195 citations · 234 across the 15 of their papers we have counts for
11 papers · 1 filter
Bring Your Own Objective: Inter-operability of Network Objectives in Datacenters
Sanjoli Narang, Anup Agarwal, Venkat Arun +1
Datacenter networks are currently locked in a "tyranny of the single objective". While modern workloads demand diverse performance goals, ranging from coflow completion times, per-…
MonkeyTree: Near-Minimal Congestion for Multi-tenant Training via Migration
Anton A. Zabreyko, Weiyang Wang, Manya Ghobadi
We present MonkeyTree, the first system to mitigate network congestion in multi-tenant GPU clusters through job-migration based defragmentation rather than network-layer techniques…
LINC: An In-Network Coding Approach to Tame Packet Loss in Hybrid Wireless-Fiber Backbones
Benoit Pit-Claudel, Muriel Médard, Manya Ghobadi
The emergence of ultra-low latency applications, such as financial transactions, has driven the development of hybrid backbone networks that rely on fiber, satellite, and microwave…
Scalable Routing in a City-Scale Wi-Fi Network for Disaster Recovery
Ziqian Liu, Om Chabra, James Lynch +3
In this paper, we present a new city-scale decentralized mesh network system suited for disaster recovery and emergencies. When wide-area connectivity is unavailable or significant…
MLCC: A Congestion Control Technique to Accelerate ML Training
Anton A. Zabreyko, Sanjoli Narang, Sudarsanan Rajasekaran +1
We present MLCC, a novel technique to augment today's congestion control algorithms to accelerate DNN training jobs in shared GPU clusters in a fully distributed manner. At the hea…
CASSINI: Network-Aware Job Scheduling in Machine Learning Clusters
Sudarsanan Rajasekaran, Manya Ghobadi, Aditya Akella
We present CASSINI, a network-aware job scheduler for machine learning (ML) clusters. CASSINI introduces a novel geometric abstraction to consider the communication pattern of diff…
Rail-only: A Low-Cost High-Performance Network for Training LLMs with Trillion Parameters
Weiyang Wang, Manya Ghobadi, Kayvon Shakeri +2
This paper presents a low-cost network architecture for training large language models (LLMs) at hyperscale. We study the optimal parallelization strategy of LLMs and propose a nov…
TopoOpt: Co-optimizing Network Topology and Parallelization Strategy for Distributed Training Jobs
Weiyang Wang, Moein Khazraee, Zhizhen Zhong +5
We propose TopoOpt, a novel direct-connect fabric for deep neural network (DNN) training workloads. TopoOpt co-optimizes the distributed training process across three dimensions: c…
FB: A Flexible Buffer Management Scheme for Data Center Switches
Maria Apostolaki, Vamsi Addanki, Manya Ghobadi +1
Today, network devices share buffer across priority queues to avoid drops during transient congestion. While cost-effective most of the time, this sharing can cause undesired inter…
Performance Analysis of Demand-Oblivious and Demand-Aware Optical Datacenter Network Designs
Chen Griner, Johannes Zerwas, Andreas Blenk +3
This paper presents a performance analysis of the design space of optical datacenter networks, including both demand-oblivious (static or dynamic) and demand-aware networks. We for…
Measuring the Complexity of Packet Traces
Chen Avin, Manya Ghobadi, Chen Griner +1
This paper studies the structure of several real-world traces (including Facebook, High-Performance Computing, Machine Learning, and simulation generated traces) and presents a sys…