Pre-allocating Arrays for intermediate calculations

To save on computation time for a function i often use, i created a preallocated array that i pass to the function to store intermediate calculations. As an example, albeit a contrived one, i have this function test() that I often need to use:

# Let N be some number representing some integer representing the array size
_b = Array{Float64,1}(undef,N)
function test( a, b = _b)
     for i in 1:100 
          b[i] += a + i
     return( b .* 100 )

In the example, i created a preallocated array and use it to store intermediate results in the hope to save on computation time. However, I wonder if this makes sense, if it could cause some unforeseen bugs later on and if there are better ways to go about reducing computation time.

Please advise.

Please note that i meant b[i] = a + i. Which is very different from b[i] += a + i. Sorry for the confusion. The responses below still hold. thanks for pointing out @DNF.

Rather than using global variables, it is often better to let the caller preallocate the workspace needed. The caller can then hoist that preallocation outside of the loop.


Hi @kristoffer.carlsson, thanks for your prompt response. Do you mean that I do not have to worry about preallocation within functions as the function will automatically do a preallocation? In the context of the given example, it means the object b, once created, will always exists till the programme stops?

No, more like

function test!( b, a)
    @inbounds @simd for i in 1:100 
        b[i] += a + i
    # Why not reuse "b" again?
    b .*= 100

function test_a_lot(N)
    _b = Array{Float64,1}(undef, 100)
    result = 0.0
    for n ∈ 1:N
        result += (-0.01)^n * sum(test!(_b, 0.5n))

That is, whatever function you’re using that is using test many times should manage preallocating the buffer.
If you’re only using test once, there isn’t going to be any benefit.

If you’re using it many times from many different places, maybe you can pass around a program state object that stores all the state and buffer variables, so you only need to manage a single object.


It’s a bit odd to go to the trouble of pre-allocating, only to throw away the benefit and make a new vector in the return statement…

You should definitely be careful with globals.

_b will be mutated each time you call test, so the returned result will be different each time, even with the same input. Is that intentional? And using undef and then incrementing, means that the output may contain garbage. Is that intentional too?

PS. Minor point: return is a keyword not a function, so it’s idiomatic to not use parens: return b .* 100

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Maybe capturing into a closure is the pattern you are looking for. Here’s what that could look like (with a similarly contrived example):

julia> f = let b=Vector{Int}(undef, 1000)
           function f(x)
              b .= (1:1000) .+ x
              return sum(b)
f (generic function with 1 method)

julia> f(3)

I think this expresses what you want to do in the nicest way. Depending on your use case, it does have a performance downside: . Do benchmark your code to check.

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I would argue that in most cases, defining a struct that holds the buffer and possibly making it callable is a better solution β€” it provides better introspection and maintainability, and of course you are less likely to run into the above bug.

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thanks @tkluck, this does indeed reflect what I hope to do. But as you cautioned, it could result in performance issues. Will keep that in mind.

thanks @Tamas_Papp, do you mean that I should define a struct that holds these interim objects that i can call when needed? Hope its not a stupid question, I code predominantly in R and dont usually find myself having to deal with these different types of programming tools.

thanks for pointing out @DNF, it should have been b[i] = a + i in the example.

Also thanks for pointing out that parentheses are not necessary for the keyword return

maybe an example helps:

struct BufferedContrivedExample{T}

BufferedContrivedExample(T, N::Integer) = BufferedContrivedExample(Vector{T}(undef, N))

function (bce::BufferedContrivedExample)(a)
    b = bce.buffer
    for i in axes(b, 1)
        b[i] += a + i
    b .* 100

bce = BufferedContrivedExample(Float64, 100);