24 citations · 71 across the 6 of their papers we have counts for
6 papers
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…
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…
Molecular Dynamics Simulations on Cloud Computing and Machine Learning Platforms
Prateek Sharma, Vikram Jadhao
Scientific computing applications have benefited greatly from high performance computing infrastructure such as supercomputers. However, we are seeing a paradigm shift in the compu…
Cloud-scale VM Deflation for Running Interactive Applications On Transient Servers
Alexander Fuerst, Ahmed Ali-Eldin, Prashant Shenoy +1
Transient computing has become popular in public cloud environments for running delay-insensitive batch and data processing applications at low cost. Since transient cloud servers…
Modeling The Temporally Constrained Preemptions of Transient Cloud VMs
JCS Kadupitiya, Vikram Jadhao, Prateek Sharma
Transient cloud servers such as Amazon Spot instances, Google Preemptible VMs, and Azure Low-priority batch VMs, can reduce cloud computing costs by as much as , but can…
Portfolio-driven Resource Management for Transient Cloud Servers
Prateek Sharma, David Irwin, Prashant Shenoy
Cloud providers have begun to offer their surplus capacity in the form of low-cost transient servers, which can be revoked unilaterally at any time. While the low cost of transient…