53 citations · 95 across the 10 of their papers we have counts for
11 papers
LocalNewton: Reducing Communication Bottleneck for Distributed Learning
Vipul Gupta, Avishek Ghosh, Michal Derezinski +3
To address the communication bottleneck problem in distributed optimization within a master-worker framework, we propose LocalNewton, a distributed second-order algorithm with loca…
BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear Memory
Amirali Aghazadeh, Vipul Gupta, Alex DeWeese +2
We consider feature selection for applications in machine learning where the dimensionality of the data is so large that it exceeds the working memory of the (local) computing mach…
Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism
Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang +6
In this paper, we consider hybrid parallelism -- a paradigm that employs both Data Parallelism (DP) and Model Parallelism (MP) -- to scale distributed training of large recommendat…
Utility-based Resource Allocation and Pricing for Serverless Computing
Vipul Gupta, Soham Phade, Thomas Courtade +1
Serverless computing platforms currently rely on basic pricing schemes that are static and do not reflect customer feedback. This leads to significant inefficiencies from a total u…
Serverless Straggler Mitigation using Local Error-Correcting Codes
Vipul Gupta, Dominic Carrano, Yaoqing Yang +3
Inexpensive cloud services, such as serverless computing, are often vulnerable to straggling nodes that increase end-to-end latency for distributed computation. We propose and impl…
Stochastic Weight Averaging in Parallel: Large-Batch Training that Generalizes Well
Vipul Gupta, Santiago Akle Serrano, Dennis DeCoste
We propose Stochastic Weight Averaging in Parallel (SWAP), an algorithm to accelerate DNN training. Our algorithm uses large mini-batches to compute an approximate solution quickly…