activity
20242026
collaborators
Showing 2025Show all

9 papers · 1 filter

cs.NI2025

Stable and Fault-Tolerant Decentralized Traffic Engineering

Arjun Devraj, Umesh Krishnaswamy, Ying Zhang +4

Cloud providers have recently decentralized their wide-area network traffic engineering (TE) systems to contain the impact of TE controller failures. In the decentralized design, a…

cs.DC2025

PCCL: Photonic circuit-switched collective communication for distributed ML

Abhishek Vijaya Kumar, Arjun Devraj, Rachee Singh

Modern distributed ML suffers from a fundamental gap between the theoretical and realized performance of collective communication algorithms due to congestion and hop-count induced…

cs.NI2025

Morphlux: Transforming Torus Fabrics for Efficient Multi-tenant ML

Abhishek Vijaya Kumar, Eric Ding, Arjun Devraj +2

We develop Morphlux, a server-scale programmable photonic fabric to interconnect accelerators within servers. We show that augmenting state-of-the-art torus-based ML data-centers w…

cs.NI2025

Photonic Rails in ML Datacenters

Eric Ding, Chuhan Ouyang, Rachee Singh

Rail-optimized network fabrics have become the de facto datacenter scale-out fabric for large-scale ML training. However, the use of high-radix electrical switches to provide all-t…

cs.DC2025

FlashMoE: Fast Distributed MoE in a Single Kernel

Osayamen Jonathan Aimuyo, Byungsoo Oh, Rachee Singh

The computational sparsity of Mixture-of-Experts (MoE) models enables sub-linear growth in compute cost as model size increases, thus offering a scalable path to training massive n…

cs.LG2025

Efficient AllReduce with Stragglers

Arjun Devraj, Eric Ding, Abhishek Vijaya Kumar +2

Distributed machine learning workloads use data and tensor parallelism for training and inference, both of which rely on the AllReduce collective to synchronize gradients or activa…

cs.LG2025

LUMION: Fast Fault Recovery for ML Jobs Using Programmable Optical Fabrics

Abhishek Vijaya Kumar, Eric Ding, Arjun Devraj +2

When accelerators fail in modern ML datacenters, operators migrate the affected ML training or inference jobs to entirely new racks. This approach, while preserving network perform…

cs.NI2025

Chip-to-chip photonic connectivity in multi-accelerator servers for ML

Abhishek Vijaya Kumar, Arjun Devraj, Darius Bunandar +1

We present a rack-scale compute architecture for ML using multi-accelerator servers connected via chip-to-chip silicon photonic components. Our architecture achieves (1) multi-tena…

cs.NI2025

PipSwitch: A Circuit Switch Using Programmable Integrated Photonics

Eric Ding, Rachee Singh

We present an optical circuit switch design for programmable integrated photonics (PIPs). Our solution finds the correct and optimal set of matchings that provides all-to-all netwo…