Publications

International Conference Proceedings/Journals

1. EQUILIBRIA: Co-Optimizing Energy and Latency in Online ML-based Stream Processing Systems

Sejeong Oh, Soyang Baek, Gordon E. Moon, Sungyong Park

The 25th IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), Tromsø, Norway, 2025

2. Efficient GNN-based Social Recommender Systems through Social Graph Refinement [paper]

Sangmin Ga, Paul Hyunbin Cho, Gordon E. Moon, Sungwon Jung

The Journal of Supercomputing, 2025

3. Tensor Core-Adapted Sparse Matrix Multiplication for Accelerating Sparse Deep Neural Networks [paper]

Yoonsang Han, Inseo Kim, Jinsung Kim, Gordon E. Moon

Electronics, 2024

4. ML-based Dynamic Operator-Level Query Mapping for Stream Processing Systems in Heterogeneous Computing Environments [paper]

Sejeong Oh, Gordon E. Moon, Sungyong Park

IEEE International Conference on Cluster Computing (CLUSTER), Kobe, Japan, 2024

5. Exploring Attention Sparsity to Accelerate Transformer Training on GPUs [paper]

Bokyeong Yoon, Ah-hyun Lee, Jinsung Kim, Gordon E. Moon

IEEE Access, 2024

6. Accelerated Block-Sparsity-Aware Matrix Reordering for Leveraging Tensor Cores in Sparse Matrix-Multivector Multiplication [paper]

Eunji Lee*, Yoonsang Han*, Gordon E. Moon (* equal contribution)

The 30th International European Conference on Parallel and Distributed Computing (Euro-Par), Madrid, Spain, 2024

7. Layer-Wise Sparse Training of Transformer via Convolutional Flood Filling [paper]

Bokyeong Yoon, Yoonsang Han, Gordon E. Moon

The 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Taipei, Taiwan, 2024

8. Chronica: A Data-Imbalance-Aware Scheduler for Distributed Deep Learning [paper]

Sanha Maeng, Gordon E. Moon, Sungyong Park

The 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid), Bangalore, India, 2023

9. Parallel Training of GRU Networks with a Multi-Grid Solver for Long Sequences [paper]

Gordon E. Moon, Eric C. Cyr

International Conference on Learning Representations (ICLR), 2022

10. Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication [paper]

Gordon E. Moon, Hyoukjun Kwon, Geonhwa Jeong, Prasanth Chatarasi, Sivasankaran Rajamanickam, Tushar Krishna, 

IEEE Transactions on Parallel and Distributed Systems (TPDS), 2022

11. Extending Sparse Tensor Accelerators to Support Multiple Compression Formats [paper]

Eric Qin, Geonhwa Jeong, William Won, Sheng-Chun Kao, Hyoukjun Kwon, Sudarshan Srinivasan, Dipankar Das, Gordon E. Moon, Sivasankaran Rajamanickam, Tushar Krishna

IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2021

12. ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization [paper]

Gordon E. Moon, J. Austin Ellis, Aravind Sukumaran-Rajam, Srinivasan Parthasarathy, P. Sadayappan

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020

13. Parallel Data-Local Training for Optimizing Word2Vec Embeddings for Word and Graph Embeddings [paper]

Gordon E. Moon, Denis Newman-Griffis, Jinsung Kim, Aravind Sukumaran-Rajam, Eric Fosler-Lussier, P. Sadayappan

International Workshop on Machine Learning in High-Performance Computing Environments (MLHPC), Denver, CO, USA, 2019

Domestic Conference Proceedings/Journals

1. Transformer-based Recommender Systems with Social Information

Cheolhee Kim, Bokyeong Yoon, Paul Hyunbin Cho, Euhyun Moon

Korea Software Congress (KSC), 2023

2. Heterogeneity-Aware Calibration for Federated Learning with Non-IID Data

Sooyeon Kim, Haeeun Lee, Eunji Lee, Euhyun Moon

Korea Software Congress (KSC), 2023

3. Leveraging Convolution Filter for Sparsifying Attention in Transformer

Bokyeong Yoon, Euhyun Moon

Korea Computer Congress (KCC), 2023

4. A Data Imbalance Minimization Strategy for Scalable Deep Learning Training

Sanha Maeng, Euhyun Moon, Sungyong Park

Journal of KIISE, 2023