O-16: distarray memory-mapped array functionality (optional)
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- numpy, python
- Domain
- data, distributed-systems
Research direction
The issue names no files, tests, or entry points. Start by locating ODIN's array backing-store implementation and reviewing NumPy's memory-mapped array functionality. Done means ODIN can use memory-mapped arrays as a backing store for distributed computations on arrays larger than main memory.
Written by the indexing model from the issue text.
Description
Objective: To provide functionality that allows ODIN to use memory-mapped arrays as
the backing store for data.
Relevance: This provides support for users who have large storage capacity on, e.g. a workstation, and cannot fit an entire array in main memory. By allowing the backing store to use memory-mapped arrays, these users can perform distributed computations on arrays larger than can fit in main memory.
Description: The initial implementation of this task will likely build on top of NumPy’s existing memory-mapped array functionality. Based on this initial implementation and input from the distributed array protocol design, refinements will be made accordingly.
- Dominant language
- Python
- Stars
- 5
- Forks
- 1
- PR merge metrics
- No merged PRs in 30d
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.
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