JuliaGPU / JuliaGPU/DaggerGPU.jl
Usage example
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 55
- Forks
- 11
- PR merge metrics
- No merged PRs in 30d
Description
Hello, would it be possible to add a usage example? I couldn't find one here, nor in the Dagger.jl docs.
For example, let's say I have the following task:
# two large matrices
A = rand(1000, 1000)
B = rand(1000, 1000)
# move them to gpu and multiply there
A_gpu = CUDA.Matrix(A)
B_gpu = CUDA.Matrix(B)
C_gpu = A_gpu*B_gpu
# move back to cpu to use there.
C = Matrix(C_gpu)
Intuitively, with Dagger, I'd just try to write it like this:
# two large matrices
A = rand(1000, 1000)
B = rand(1000, 1000)
# move them to gpu and multiply there
A_gpu = Dagger.@spawn CUDA.Matrix(A)
B_gpu = Dagger.@spawn CUDA.Matrix(B)
C_gpu = Dagger.@spawn A_gpu*B_gpu
# move back to cpu to use there.
C = Dagger.@spawn Matrix(C_gpu)
What role does DaggerGPU.jl play here? It seems I could even do this with just Dagger.jl?
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
Review the existing Dagger.jl documentation and the Julia/CUDA usage shown in this issue. Explain what DaggerGPU.jl contributes compared with using Dagger.jl alone, and add a runnable GPU matrix example that makes the data movement and multiplication workflow clear.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- documentation, hpc
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100