66 citations · 109 across the 10 of their papers we have counts for
6 papers · 1 filter
DistGNN-MB: Distributed Large-Scale Graph Neural Network Training on x86 via Minibatch Sampling
Md Vasimuddin, Ramanarayan Mohanty, Sanchit Misra +1
Training Graph Neural Networks, on graphs containing billions of vertices and edges, at scale using minibatch sampling poses a key challenge: strong-scaling graphs and training exa…
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks
Vasimuddin Md, Sanchit Misra, Guixiang Ma +6
Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is a critical problem that needs to scale to hundreds of compute nodes to be feasible. It…
High-Performance Deep Learning via a Single Building Block
Evangelos Georganas, Kunal Banerjee, Dhiraj Kalamkar +6
Deep learning (DL) is one of the most prominent branches of machine learning. Due to the immense computational cost of DL workloads, industry and academia have developed DL librari…
A Study of BFLOAT16 for Deep Learning Training
Dhiraj Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi +16
This paper presents the first comprehensive empirical study demonstrating the efficacy of the Brain Floating Point (BFLOAT16) half-precision format for Deep Learning training acros…
Hierarchical Block Sparse Neural Networks
Dharma Teja Vooturi, Dheevatsa Mudigere, Sasikanth Avancha
Sparse deep neural networks(DNNs) are efficient in both memory and compute when compared to dense DNNs. But due to irregularity in computation of sparse DNNs, their efficiencies ar…
RAIL: Risk-Averse Imitation Learning
Anirban Santara, Abhishek Naik, Balaraman Ravindran +4
Imitation learning algorithms learn viable policies by imitating an expert's behavior when reward signals are not available. Generative Adversarial Imitation Learning (GAIL) is a s…