45 citations · 137 across the 22 of their papers we have counts for
23 papers
Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs
Muhammed Fatih Balın, Ümit V. Çatalyürek
Graph Neural Networks (GNNs) have received significant attention recently, but training them at a large scale remains a challenge. Mini-batch training coupled with sampling is used…
PGAbB: A Block-Based Graph Processing Framework for Heterogeneous Platforms
Abdurrahman Yasar, Sivasankaran Rajamanickam, Jonathan W. Berry +1
Designing flexible graph kernels that can run well on various platforms is a crucial research problem due to the frequent usage of graphs for modeling data and recent architectural…
A Simple and Elegant Mathematical Formulation for the Acyclic DAG Partitioning Problem
M. Yusuf Özkaya, Ümit V. Çatalyürek
This work addresses the NP-Hard problem of acyclic directed acyclic graph (DAG) partitioning problem. The acyclic partitioning problem is defined as partitioning the vertex set of…
Efficient Hierarchical State Vector Simulation of Quantum Circuits via Acyclic Graph Partitioning
Bo Fang, M. Yusuf Özkaya, Ang Li +2
Early but promising results in quantum computing have been enabled by the concurrent development of quantum algorithms, devices, and materials. Classical simulation of quantum prog…
MG-GCN: Scalable Multi-GPU GCN Training Framework
Muhammed Fatih Balın, Kaan Sancak, Ümit V. Çatalyürek
Full batch training of Graph Convolutional Network (GCN) models is not feasible on a single GPU for large graphs containing tens of millions of vertices or more. Recent work has sh…
A Block-Based Triangle Counting Algorithm on Heterogeneous Environments
Abdurrahman Yaşar, Sivasankaran Rajamanickam, Jonathan Berry +1
Triangle counting is a fundamental building block in graph algorithms. In this paper, we propose a block-based triangle counting algorithm to reduce data movement during both seque…