6 papers
Essential-Web v1.0: 24T tokens of organized web data
Essential AI, :, Andrew Hojel +22
Data plays the most prominent role in how language models acquire skills and knowledge. The lack of massive, well-organized pre-training datasets results in costly and inaccessible…
Sparsity-Aware Communication for Distributed Graph Neural Network Training
Ujjaini Mukhodopadhyay, Alok Tripathy, Oguz Selvitopi +2
Graph Neural Networks (GNNs) are a computationally efficient method to learn embeddings and classifications on graph data. However, GNN training has low computational intensity, ma…
Scaling Graph Neural Networks for Particle Track Reconstruction
Alok Tripathy, Alina Lazar, Xiangyang Ju +3
Particle track reconstruction is an important problem in high-energy physics (HEP), necessary to study properties of subatomic particles. Traditional track reconstruction algorithm…
Distributed Matrix-Based Sampling for Graph Neural Network Training
Alok Tripathy, Katherine Yelick, Aydin Buluc
Graph Neural Networks (GNNs) offer a compact and computationally efficient way to learn embeddings and classifications on graph data. GNN models are frequently large, making distri…
Scalable Hash Table for NUMA Systems
Alok Tripathy, Oded Green
Hash tables are used in a plethora of applications, including database operations, DNA sequencing, string searching, and many more. As such, there are many parallelized hash tables…
Reducing Communication in Graph Neural Network Training
Alok Tripathy, Katherine Yelick, Aydin Buluc
Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse connectivity information of the data. GNNs represent this connectivity as spars…