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arXiv · 2508.03674

Morphlux: Transforming Torus Fabrics for Efficient Multi-tenant ML

Abhishek Vijaya Kumar·Eric Ding·Arjun Devraj·Darius Bunandar·Rachee Singh
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Abstract

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 with Morphlux can improve the bandwidth of tenant compute allocations by up to 66%, reduce compute fragmentation by up to 70%, and minimize the blast radius of chip failures. We develop a novel end-to-end hardware prototype of Morphlux to demonstrate these performance benefits which translate to 1.72X improvement in training throughput of ML models. By rapidly programming the server-scale fabric in our hardware testbed, Morphlux can replace a failed accelerator chip with a healthy one in 1.2 seconds.

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Abhishek Vijaya Kumar, Eric Ding, Arjun Devraj, Darius Bunandar, Rachee Singh. 2025-07-20. Morphlux: Transforming Torus Fabrics for Efficient Multi-tenant ML. https://arxiv.org/abs/2508.03674

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