JuliaDiff / JuliaDiff/ReverseDiff.jl
Exploiting sparsity in higher-order differentiation computations
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- Dominant language
- Julia
- Stars
- 393
- Forks
- 60
- Avg merge
- 18h 24m
- Merged PRs (30d)
- 8
Description
We should explore the use of techniques such as edge-pushing, graph coloring, etc. to discover and exploit sparsity patterns in ReverseDiff's second-order derivative computations.
Maybe sparsity optimization could be employed automagically whenever nested tapes are encountered?
Contributor guide
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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 reviewing ReverseDiff's second-order derivative computations and how nested tapes are handled. Investigate the mentioned edge-pushing and graph-coloring techniques, then define a concrete sparsity optimization scope and measurable completion criteria before implementation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100