1 citations · 1 across the 3 of their papers we have counts for
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TRANSIT: Transparent Scale-in for Multi-Node LLM Training
Hyungyo Kim, Nicholas Satchanov, Hrishi Shah +9
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 exten…
Towards Continuous Profiling and Optimization of Quantum-Classical Pipelines
Ayush Bansal, Owen Cochell, Santiago Núñez-Corrales +4
Quantum applications increasingly execute as multi-stage quantum-classical pipelines, interleaving QPU computation with classical stages like circuit generation, transpilation, lay…
Tuning Collective Patterns to Alleviate Congestion in Shared AI Clusters
Eashan Gupta, Yongzhou Chen, Apoorve Mohan +3
Distributed AI training involves recurring rounds of data exchange between multiple pairs of GPU nodes. Slowdown in even one flow due to congestion can cause the entire communicati…