9 citations · 23 across the 4 of their papers we have counts for
4 papers
Flexible Communication for Optimal Distributed Learning over Unpredictable Networks
Sahil Tyagi, Martin Swany
Gradient compression alleviates expensive communication in distributed deep learning by sending fewer values and its corresponding indices, typically via Allgather (AG). Training w…
Accelerating Distributed ML Training via Selective Synchronization
Sahil Tyagi, Martin Swany
In distributed training, deep neural networks (DNNs) are launched over multiple workers concurrently and aggregate their local updates on each step in bulk-synchronous parallel (BS…
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…
ScaDLES: Scalable Deep Learning over Streaming data at the Edge
Sahil Tyagi, Martin Swany
Distributed deep learning (DDL) training systems are designed for cloud and data-center environments that assumes homogeneous compute resources, high network bandwidth, sufficient…