195 citations · 234 across the 15 of their papers we have counts for
16 papers
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…
Checkmate: Zero-Overhead Model Checkpointing via Network Gradient Replication
Ankit Bhardwaj, Weiyang Wang, Jeremy Carin +2
This paper presents Checkmate, a system that enables per-iteration checkpointing in DNN training without any training slowdown. The traditional approach to checkpointing requires a…
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…
PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels
Homa Esfahanizadeh, Adam Yala, Rafael G. L. D'Oliveira +8
Allowing organizations to share their data for training of machine learning (ML) models without unintended information leakage is an open problem in practice. A promising technique…
InfoShape: Task-Based Neural Data Shaping via Mutual Information
Homa Esfahanizadeh, William Wu, Manya Ghobadi +2
The use of mutual information as a tool in private data sharing has remained an open challenge due to the difficulty of its estimation in practice. In this paper, we propose InfoSh…
Delocalized Photonic Deep Learning on the Internet's Edge
Alexander Sludds, Saumil Bandyopadhyay, Zaijun Chen +13
Advances in deep neural networks (DNNs) are transforming science and technology. However, the increasing computational demands of the most powerful DNNs limit deployment on low-pow…
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…
NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training
Adam Yala, Homa Esfahanizadeh, Rafael G. L. D' Oliveira +6
Balancing the needs of data privacy and predictive utility is a central challenge for machine learning in healthcare. In particular, privacy concerns have led to a dearth of public…
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…