24 citations · 61 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…