# How to avoid memory allocation while doing sum on a GPU?
**URL:** https://discourse.julialang.org/t/how-to-avoid-memory-allocation-while-doing-sum-on-a-gpu/128225
**Category:** General Usage
**Tags:** cuda, memory-allocation, cudajl
**Created:** [April 19, 2025, 3:06pm UTC](https://discourse.julialang.org/t/how-to-avoid-memory-allocation-while-doing-sum-on-a-gpu/128225 "2025-04-19T15:06:01Z")
**Posts on this page:** 8
**Page:** 1
### Author:  [@Shashank](https://discourse.julialang.org/u/Shashank)
#### Post date: [April 19, 2025, 3:06pm UTC](https://discourse.julialang.org/t/how-to-avoid-memory-allocation-while-doing-sum-on-a-gpu/128225/1 "2025-04-19T15:06:01Z")
I have been having a problem with memory allocation when using sum on a CUDA array. Is there a way to avoid it? I have pasted the relevant parts of the code below:
```julia
println("3:", CUDA.memory_status())
Cgain1_reshaped_for_sum .= sum(u_times_cth,dims=(2,))
println("4:", CUDA.memory_status())
```
The output of the code is given below:
```julia
3:nothing
Effective GPU memory usage: 2.43% (2.265 GiB/93.096 GiB)
Memory pool usage: 1.513 GiB (1.562 GiB reserved)
4:nothing
Effective GPU memory usage: 2.53% (2.358 GiB/93.096 GiB)
Memory pool usage: 1.597 GiB (1.656 GiB reserved)
```
This is not a large increase in memory allocation, but the above statements are in a loop, and the memory allocation continues to increase. I can solve this problem using GC.gc() at the beginning of each iteration, but then the code becomes very slow. Is there a way to avoid memory allocation when doing a sum on GPUs? It should be noted that Cgain1\_reshaped\_for\_sum and u\_times\_cth are preallocated CUDA arrays. So I do not understand where the memory allocation is coming from. I tried this on A100 and H100 chips, and the result is the same.
Any suggestions on how to avoid allocation would be very much appreciated.
---
### Author:  [@eldee](https://discourse.julialang.org/u/eldee)
#### Post date: [April 19, 2025, 4:30pm UTC](https://discourse.julialang.org/t/how-to-avoid-memory-allocation-while-doing-sum-on-a-gpu/128225/2 "2025-04-19T16:30:19Z")
> [@Shashank](#):
>
> `Cgain1_reshaped_for_sum .= sum(u_times_cth,dims=(2,))`
Here you’re first creating a new `CuArray` containing the sum, which you then copy over into `Cgain1_reshaped_for_sum`. Instead you can use `sum!`:
```julia-repl
julia> using CUDA
julia> x = CUDA.rand(10^6, 10^3);
julia> y = CuArray{Float32}(undef, size(x, 1), 1);
julia> CUDA.@allocated y .= sum(x, dims=2)
4000000
julia> CUDA.@allocated sum!(y, x)
0
julia> y == sum(x, dims=2)
true
```
---
### Author:  [@Shashank](https://discourse.julialang.org/u/Shashank)
#### Post date: [April 19, 2025, 4:34pm UTC](https://discourse.julialang.org/t/how-to-avoid-memory-allocation-while-doing-sum-on-a-gpu/128225/3 "2025-04-19T16:34:41Z")
Thanks, but I tried it and it does not work because sum! does not allow the argument dims. So it only works for 1d arrays. If there is a 4d array, which is what I have, then I cannot sum over the second axis like I am doing. Is there a workaround to make sum! work for multi-dimensional arrays?
---
### Author:  [@eldee](https://discourse.julialang.org/u/eldee)
#### Post date: [April 19, 2025, 4:53pm UTC](https://discourse.julialang.org/t/how-to-avoid-memory-allocation-while-doing-sum-on-a-gpu/128225/4 "2025-04-19T16:53:28Z")
> [@Shashank](#):
>
> So it only works for 1d arrays
Well, in my example above, `x` is a 2D array 🙂 . The trick is to insert `1`s into the shape of the preallocated output.
```julia-repl
help?> sum!
search: sum! sum cumsum! put! dump
sum!(r, A)
Sum elements of A over the singleton dimensions of r, and write results to r.
(...)
```
You could also use `reshape` for that.
```julia-repl
julia> x = CUDA.ones(2, 3, 4);
julia> y = CuArray{Float32}(undef, 2, 4); # will contain sum(x, dims=2)
julia> sum!(reshape(y, (2, 1, 4)), x); y
2×4 CuArray{Float32, 2, CUDA.DeviceMemory}:
3.0 3.0 3.0 3.0
3.0 3.0 3.0 3.0
```
---
### Author:  [@Shashank](https://discourse.julialang.org/u/Shashank)
#### Post date: [April 19, 2025, 5:01pm UTC](https://discourse.julialang.org/t/how-to-avoid-memory-allocation-while-doing-sum-on-a-gpu/128225/5 "2025-04-19T17:01:26Z")
Sorry about that. I did not understand the syntax for sum! but I am surprised that it does not resolve my problem. If I run the code below:
```julia
u_reshaped = reshape(u, (dof, cbins, ebins, rbins))
du_reshaped = reshape(du, (dof, cbins, ebins, rbins))
Cgain0_reshaped .= reshape(sum(u_reshaped, dims=(2,)),(dof,ebins,rbins)) .* dctharr
println("2: ",CUDA.memory_status())
cdirchhere_reshaped = reshape(cdirchhere, (dof, ebins, rbins))
bkpeakhere_reshaped = reshape(bkpeakhere, (rbins, 1, 1, 1))
ctharr_reshaped = reshape(ctharr, (1, cbins, 1, 1))
stharr_reshaped = reshape(stharr, (1, cbins, 1, 1))
rarr_reshaped = reshape(rarr,(1,1,1,rbins))
cgrad_prefactor .= -(stharr_reshaped .* stharr_reshaped ./ rarr_reshaped) / dctharr
#println("3:", CUDA.memory_status())
println(size(cgrad_prefactor))
println(size(u_times_cth)," ",size(u_reshaped)," ",size(ctharr_reshaped))
u_times_cth .= u_reshaped .* ctharr_reshaped
println("3:", CUDA.memory_status())
sum!(Cgain1_reshaped_for_sum,u_times_cth)
println("4:", CUDA.memory_status())
Cgain1_reshaped .= reshape(Cgain1_reshaped_for_sum, (dof, ebins, rbins)) .* dctharr
```
I get the output:
```julia
2: nothing
(1, 75, 1, 150)
(8, 75, 25, 150) (8, 75, 25, 150) (1, 75, 1, 1)
Effective GPU memory usage: 1.93% (1.796 GiB/93.096 GiB)
Memory pool usage: 1.184 GiB (1.219 GiB reserved)
3:nothing
Effective GPU memory usage: 1.93% (1.796 GiB/93.096 GiB)
Memory pool usage: 1.184 GiB (1.219 GiB reserved)
4:nothing
Effective GPU memory usage: 2.03% (1.890 GiB/93.096 GiB)
Memory pool usage: 1.267 GiB (1.312 GiB reserved)
```
There is clearly memory allocation happening between 3: and 4: but I do not understand why.
---
### Author:  [@eldee](https://discourse.julialang.org/u/eldee)
#### Post date: [April 19, 2025, 5:14pm UTC](https://discourse.julialang.org/t/how-to-avoid-memory-allocation-while-doing-sum-on-a-gpu/128225/6 "2025-04-19T17:14:31Z")
Hmm, strange… Could you extract a MWE?
---
### Author:  [@MrBelette](https://discourse.julialang.org/u/MrBelette)
#### Post date: [April 19, 2025, 8:35pm UTC](https://discourse.julialang.org/t/how-to-avoid-memory-allocation-while-doing-sum-on-a-gpu/128225/7 "2025-04-19T20:35:19Z")
Could it be [Inconsistency between CUDA.jl and Base for sum! · Issue #2506 · JuliaGPU/CUDA.jl · GitHub](https://github.com/JuliaGPU/CUDA.jl/issues/2506)?
---
### Author:  [@maleadt](https://discourse.julialang.org/u/maleadt)
#### Post date: [April 20, 2025, 7:00am UTC](https://discourse.julialang.org/t/how-to-avoid-memory-allocation-while-doing-sum-on-a-gpu/128225/8 "2025-04-20T07:00:48Z")
When a reduction is large and happens in multiple steps, we allocate a temporary container: [CUDA.jl/src/mapreduce.jl at 689f98587c2587614e13047fbcdd417b52749edd · JuliaGPU/CUDA.jl · GitHub](https://github.com/JuliaGPU/CUDA.jl/blob/689f98587c2587614e13047fbcdd417b52749edd/src/mapreduce.jl#L263-L264). This could be the source, and could be avoided by doing atomic operations from the kernel instead. Or maybe we could add a call to `unsafe_free!` at the end of `mapreducedim!`, which should behave similarly to you calling `GC.gc`.
Also, better use `CUDA.@time` to report on the number/amount instead of parsing the diff between calls to `CUDA.memory_status()`.