24 citations · 61 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
Scavenger: A Cloud Service for Optimizing Cost and Performance of ML Training
Sahil Tyagi, Prateek Sharma
While the pay-as-you-go nature of cloud virtual machines (VMs) makes it easy to spin-up large clusters for training ML models, it can also lead to ballooning costs. The 100s of vir…
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…
Collaborative Reuse of Streaming Dataflows in IoT Applications
Shilpa Chaturvedi, Sahil Tyagi, Yogesh Simmhan
Distributed Stream Processing Systems (DSPS) like Apache Storm and Spark Streaming enable composition of continuous dataflows that execute persistently over data streams. They are…