34 citations · 112 across the 16 of their papers we have counts for
20 papers
Rightsizing Clusters for Time-Limited Tasks
Venkatesan T. Chakaravarthy, Padmanabha V. Seshadri, Pooja Aggarwal +4
In conventional public clouds, designing a suitable initial cluster for a given application workload is important in reducing the computational foot-print during run-time. In edge…
GREED: A Neural Framework for Learning Graph Distance Functions
Rishabh Ranjan, Siddharth Grover, Sourav Medya +3
Among various distance functions for graphs, graph and subgraph edit distances (GED and SED respectively) are two of the most popular and expressive measures. Unfortunately, exact…
Efficient Scaling of Dynamic Graph Neural Networks
Venkatesan T. Chakaravarthy, Shivmaran S. Pandian, Saurabh Raje +3
We present distributed algorithms for training dynamic Graph Neural Networks (GNN) on large scale graphs spanning multi-node, multi-GPU systems. To the best of our knowledge, this…
Generalizations of Length Limited Huffman Coding for Hierarchical Memory Settings
Shashwat Banchhor, Rishikesh Gajjala, Yogish Sabharwal +1
In this paper, we study the problem of designing prefix-free encoding schemes having minimum average code length that can be decoded efficiently under a decode cost model that capt…
Effective Elastic Scaling of Deep Learning Workloads
Vaibhav Saxena, K. R. Jayaram, Saurav Basu +2
The increased use of deep learning (DL) in academia, government and industry has, in turn, led to the popularity of on-premise and cloud-hosted deep learning platforms, whose goals…
PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination
Saurabh Goyal, Anamitra R. Choudhury, Saurabh M. Raje +3
We develop a novel method, called PoWER-BERT, for improving the inference time of the popular BERT model, while maintaining the accuracy. It works by: a) exploiting redundancy pert…