3 papers
cs.LG2026
Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training
Cunyang Wei, Siddharth Singh, Aishwarya Sarkar +7
Graph neural networks (GNNs) are widely used for learning on graph datasets derived from various real-world scenarios. Learning from extremely large graphs requires distributed tra…
cs.LG2025
Plexus: Taming Billion-edge Graphs with 3D Parallel Full-graph GNN Training
Aditya K. Ranjan, Siddharth Singh, Cunyang Wei +1
Graph neural networks (GNNs) leverage the connectivity and structure of real-world graphs to learn intricate properties and relationships between nodes. Many real-world graphs exce…
cs.LG2025
Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers
Siddharth Singh, Prajwal Singhania, Aditya Ranjan +9
Training and fine-tuning large language models (LLMs) with hundreds of billions to trillions of parameters requires tens of thousands of GPUs, and a highly scalable software stack.…