I graduated with my Ph.D. under the supervision of Dr. Keshav Pingali in August 2021. During my Ph.D. years, I mainly work in distributed graph analytics and associated algorithms; near the end of my Ph.D. study I focused mainly on graph neural networks.
I received both my Bachelor's of Science and my Master's of Science in Computer Science in Spring 2017 at The University of Texas at Austin under the Integrated BS/MS program in the Department of Computer Science.
I work at KatanaGraph, a startup that uses research I was invovled in my years as as a Ph.D. student, where I am invovled in development of the graph engine and GraphAI. In particular, I was responsible for much of the infrastructure in the KatanaGraph distributed subgraph sampler for GNN training. I have worked as an intern at Intel Corporation where I developed graph applications using CUDA and Intel's DPC++ and analyzed their performance. I have also interned at KatanaGraph, a startup that uses some of the research that I have been involved in over the years: there I co-designed and developed parts of the initial KatanaGraph graph querying engine. Finally, during Spring 2021, I worked with AbbVie in developing an efficient implementation for graph transformer networks that can be used with heterogeneous knowledge graphs.
Links from the title of a paper typically go to the "official" conference/proceedings PDF provider (if it exists) unless otherwise mentioned, while the PDF link goes to a local copy of the paper. If the official provider doesn't give public access, the paper will have a local copy you can download for non-commercial purposes.
Paper errata/important notes can be found by clicking here. There will be an "Errata" link on applicable papers below.
Efficient Distribution for Deep Learning on Large Graphs
Loc Hoang, Xuhao Chen, Hochan Lee, Roshan Dathathri, Gurbinder Gill, Keshav Pingali
Proceedings of the First MLSys Workshop on Graph Neural Networks and Systems, 2021
The PDF linked above contains updates to the Appendix not present in the
version on the GNNSys webpage. See Errata for details.
[Poster]
[Errata]
Single Machine Graph Analytics on Massive Datasets Using Intel Optane DC Persistent Memory
Gurbinder Gill, Roshan Dathathri, Loc Hoang, Ramesh Peri, Keshav Pingali
VLDB 2020 46th International Conference on Very Large Data Bases, August 2020
A Study of Graph Analytics for Massive Datasets on Large-Scale Distributed GPUs
Vishwesh Jatala, Roshan Dathathri, Gurbinder Gill, Loc Hoang, V. Krishna Nandivada, Keshav Pingali
IPDPS 2020 34th International Parallel and Distributed Processing Symposium, May 2020
Gluon-Async: A Bulk-Asynchronous System for Distributed and Heterogeneous Graph Analytics
Roshan Dathathri, Gurbinder Gill, Loc Hoang, Hoang-Vu Dang, Vishwesh Jatala, V. Krishna Nandivada, Marc Snir, Keshav Pingali
PACT 2019 28th International Conference on Parallel Architectures and Compilation Techniques, September 2019
Best Paper Nominee
[PDF]
DistTC: High Performance Distributed Triangle Counting
Loc Hoang*, Vishwesh Jatala*, Xuhao Chen, Udit Agarwal, Roshan Dathathri, Gurbinder Gill, Keshav Pingali
*Authors contributed equally
HPEC 2019 23rd IEEE High Performance Extreme Computing, Graph Challenge, September 2019
Please see the important note linked below:
[PDF][Errata/Important Note]
A Study of Partitioning Policies for Graph Analytics on Large-scale Distributed Platforms
Gurbinder Gill, Roshan Dathathri, Loc Hoang, Keshav Pingali
VLDB 2019 45th International Conference on Very Large Data Bases, August 2019
CuSP: A Customizable Streaming Edge Partitioner for Distributed Graph Analytics
Loc Hoang, Roshan Dathathri, Gurbinder Gill, Keshav Pingali
IPDPS 2019 33rd IEEE International Parallel and Distributed Processing Symposium, May 2019
[PDF] [Slides]
Phoenix: A Substrate for Resilient Distributed Graph Analytics
A round-efficient distributed betweenness centrality algorithm
Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics
Email: shorter of first and last name at cs.utexas.edu
I don't have a public presence on social media (this includes LinkedIn).
Hobbies are mostly video games and reading. I used to watch a lot of anime (not much at all these days).
Last update: May 2023