1 citations · 1 across the 9 of their papers we have counts for
10 papers
LoDAdaC: a unified local training-based decentralized framework with adaptive gradients and compressed communication
Wei Liu, Anweshit Panda, Ujwal Pandey +5
In the decentralized distributed learning, achieving fast convergence and low communication cost is essential for scalability and high efficiency. Adaptive gradient methods, such a…
Anonymized Network Sensing using C++26 std::execution on GPUs
Michael Mandulak, Sayan Ghosh, S M Ferdous +2
Large-scale network sensing plays a vital role in network traffic analysis and characterization. As network packet data grows increasingly large, parallel methods have become mains…
Compressed Decentralized Momentum Stochastic Gradient Methods for Nonconvex Optimization
Wei Liu, Anweshit Panda, Ujwal Pandey +6
In this paper, we design two compressed decentralized algorithms for solving nonconvex stochastic optimization under two different scenarios. Both algorithms adopt a momentum techn…
ApproxJoin: Approximate Matching for Efficient Verification in Fuzzy Set Similarity Join
Michael Mandulak, S M Ferdous, Sayan Ghosh +2
The set similarity join problem is a fundamental problem in data processing and discovery, relying on exact similarity measures between sets. In the presence of alterations, such a…
On the Robustness of Graph Reduction Against GNN Backdoor
Yuxuan Zhu, Michael Mandulak, Kerui Wu +4
Graph Neural Networks (GNNs) are gaining popularity across various domains due to their effectiveness in learning graph-structured data. Nevertheless, they have been shown to be su…
Parallel coarsening of graph data with spectral guarantees
Christopher Brissette, Andy Huang, George Slota
Finding coarse representations of large graphs is an important computational problem in the fields of scientific computing, large scale graph partitioning, and the reduction of geo…
Parallel Graph Coloring Algorithms for Distributed GPU Environments
Ian Bogle, Erik G Boman, Karen D Devine +2
Graph coloring is often used in parallelizing scientific computations that run in distributed and multi-GPU environments; it identifies sets of independent data that can be updated…
A simple method for improving the accuracy of Chung-Lu random graph generation
Christopher Brissette, George Slota
Random graph models play a central role in network analysis. The Chung-Lu model, which connects nodes based on their expected degrees is of particular interest. It is widely used t…
Distributed Graph Layout for Scalable Small-world Network Analysis
George M Slota, Sivasankaran Rajamanickam, Kamesh Madduri
The in-memory graph layout or organization has a considerable impact on the time and energy efficiency of distributed memory graph computations. It affects memory locality, inter-t…
Partitioning Trillion-edge Graphs in Minutes
George M Slota, Sivasankaran Rajamanickam, Karen Devine +1
We introduce XtraPuLP, a new distributed-memory graph partitioner designed to process trillion-edge graphs. XtraPuLP is based on the scalable label propagation community detection…