305 citations · 320 across the 24 of their papers we have counts for
6 papers · 1 filter
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…
Final Report for CHESS: Cloud, High-Performance Computing, and Edge for Science and Security
Nathan Tallent, Jan Strube, Luanzheng Guo +9
Automating the theory-experiment cycle requires effective distributed workflows that utilize a computing continuum spanning lab instruments, edge sensors, computing resources at mu…
Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows
Rafael Ferreira da Silva, Deborah Bard, Kyle Chard +108
The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensit…
Overcoming Memory Constraints in Quantum Circuit Simulation with a High-Fidelity Compression Framework
Boyuan Zhang, Bo Fang, Fanjiang Ye +4
Full-state quantum circuit simulation requires exponentially increased memory size to store the state vector as the number of qubits scales, presenting significant limitations in c…
Order-Preserving Dimension Reduction for Multimodal Semantic Embedding
Chengyu Gong, Gefei Shen, Luanzheng Guo +2
Searching for the -nearest neighbors (KNN) in multimodal data retrieval is computationally expensive, particularly due to the inherent difficulty in comparing similarity measure…
SAM-I-Am: Semantic Boosting for Zero-shot Atomic-Scale Electron Micrograph Segmentation
Waqwoya Abebe, Jan Strube, Luanzheng Guo +5
Image segmentation is a critical enabler for tasks ranging from medical diagnostics to autonomous driving. However, the correct segmentation semantics - where are boundaries locate…