| A Large-Scale Study in Predictability of Daily Activities and Places G Moon, J Hamm Proceedings of the 8th EAI International Conference on Mobile Computing …, 2016 | 23 | 2016 |
| Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication GE Moon, H Kwon, G Jeong, P Chatarasi, S Rajamanickam, T Krishna IEEE Transactions on Parallel and Distributed Systems 33 (4), 1002-1014, 2021 | 17 | 2021 |
| Extending Sparse Tensor Accelerators to Support Multiple Compression Formats E Qin, G Jeong, W Won, SC Kao, H Kwon, S Srinivasan, D Das, GE Moon, ... Proceedings of the 35th IEEE International Parallel & Distributed Processing …, 2021 | 15 | 2021 |
| ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization GE Moon, JA Ellis, A Sukumaran-Rajam, S Parthasarathy, P Sadayappan Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 15* | 2020 |
| Parallel data-local training for optimizing word2vec embeddings for word and graph embeddings GE Moon, D Newman-Griffis, J Kim, A Sukumaran-Rajam, ... 2019 IEEE/ACM Workshop on Machine Learning in High Performance Computing …, 2019 | 5 | 2019 |
| Parallel Latent Dirichlet Allocation on GPUs GE Moon, I Nisa, A Sukumaran-Rajam, B Bandyopadhyay, ... International Conference on Computational Science, 259-272, 2018 | 5* | 2018 |
| Parallel Training of GRU Networks with a Multi-Grid Solver for Long Sequences GE Moon, EC Cyr International Conference on Learning Representations, 2022 | 4 | 2022 |
| SPION: Layer-Wise Sparse Training of Transformer via Convolutional Flood Filling B Yoon, Y Han, GE Moon arXiv preprint arXiv:2309.12578, 2023 | | 2023 |
| Chronica: A Data-Imbalance-Aware Scheduler for Distributed Deep Learning S Maeng, GE Moon, S Park Proceedings of the 23rd IEEE/ACM International Symposium on Cluster, Cloud …, 2023 | | 2023 |
| Adapting Multigrid-in-Time to Train Deep Neural Networks [Slides] EC Cyr, S Guenther, L Ruthotto, JB Schroder, NR Gauger, G Moon, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | | 2022 |
| Parallel-in-Time Training of Recurrent Neural Networks EC Cyr, G Moon 2021 Fall Western Sectional Meeting, 2021 | | 2021 |
| Mixed-Precision Schemes for Linear Algebra Kernels on GPUs G Moon, S Rajamanickam Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021 | | 2021 |
| MINT: Microarchitecture for Efficient and Interchangeable CompressioN Formats on Tensor Algebra. E Qin, G Jeong, W Won, SC Kao, H Kwon, S Srinivasan, D Das, GE Moon, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2020 | | 2020 |
| Utilizing Spatial Accelerators for Machine Learning and Linear Algebra Kernels GE Moon, S Rajamanickam, T Krishna, H Kwon, P Chatarasi, E Qin Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2020 | | 2020 |
| Parallel Algorithms for Machine Learning GE Moon The Ohio State University, 2019 | | 2019 |
| Parallel LDA with Over-Decomposition GE Moon, A Sukumaran-Rajam, P Sadayappan 2017 IEEE 24th International Conference on High Performance Computing …, 2017 | | 2017 |
P SadayappanProfessor, Kahlert School of Computing, University of UtahVerified email at cs.utah.edu
