34 citations · 112 across the 16 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
On Optimizing Distributed Tucker Decomposition for Sparse Tensors
Venkatesan T. Chakaravarthy, Jee W. Choi, Douglas J. Joseph +4
The Tucker decomposition generalizes the notion of Singular Value Decomposition (SVD) to tensors, the higher dimensional analogues of matrices. We study the problem of constructing…
Efficient Training of Convolutional Neural Nets on Large Distributed Systems
Sameer Kumar, Dheeraj Sreedhar, Vaibhav Saxena +2
Deep Neural Networks (DNNs) have achieved im- pressive accuracy in many application domains including im- age classification. Training of DNNs is an extremely compute- intensive pr…
Efficient Inferencing of Compressed Deep Neural Networks
Dharma Teja Vooturi, Saurabh Goyal, Anamitra R. Choudhury +2
Large number of weights in deep neural networks makes the models difficult to be deployed in low memory environments such as, mobile phones, IOT edge devices as well as "inferencin…