QuantEcon / QuantEcon/QuantEcon.py
Parallelization of DiscreteDP solvers
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- Python
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Description
Is it possible to modify the solvers within DiscreteDP so that they can exploit multiple cores? This is a question from a user (Jack Shin) and I'm sure it would be very valuable to users if that could be done transparently (and if they had access to a machine with sufficiently many cores).
Same question goes for the Julia code, come to think of it.
@spencerlyon2 @cc7768 @oyamad Any thoughts?
Contributor guide
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 DiscreteDP solvers in the Python project and identifying any corresponding Julia implementation. Determine which solver operations could safely use multiple cores and what transparent behavior should mean for users. Done requires a concrete parallelization design, implementation scope, and validation approach for both codebases.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia, python
- Domain
- performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100