6 citations · 8 across the 12 of their papers we have counts for
4 papers · 1 filter
Fast MoE Inference via Predictive Prefetching and Expert Replication
Ankit Jyothish, Ali Jannesari, Aishwarya Sarkar +1
The Mixture of Experts (MoE) architecture has become a fundamental building block in state-of-the-art large language models (LLMs), improving domain-specific expertise in LLMs and…
NOMAD: Generating Embeddings for Massive Distributed Graphs
Aishwarya Sarkar, Sayan Ghosh, Nathan R. Tallent +1
Successful machine learning on graphs or networks requires embeddings that not only represent nodes and edges as low-dimensional vectors but also preserve the graph structure. Esta…
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…
Rudder: Steering Prefetching in Distributed GNN Training using LLM Agents
Aishwarya Sarkar, Sayan Ghosh, Nathan Tallent +3
Large-scale Graph Neural Networks (GNNs) are typically trained by sampling a vertex's neighbors to a fixed distance. Because large input graphs are distributed, training requires f…