17 citations · 23 across the 6 of their papers we have counts for
6 papers
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…
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…
SFC: Near-Source Congestion Signaling and Flow Control
Yanfang Le, Jeongkeun Lee, Jeremias Blendin +11
State-of-the-art congestion control algorithms for data centers alone do not cope well with transient congestion and high traffic bursts. To help with these, we revisit the concept…
Efficient Data-Plane Memory Scheduling for In-Network Aggregation
Hao Wang, Yuxuan Qin, ChonLam Lao +3
As the scale of distributed training grows, communication becomes a bottleneck. To accelerate the communication, recent works introduce In-Network Aggregation (INA), which moves th…
PL2: Towards Predictable Low Latency in Rack-Scale Networks
Yanfang Le, Radhika Niranjan Mysore, Lalith Suresh +4
High performance rack-scale offerings package disaggregated pools of compute, memory and storage hardware in a single rack to run diverse workloads with varying requirements, inclu…