11 citations · 23 across the 8 of their papers we have counts for
10 papers · 1 filter
PCM: Programmable Congestion Management for Terabit RDMA NICs
Mikhail Khalilov, Timo Schneider, Tommaso Bonato +10
Datacenter congestion control (CC) and load balancing (LB) evolve rapidly as workload demands change, but deployment depends on RDMA hardware and transport interfaces. Emerging pro…
The Multipath Reliable Connection (MRC) Transport
Rip Sohan, Eric Spada, Eric Davis +36
MRC is an open, production-grade transport designed for large-scale AI/ML training over best-effort Ethernet. It extends RoCEv2 with explicit, composable primitives for per-packet…
Resilient AI Supercomputer Networking using MRC and SRv6
Joao Araujo, Alex Chow, Mark Handley +47
Tail latency dominates the performance of synchronous pretraining jobs when running at very large scales. We describe a three-pronged approach: (1) a new RDMA-based transport proto…
In-Network Collective Operations: Game Changer or Challenge for AI Workloads?
Torsten Hoefler, Mikhail Khalilov, Josiah Clark +9
This paper summarizes the opportunities of in-network collective operations (INC) for accelerated collective operations in AI workloads. We provide sufficient detail to make this i…
Uno: A One-Stop Solution for Inter- and Intra-Datacenter Congestion Control and Reliable Connectivity
Tommaso Bonato, Sepehr Abdous, Abdul Kabbani +11
Cloud computing and AI workloads are driving unprecedented demand for efficient communication within and across datacenters. However, the coexistence of intra- and inter-datacenter…
Ultra Ethernet's Design Principles and Architectural Innovations
Torsten Hoefler, Karen Schramm, Eric Spada +13
The recently released Ultra Ethernet (UE) 1.0 specification defines a transformative High-Performance Ethernet standard for future Artificial Intelligence (AI) and High-Performance…
SDR-RDMA: Software-Defined Reliability Architecture for Planetary Scale RDMA Communication
Mikhail Khalilov, Siyuan Shen, Marcin Chrapek +16
RDMA is vital for efficient distributed training across datacenters, but millisecond-scale latencies complicate the design of its reliability layer. We show that depending on long-…
STrack: A Reliable Multipath Transport for AI/ML Clusters
Yanfang Le, Rong Pan, Peter Newman +5
Emerging artificial intelligence (AI) and machine learning (ML) workloads present new challenges of managing the collective communication used in distributed training across hundre…
REPS: Recycled Entropy Packet Spraying for Adaptive Load Balancing and Failure Mitigation
Tommaso Bonato, Abdul Kabbani, Ahmad Ghalayini +7
Next-generation datacenters require highly efficient network load balancing to manage the growing scale of artificial intelligence (AI) training and general datacenter traffic. How…
SMaRTT: Sender-based Marked Rapidly-adapting Trimmed & Timed Transport
Tommaso Bonato, Abdul Kabbani, Ahmad Ghalayini +10
With the rapid growth of artificial intelligence (AI) workloads in datacenters, the Ultra Ethernet Consortium (UEC) has defined a new high-performance transport layer to deliver th…