These suggestions all create an intermediate array that seems like it is redundant. But it’s not so easy to come up with a trivial solution that avoids it. I thought maybe accumulate would help, but I actually need something like mapaccumulate:

mapaccumulate(exp, *, x; dims=1)

Unfortunately, there is no mapaccumulate.

Looks like a loop is needed, unless I’m missing something. Perhaps Transducers.jl has something helpful.

this is not equivalent to the other methods, something must be off. Your previous post is slightly faster than my original method.

I should add that the dimensions I gave for the example are way off of the true size. The number of columns is way way bigger than rows. randn(750,100000) is pretty close to the actual size of the input.

This approach is 4 times fast than the mapslices approach

using BenchmarkTools
function cumprod_exp(x)
y = similar(x)
foreach(
[view(y, :, i) for i in 1:size(y, 2)],
[view(x, :, i) for i in 1:size(x, 2)]
) do v, u
cumprod!(v, exp.(u))
end
return y
end
@btime cumprod_exp($(rand(10, 100)))

You can make it even faster if x is no longer needed

function cumprod_exp(x)
y = similar(x)
foreach(
[view(y, :, i) for i in 1:size(y, 2)],
[view(x, :, i) for i in 1:size(x, 2)]
) do v, u
u .= exp.(u)
cumprod!(v, u)
end
return y
end

Note, that you can easily save even more time by preallocating y – this just avoids the vcats in mapslices

Yea I ended up rolling my own along those lines once I realized that there was no in-place mapslices (why!?). I also started chaining together 2 additional operations, a trailing sum of N elements and then a log difference from N periods ago which meant that stuff would allocate regardless unless I was smart about those (since they don’t just use consecutive entries). I ended up using CircularBuffer from DataStructures.jl for that part. Now the only thing that allocates is the buffer.

I guess the limiting part is really just that I’m doing this 10s or 100s of thousands of times. Unfortunate that the way this is being used I can’t parallelize it across columns. Oh well. Thanks for the suggestions!