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cs.LG2025
OmniLearn: A Framework for Distributed Deep Learning over Heterogeneous Clusters
Sahil Tyagi, Prateek Sharma
Deep learning systems are optimized for clusters with homogeneous resources. However, heterogeneity is prevalent in computing infrastructure across edge, cloud and HPC. When traini…
cs.LG2023★ 24 cited
Taming Resource Heterogeneity In Distributed ML Training With Dynamic Batching
Sahil Tyagi, Prateek Sharma
Current techniques and systems for distributed model training mostly assume that clusters are comprised of homogeneous servers with a constant resource availability. However, clust…
cs.LG2023★ 6 cited
GraVAC: Adaptive Compression for Communication-Efficient Distributed DL Training
Sahil Tyagi, Martin Swany
Distributed data-parallel (DDP) training improves overall application throughput as multiple devices train on a subset of data and aggregate updates to produce a globally shared mo…