JuliaGPU / JuliaGPU/KernelAbstractions.jl
3d convolutions
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 523
- Forks
- 88
- Avg merge
- 1d 11h
- Merged PRs (30d)
- 25
Description
Hello I do not see anything that will make it impossible to use this package with 3d convolutions (for example for each voxel in CT scan I want to calculate the mean and standard deviation of its neighbours ... ) yet I can not find a way I looked into NNlib of FLux but I was unable to fuse it with this library I also have done Nvidia Cuda C course so I understand basics of indexing etc. ... still I suppose that there is already good performant and correct way to do it in julia, that I am just unaware of , I would be very gratefull for any help in pointing out how to start it.
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by comparing the 3D convolution needs described here with the relevant APIs in NNlib and Flux, which the report mentions. Determine whether KernelAbstractions already exposes a suitable path for voxel-neighbour calculations; done would require a documented, performant and correct way to perform the requested 3D operation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- backend
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100