 cosα sinα; 0 -sinα cosα]
    E1 = exp(-0.01)
    E2 = exp(-0.1)
    (sinθ, cosθ) = sincosd(1)
    F = [E2 * cosθ E2 * sinθ 0; -E2 * sinθ E2 * cosθ 0; 0 0 E1]
    FR = F * R
    C = [0, 0, 1 - E1]
    # Run for 100 time steps (each loop iteration does 2 time steps).
    for t = 1:50
        mul!(M2, FR, M)
        M2 .+= C
        mul!(M, FR, M2)
        M .+= C
    end
    total = sum(M; dims = 2)

    return complex(total[1], total[2])

end

function sim_staticarrays(N)

    M = fill(SVector(0.0, 0.0, 1.0), N)

    (sinα, cosα) = sincosd(30)
    R = @SMatrix [1 0 0; 0 cosα sinα; 0 -sinα cosα]
    E1 = exp(-0.01)
    E2 = exp(-0.1)
    (sinθ, cosθ) = sincosd(1)
    F = @SMatrix [E2 * cosθ E2 * sinθ 0; -E2 * sinθ E2 * cosθ 0; 0 0 E1]
    FR = F * R
    C = @SVector [0, 0, 1 - E1]
    # Run for 100 time steps (each loop iteration does 1 time step).
    for t = 1:100
        # Apply simulation dynamics to each 3D vector.
        for i = 1:length(M)
            M[i] = FR * M[i] + C
        end
    end
    total = sum(M)

    return complex(total[1], total[2])

end

function main(N)

    r1 = @btime sim_arrays($N)
    r2 = @btime sim_staticarrays($N)
    @assert r1 ≈ r2 # Make sure the results are the same.

end

The speed-ups on my laptop for different values of N were as follows:

N = 10: 14.6x faster
N = 100: 7.1x faster
N = 1000: 5.2x faster

(Here, N is the number of 3D vectors in the simulation, not the size of the StaticArrays.)

Note also that I wrote sim_arrays to be as performant as possible by doing in-place operations (like mul!), which has the unfortunate side effect of making the code a bit harder to read. Therefore, sim_staticarrays is both faster and easier to read!

As another example of how StaticArrays.jl can speed up a more involved application, see the DifferentialEquations.jl docs.

Summary

In this post, we discussed StaticArrays.jl. We saw that StaticArrays are drop-in replacements for regular Julia Arrays. We also saw that using StaticArrays can result in some nice speed-ups over using Arrays, at least when the sizes of the arrays are not too big.

Are array operations a bottleneck in your code? Try out StaticArrays.jl and then comment below how it helps!

Additional Links

StaticArrays.jl Docs
- Documentation for StaticArrays.jl.

Cover image background from https://openverse.org/image/875bf026-11ef-47a8-a63c-ee1f1877c156?q=circuit%20board%20array.

Mastering Efficient Array Operations with StaticArrays.jl in Julia

Discover techniques to accelerate array operations and matrix calculations using StaticArrays.jl.

Table of contents

How to Use StaticArrays.jl

Constructors

Conversion to/from `Array`

Comparing `StaticArray`s to `Array`s

Summary

Additional Links

Mastering Efficient Array Operations with StaticArrays.jl in Julia

Discover techniques to accelerate array operations and matrix calculations using StaticArrays.jl.

Table of contents

How to Use StaticArrays.jl

Constructors

Conversion to/from Array

Comparing StaticArrays to Arrays

Summary

Additional Links

Conversion to/from `Array`

Comparing `StaticArray`s to `Array`s