6 citations · 7 across the 3 of their papers we have counts for
3 papers
MassiveGNN: Efficient Training via Prefetching for Massively Connected Distributed Graphs
Aishwarya Sarkar, Sayan Ghosh, Nathan R. Tallent +1
Graph Neural Networks (GNN) are indispensable in learning from graph-structured data, yet their rising computational costs, especially on massively connected graphs, pose significa…
The Landscape and Challenges of HPC Research and LLMs
Le Chen, Nesreen K. Ahmed, Akash Dutta +14
Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning. Both encoder-decoder models and prompt-based techniques ha…
Accelerating Domain-aware Deep Learning Models with Distributed Training
Aishwarya Sarkar, Chaoqun Lu, Ali Jannesari
Recent advances in data-generating techniques led to an explosive growth of geo-spatiotemporal data. In domains such as hydrology, ecology, and transportation, interpreting the com…