On the Computation Rate of All-Reduce
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866918357194768384 |
|---|---|
| author | Zhou, Yufeng Sun, Hua |
| author_facet | Zhou, Yufeng Sun, Hua |
| contents | In the All-Reduce problem, each one of the K nodes holds an input and wishes to compute the sum of all K inputs through a communication network where each pair of nodes is connected by a parallel link with arbitrary bandwidth. The computation rate of All-Reduce is defined as the number of sum instances that can be computed over each network use. For the computation rate, we provide a cut-set upper bound and a linear programming lower bound based on time (bandwidth) sharing over all schemes that first perform Reduce (aggregating all inputs at one node) and then perform Broadcast (sending the sum from that node to all other nodes). Specializing the two general bounds gives us the optimal computation rate for a class of communication networks and the best-known rate bounds (where the upper bound is no more than twice of the lower bound) for cyclic, complete, and hypercube networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22482 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | On the Computation Rate of All-Reduce Zhou, Yufeng Sun, Hua Information Theory Combinatorics In the All-Reduce problem, each one of the K nodes holds an input and wishes to compute the sum of all K inputs through a communication network where each pair of nodes is connected by a parallel link with arbitrary bandwidth. The computation rate of All-Reduce is defined as the number of sum instances that can be computed over each network use. For the computation rate, we provide a cut-set upper bound and a linear programming lower bound based on time (bandwidth) sharing over all schemes that first perform Reduce (aggregating all inputs at one node) and then perform Broadcast (sending the sum from that node to all other nodes). Specializing the two general bounds gives us the optimal computation rate for a class of communication networks and the best-known rate bounds (where the upper bound is no more than twice of the lower bound) for cyclic, complete, and hypercube networks. |
| title | On the Computation Rate of All-Reduce |
| topic | Information Theory Combinatorics |
| url | https://arxiv.org/abs/2602.22482 |