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Researcher profile

Noam Katz

Noam Katz contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceDistributed, Parallel, and Cluster Computingeess.SPNetworking and Internet Architecture

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Published work

2 published item(s)

preprint2026arXiv

Resilient AI Supercomputer Networking using MRC and SRv6

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 protocol, MRC, sprays across many paths and actively load-balances between them, eliminating the issue of flow collisions (2) the use of multi-plane Clos topologies to get the benefits of high switch radix and redundancy, allowing training clusters well over 100K GPUs to be built as two-tier topologies while increasing physical redundancy, and (3) the use of static source-routing using SRv6 to allow MRC the freedom to bypass failures by itself. We describe our experiences running MRC and static SRv6 routing in production in OpenAI and Microsoft's largest training clusters, where it has been used to train the latest frontier models. We demonstrate how MRC allows AI training jobs to ride out many network failures that previously would have interrupted training.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

CommUnet: U-net decoder for convolutional codes in communication

In recent years, deep neural networks have played a major role solving various challenges in two dimensional image processing.Fully Convolutional Networks (FCN) such as U-net have been shown to be highly successful at segmentation tasks for medical images analysis and denoising images taken in dark venues. This paper harnesses this well-known deep neural network for the channel decoding challenge recently proven to be suitable for deep neural networks.Previous work have successfully managed to decode convolutional codes using different architectures,such as Recurrent Neural Networks(RNN) and Fully Connected Neural Networks(FCNN) with promising results.However,these approaches are extremely costly in latency,computational resources and memory.This paper shows that taking the approach used in two dimensional image processing,by simple manipulation on the data in the preprocessing phase,achieves better results in a Bit Error Rate(BER) measurement with a large discount on the latency and the number of parameters required to maintain the neural decoder.

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