3 citations · 10 across the 8 of their papers we have counts for
8 papers
Pushing the Performance Envelope of DNN-based Recommendation Systems Inference on GPUs
Rishabh Jain, Vivek M. Bhasi, Adwait Jog +3
Personalized recommendation is a ubiquitous application on the internet, with many industries and hyperscalers extensively leveraging Deep Learning Recommendation Models (DLRMs) fo…
GPU Cluster Scheduling for Network-Sensitive Deep Learning
Aakash Sharma, Vivek M. Bhasi, Sonali Singh +3
We propose a novel GPU-cluster scheduler for distributed DL (DDL) workloads that enables proximity based consolidation of GPU resources based on the DDL jobs' sensitivities to the…
Analysis of Distributed Deep Learning in the Cloud
Aakash Sharma, Vivek M. Bhasi, Sonali Singh +6
We aim to resolve this problem by introducing a comprehensive distributed deep learning (DDL) profiler, which can determine the various execution "stalls" that DDL suffers from whi…
Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks
Morteza Ramezani, Weilin Cong, Mehrdad Mahdavi +2
Despite the recent success of Graph Neural Networks (GNNs), training GNNs on large graphs remains challenging. The limited resource capacities of the existing servers, the dependen…
Exploiting Activation based Gradient Output Sparsity to Accelerate Backpropagation in CNNs
Anup Sarma, Sonali Singh, Huaipan Jiang +5
Machine/deep-learning (ML/DL) based techniques are emerging as a driving force behind many cutting-edge technologies, achieving high accuracy on computer vision workloads such as i…
Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training
Anup Sarma, Sonali Singh, Huaipan Jiang +3
Recurrent Neural Networks (RNNs), more specifically their Long Short-Term Memory (LSTM) variants, have been widely used as a deep learning tool for tackling sequence-based learning…
Cocktail: Leveraging Ensemble Learning for Optimized Model Serving in Public Cloud
Jashwant Raj Gunasekaran, Cyan Subhra Mishra, Prashanth Thinakaran +2
With a growing demand for adopting ML models for a varietyof application services, it is vital that the frameworks servingthese models are capable of delivering highly accurate pre…
Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks
Weilin Cong, Rana Forsati, Mahmut Kandemir +1
Sampling methods (e.g., node-wise, layer-wise, or subgraph) has become an indispensable strategy to speed up training large-scale Graph Neural Networks (GNNs). However, existing sa…