6 citations · 8 across the 12 of their papers we have counts for
12 papers
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…
ProfilingAgent: Profiling-Guided Agentic Reasoning for Adaptive Model Optimization
Sadegh Jafari, Aishwarya Sarkar, Mohiuddin Bilwal +1
Foundation models face growing compute and memory bottlenecks, hindering deployment on resource-limited platforms. While compression techniques such as pruning and quantization are…
HydroGAT: Distributed Heterogeneous Graph Attention Transformer for Spatiotemporal Flood Prediction
Aishwarya Sarkar, Autrin Hakimi, Xiaoqiong Chen +4
Accurate flood forecasting remains a challenge for water-resource management, as it demands modeling of local, time-varying runoff drivers (e.g., rainfall-induced peaks, baseflow t…
Enhanced Soups for Graph Neural Networks
Joseph Zuber, Aishwarya Sarkar, Joseph Jennings +1
Graph Neural Networks (GNN) have demonstrated state-of-the-art performance in numerous scientific and high-performance computing (HPC) applications. Recent work suggests that "soup…
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…
Transfer Learning Approaches for Knowledge Discovery in Grid-based Geo-Spatiotemporal Data
Aishwarya Sarkar, Jien Zhang, Chaoqun Lu +1
Extracting and meticulously analyzing geo-spatiotemporal features is crucial to recognize intricate underlying causes of natural events, such as floods. Limited evidence about hidd…
HydroDeep -- A Knowledge Guided Deep Neural Network for Geo-Spatiotemporal Data Analysis
Aishwarya Sarkar, Jien Zhang, Chaoqun Lu +1
Due to limited evidence and complex causes of regional climate change, the confidence in predicting fluvial floods remains low. Understanding the fundamental mechanisms intrinsic t…