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Jithin Jose

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Artificial Intelligence Distributed, Parallel, and Cluster Computing Networking and Internet Architecture physics.med-ph

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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.

preprint2012arXiv

Speed-of-sound compensated photoacoustic tomography for accurate imaging

In most photoacoustic (PA) measurements, variations in speed-of-sound (SOS) of the subject are neglected under the assumption of acoustic homogeneity. Biological tissue with spatially heterogeneous SOS cannot be accurately reconstructed under this assumption. We present experimental and image reconstruction methods with which 2-D SOS distributions can be accurately acquired and reconstructed, and with which the SOS map can be used subsequently to reconstruct highly accurate PA tomograms. We begin with a 2-D iterative reconstruction approach in an ultrasound transmission tomography (UTT) setting, which uses ray refracted paths instead of straight ray paths to recover accurate SOS images of the subject. Subsequently, we use the SOS distribution in a new 2-D iterative approach, where refraction of rays originating from PA sources are accounted for in accurately retrieving the distribution of these sources. Both the SOS reconstruction and SOS-compensated PA reconstruction methods utilize the Eikonal equation to model acoustic wavefront propagation, which is solved using a high accuracy fast marching method (HAFMM). We validate the new reconstruction algorithms using numerical phantoms. For experiments we use the PER-PACT method which can be used to simultaneously acquire SOS and PA data from subjects. We test the reconstruction algorithms using experimental data acquired with the PER-PACT setup from challenging physical phantoms. The results show that it is important to take SOS inhomogeneities into account. The iterative reconstruction algorithms, that model acoustic refractive effects, yield artifact-free highly accurate images. Our approach of using the hybrid measurement method and the new reconstruction algorithms, is successful in substantially improving the quality of PA images with a minimization of blurring and artefacts.