madeleineudell / madeleineudell/ParallelSparseMatMul.jl
Compute transpose in parallel
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 44
- Forks
- 13
- PR merge metrics
- No merged PRs in 30d
Description
Currently the transpose of the shared sparse matrix is computed in serial by calling transpose on a local version of the matrix: `transpose(A) = share(transpose(localize(A)))`. We should be able to transpose much more quickly using parallelism.
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 locating the shared sparse matrix transpose path described as transpose(A) = share(transpose(localize(A))). Read the surrounding implementation and existing tests or benchmarks, then determine how parallel transpose should be measured against the current serial path. Done means transpose uses parallelism without changing its result or shared-matrix behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- performance
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100