Author

Apoorve Mohan

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Preprint Oct 2026

TRANSIT: Transparent Scale-in for Multi-Node LLM Training

TRANSIT is a transparent scale-in framework to enable multi-node model training on fewer GPUs while maintaining training efficiency by transparently leveraging CPU DRAM as an extension of GPU memory during distributed training. It achieves this through a user-space interposition layer, requiring no modifications to the...

Hyungyo Kim, Nicholas Satchanov, Hrishi Shah et al. · 0 citations

Preprint Sep 2026

Towards Continuous Profiling and Optimization of Quantum-Classical Pipelines

LLQM (Low-Level Quantum Machine), a profiling-driven meta-framework for quantum-classical pipelines that decomposes pipelines into fine-grained tasks and continuously profiles their CPU/GPU, memory, QPU, and queue dependencies alongside real-time hardware states, is presented.

Ayush Bansal, Owen Cochell, Santiago Núñez-Corrales et al. · 0 citations

#machine learning Preprint Sep 2026

Tuning Collective Patterns to Alleviate Congestion in Shared AI Clusters

This paper builds a system, REACT, that tunes the recurring pattern of data exchange between GPU nodes (known as communication collectives) in response to congestion, and improves communication performance by 13%-38% under network congestion.

Eashan Gupta, Yong-Zhou Chen, Apoorve Mohan et al. · 0 citations