22 citations · 49 across the 12 of their papers we have counts for
14 papers
GPU-Accelerated Multilevel Graph Clustering: A Parallel Perspective on Louvain and Leiden
Michael S. Gilbert, Kamesh Madduri
The sequential Louvain and Leiden algorithms are widely used techniques for modularity-optimizing clustering (or community detection) in large graphs. We present pLouvain and pLeid…
Scalable and Adaptive Parallel Training of Graph Transformer on Large Graphs
Jun-Liang Lin, Kamesh Madduri, Mahmut Taylan Kandemir
Graph foundation models have demonstrated remarkable adaptability across diverse downstream tasks through large-scale pretraining on graphs. However, existing implementations of th…
BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Recommendations
Ashirbad Mishra, Jinyu Zhao, Soumik Dey +3
In the domain of sponsored search advertising, the focus of Keyphrase recommendation has largely been on exact match types, which pose issues such as high management expenses, limi…
GraphEx: A Graph-based Extraction Method for Advertiser Keyphrase Recommendation
Ashirbad Mishra, Soumik Dey, Marshall Wu +5
Online sellers and advertisers are recommended keyphrases for their listed products, which they bid on to enhance their sales. One popular paradigm that generates such recommendati…
Graphite: A Graph-based Extreme Multi-Label Short Text Classifier for Keyphrase Recommendation
Ashirbad Mishra, Soumik Dey, Jinyu Zhao +3
Keyphrase Recommendation has been a pivotal problem in advertising and e-commerce where advertisers/sellers are recommended keyphrases (search queries) to bid on to increase their…
Jet: Multilevel Graph Partitioning on Graphics Processing Units
Michael S. Gilbert, Kamesh Madduri, Erik G. Boman +1
The multilevel heuristic is the dominant strategy for high-quality sequential and parallel graph partitioning. Partition refinement is a key step of multilevel graph partitioning.…
Convex Latent Effect Logit Model via Sparse and Low-rank Decomposition
Hongyuan Zhan, Kamesh Madduri, Venkataraman Shankar
In this paper, we propose a convex formulation for learning logistic regression model (logit) with latent heterogeneous effect on sub-population. In transportation, logistic regres…
Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction
Hongyuan Zhan, Gabriel Gomes, Xiaoye S. Li +2
Computational efficiency is an important consideration for deploying machine learning models for time series prediction in an online setting. Machine learning algorithms adjust mod…
Shared-memory Graph Truss Decomposition
Humayun Kabir, Kamesh Madduri
We present PKT, a new shared-memory parallel algorithm and OpenMP implementation for the truss decomposition of large sparse graphs. A k-truss is a dense subgraph definition that c…
Distributed-Memory Breadth-First Search on Massive Graphs
Aydin Buluc, Scott Beamer, Kamesh Madduri +2
This chapter studies the problem of traversing large graphs using the breadth-first search order on distributed-memory supercomputers. We consider both the traditional level-synchr…
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…
Modern Gyrokinetic Particle-In-Cell Simulation of Fusion Plasmas on Top Supercomputers
Bei Wang, Stephane Ethier, William Tang +4
The Gyrokinetic Toroidal Code at Princeton (GTC-P) is a highly scalable and portable particle-in-cell (PIC) code. It solves the 5D Vlasov-Poisson equation featuring efficient utili…
Parallel Breadth-First Search on Distributed Memory Systems
Aydin Buluc, Kamesh Madduri
Data-intensive, graph-based computations are pervasive in several scientific applications, and are known to to be quite challenging to implement on distributed memory systems. In t…