2 citations · 5 across the 5 of their papers we have counts for
5 papers
BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs
Loc Hoang, Rita Brugarolas Brufau, Ke Ding +1
We present BatchGNN, a distributed CPU system that showcases techniques that can be used to efficiently train GNNs on terabyte-sized graphs. It reduces communication overhead with…
Optimizing Graph Transformer Networks with Graph-based Techniques
Loc Hoang, Udit Agarwal, Gurbinder Gill +4
Graph transformer networks (GTN) are a variant of graph convolutional networks (GCN) that are targeted to heterogeneous graphs in which nodes and edges have associated type informa…
Sandslash: A Two-Level Framework for Efficient Graph Pattern Mining
Xuhao Chen, Roshan Dathathri, Gurbinder Gill +2
Graph pattern mining (GPM) is used in diverse application areas including social network analysis, bioinformatics, and chemical engineering. Existing GPM frameworks either provide…
An Adaptive Load Balancer For Graph Analytical Applications on GPUs
Vishwesh Jatala, Loc Hoang, Roshan Dathathri +3
Load-balancing among the threads of a GPU for graph analytics workloads is difficult because of the irregular nature of graph applications and the high variability in vertex degree…
Single Machine Graph Analytics on Massive Datasets Using Intel Optane DC Persistent Memory
Gurbinder Gill, Roshan Dathathri, Loc Hoang +2
Intel Optane DC Persistent Memory (Optane PMM) is a new kind of byte-addressable memory with higher density and lower cost than DRAM. This enables the design of affordable systems…