Data partitioning assigns different portions of data to different computational resources. In distributed artificial intelligence, a tensor may be split by examples, features, model dimensions, experts, or sequence positions, depending on the algorithm and hardware topology.
A useful partition balances work while minimizing data exchange and duplication. Poor boundaries can create uneven load, excessive communication, difficult synchronization, or expensive reshaping. The best partition depends on tensor shape, operation order, memory capacity, and network links.
Acronyms and aliases
data sharding variantworkload partitioning variant
Related terms
Frequently asked questions
Why is data partitioning important for parallel computing?
It determines which processor owns each piece of work and how much data must be exchanged before the overall result can be completed.
What makes a data partition inefficient?
An inefficient partition creates imbalanced work, excessive communication, duplicated data, costly reshaping, or synchronization that leaves processors idle.
