5 papers
Ofan: Optimal Load Balancing for AI Training
Sarah McClure, Evyatar Cohen, Jakob Krebs +5
The extreme collective completion time (CCT) demands of AI workloads challenge existing packet spraying algorithms, which can have trouble efficiently load-balancing workloads that…
Incast-Free MoE Rate-Based Scheduling
Evyatar Cohen, Jose Yallouz, Alexander Shpiner +3
Mixture of Experts (MoE) architectures have become key to large language models; however, their typical round-robin (RR) scheduling introduces significant bottlenecks. In this pape…
Avoiding Cross-Datacenter Collective Congestion via Disaggregated Buffering
Mariano Scazzariello, Noga H. Rotman, Dima Gavrilenko +6
LLM training at the scale of tens of thousands of GPUs now spans multiple datacenters (DC), making cross-DC collectives over long-haul links unavoidable. A critical and overlooked…
RFSeek and Ye Shall Find
Noga H. Rotman, Tiago Ferreira, Hila Peleg +2
Requests for Comments (RFCs) are extensive specification documents for network protocols, but their prose-based format and their considerable length often impede precise operationa…
Congestion Control for Spraying with Congested Paths
Barak Gerstein, Mark Silberstein, Isaac Keslassy
Packet spraying approaches are increasingly deployed in datacenter networks. However, their combination with existing congestion control algorithms (CCAs) may lead to poor QoS, esp…