AWhat changed
Built a compiler and runtime that jointly searches inter-operator pipeline plans and intra-operator sharding to distribute deep-learning training across device meshes.
Scientific scope
Built a compiler and runtime that jointly searches inter-operator pipeline plans and intra-operator sharding to distribute deep-learning training across device meshes. The hierarchical plan is not guaranteed globally optimal; benchmarks concern specified models and clusters. The original repository is now archived, so this is a historical research system rather than a current maintenance endorsement.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. The hierarchical plan is not guaranteed globally optimal; benchmarks concern specified models and clusters. The original repository is now archived, so this is a historical research system rather than a current maintenance endorsement.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
- Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning
- Zheng et al.: Alpa automatic hierarchical parallelization — author/proceedings record
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
Full scientific record · Browse contribution grades · Request a correction