This innocent question from a newcomer in the official Slack workspace of the Julia programming language

How can I extract the Float64 element from a 1x1 Array{Float64, 1}?

spurred a series of more or less serious answers:

• use the only function: only(a)
• get the element with index 1: a[1]
• omit all indices: a[]
• use the first function: first(a)
• get the element with index 1, 1: a[1, 1]
• use the last function: last(a)
• get the element with index 1, 1, 1, 1, 1, …: a[ones(Int, 100)...] (the ... operator is called splatting)
• get the last element with the special end index (end here is automatically lowered to lastindex(a)): a[end]
• get the element in the first row and last column: a[begin, end]
• reduce the array with the sum function: reduce(sum, a)
• get the element with index floor(Int, -real(exp(π * im))): a[floor(Int, -real(exp(π * im)))], or the variant x[floor(Int, -real(cispi(1)))]
• dereference the pointer of a (remember?): unsafe_load(pointer(a))
• use the StarWarsArrays.jl package and get the fourth element (mentioned in the post about random indexing): StarWarsArray(a)[4]
• use the getindex function (a[n, ...] is a syntactic sugar for getindex(a, n, ...)): getindex(a, firstindex(a))
• get a random element from the array: rand(a)

## Benchmarking

We can compare the performance of all these versions. To do this, we can use the BenchmarkTools.jl package (benchmarks kindly provided by @Seelengrab):

using BenchmarkTools # to install it: ]add BenchmarkTools
using StarWarsArrays # to install it: ]add https://github.com/giordano/StarWarsArrays.jl
using Random # it's a standard library

only_bench(x)         = only(x)
one_idx_bench(x)      = x[1]
omit_bench(x)         = x[]
first_bench(x)        = first(x)
two_ones_idx_bench(x) = x[1, 1]
splat_bench(x)        = x[ones(Int, 100)...]
end_bench(x)          = x[end]
begin_end_bench(x)    = x[begin, end]
reduce_sum_bench(x)   = reduce(sum, x)
floor_bench(x)        = x[floor(Int, -real(exp(π * im)))]
cispi_bench(x)        = x[floor(Int, -real(cispi(1)))]
getindex_bench(x)     = getindex(x, firstindex(x))
starwars_bench(x)     = StarWarsArray(x)[4] # ಠ_ಠ
rand_bench(x)         = rand(x)

for f in [
only_bench,
one_idx_bench,
omit_bench,
first_bench,
two_ones_idx_bench,
splat_bench,
end_bench,
begin_end_bench,
reduce_sum_bench,
floor_bench,
cispi_bench,
dereference_bench,
getindex_bench,
starwars_bench,
rand_bench,
]
println(f, ':')
@btime $f(x) setup=(x=rand(1,1)) end  On my computer I get only_bench: 2.800 ns (0 allocations: 0 bytes) one_idx_bench: 1.631 ns (0 allocations: 0 bytes) omit_bench: 2.796 ns (0 allocations: 0 bytes) first_bench: 1.630 ns (0 allocations: 0 bytes) two_ones_idx_bench: 1.685 ns (0 allocations: 0 bytes) splat_bench: 4.524 μs (5 allocations: 1.75 KiB) end_bench: 1.378 ns (0 allocations: 0 bytes) begin_end_bench: 1.644 ns (0 allocations: 0 bytes) reduce_sum_bench: 1.901 ns (0 allocations: 0 bytes) floor_bench: 1.630 ns (0 allocations: 0 bytes) cispi_bench: 1.631 ns (0 allocations: 0 bytes) dereference_bench: 1.369 ns (0 allocations: 0 bytes) getindex_bench: 1.371 ns (0 allocations: 0 bytes) starwars_bench: 1.371 ns (0 allocations: 0 bytes) rand_bench: 9.599 ns (0 allocations: 0 bytes)  Unsurprisingly, the fastest solution is dereferencing a pointer, which compiles down to the following code julia> x = rand(1,1); julia> @code_native debuginfo=:none dereference_bench(x) .text movq (%rdi), %rax vmovsd (%rax), %xmm0 # xmm0 = mem[0],zero retq nopl (%rax,%rax)  This is pretty much what you’d expect from the corresponding C code. What’s remarkable is that the StarWarsArrays.jl package achieves about the same performance: accessing an element of a custom data structure using a funny custom indexing, and written without having performance in mind, is just as efficient as dereferencing a pointer. However, subnanosecond differences aren’t credible: most functions that clocked in the range 1.3-1.6 nanoseconds have basically the same performance. If you run the benchmarks again you’ll find that their performance get more or less close to that of dereferencing. The difference with dereferencing is that all other functions do bounds checking, which on average lead to slightly larger runtime. However, in high-performance computing you usually want to make sure to be within bounds at a higher level and skip bounds checks when accessing individual elements. If you’re 100% sure that the element of the array you want to access is within its bounds, you can use the macro @inbounds to skip runtime bounds checks. To see the performance when skipping bounds checks in the above functions, you can either use @inbounds in their definitions (e.g., one_idx_bench(x) = @inbounds x[1], etc…), or start Julia with the --check-bounds=no flag (this flag is not recommended for general use, as it will skip all bounds checks, not only where you know it’s safe to do so). With --check-bounds=no I get only_bench: 2.725 ns (0 allocations: 0 bytes) one_idx_bench: 1.369 ns (0 allocations: 0 bytes) omit_bench: 1.369 ns (0 allocations: 0 bytes) first_bench: 1.369 ns (0 allocations: 0 bytes) two_ones_idx_bench: 1.369 ns (0 allocations: 0 bytes) splat_bench: 4.510 μs (5 allocations: 1.75 KiB) end_bench: 1.369 ns (0 allocations: 0 bytes) begin_end_bench: 1.630 ns (0 allocations: 0 bytes) reduce_sum_bench: 1.960 ns (0 allocations: 0 bytes) floor_bench: 1.408 ns (0 allocations: 0 bytes) cispi_bench: 1.369 ns (0 allocations: 0 bytes) dereference_bench: 1.369 ns (0 allocations: 0 bytes) getindex_bench: 1.368 ns (0 allocations: 0 bytes) starwars_bench: 1.369 ns (0 allocations: 0 bytes) rand_bench: 9.606 ns (0 allocations: 0 bytes)  As expected, most functions now more consistently perform as well as dereferencing. This is confirmed by the fact they are all compiled down to the same efficient code, even the most bizarre ones like floor_bench and starwars_bench: julia> @code_native debuginfo=:none omit_bench(x) .text movq (%rdi), %rax vmovsd (%rax), %xmm0 # xmm0 = mem[0],zero retq nopl (%rax,%rax) julia> @code_native debuginfo=:none floor_bench(x) .text movq (%rdi), %rax vmovsd (%rax), %xmm0 # xmm0 = mem[0],zero retq nopl (%rax,%rax) julia> @code_native debuginfo=:none getindex_bench(x) .text movq (%rdi), %rax vmovsd (%rax), %xmm0 # xmm0 = mem[0],zero retq nopl (%rax,%rax) julia> @code_native debuginfo=:none starwars_bench(x) .text movq (%rdi), %rax vmovsd (%rax), %xmm0 # xmm0 = mem[0],zero retq nopl (%rax,%rax)  ## Lessons learned • To seriously answer the question that led to this digression, I think the first answers are all good ways to access the only element of a 1x1 array: only(a) (which however always does a runtime check to make sure that’s the only element of the array, incurring a performance hit), a[1], a[begin] a[1, 1], a[], first(a) • Custom Julia data types are efficient! The fact that a custom data type with custom indexing like StarWarsArray has basically the same performance as dereferencing a pointer (exactly same native code when skipping all bounds checks) is a testament to the power and expresiveness of the language: you can write your code in a high-level form, and have it compiled down to very efficient code • Splatting a long array can be bad for performance, both at compile- and run-time. Splatting is a convenient syntax, but use it with great care, especially when you have many elements • Reduction has relatively little overhead • rand(a) performed quite bad, but actually most of the time is spent in accessing the global default random number generator (RNG): if you care about performance (and reproducibility, too) you should always pass in the RNG as argument (see also The need for rand speed by Bogumił Kamiński):  julia> @btime rand(x) setup=(x=rand(1,1)) 9.596 ns (0 allocations: 0 bytes) 0.08218288442687438 julia> @btime rand($(Random.default_rng()), x) setup=(x=rand(1,1))
4.770 ns (0 allocations: 0 bytes)
0.8112313537560083


In the second benchmark we explicitly passed the default RNG to rand and can see that avoiding accessing the global RNG saves more than 50% of runtime. Performance is still far from ideal though. As an aside, drawing a random element from a short tuple (<= 4 elements) is optimised, and we can get again the same performance as derefercing a pointer for a 1-element tuple:

julia> @btime rand(\$(Random.default_rng()), Ref(x)[]) setup=(x=(rand(),))
1.369 ns (0 allocations: 0 bytes)
0.4308805082102649

julia> @code_native debuginfo=:none rand(Random.default_rng(), (rand(),))
.text
vmovsd  (%rsi), %xmm0                   # xmm0 = mem[0],zero
retq
nopw    %cs:(%rax,%rax)