Gordon E. Moon
Title
Cited by
Cited by
Year
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
182016
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
9*2020
Parallel Latent Dirichlet Allocation on GPUs
GE Moon, I Nisa, A Sukumaran-Rajam, B Bandyopadhyay, ...
International Conference on Computational Science, 259-272, 2018
2*2018
Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication
GE Moon, H Kwon, G Jeong, P Chatarasi, S Rajamanickam, T Krishna
arXiv preprint arXiv:2106.10499, 2021
12021
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
12019
Parallel-in-time training of recurrent neural networks
EC Cyr, G Moon
2021 Fall Western Sectional Meeting, 2021
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
2021
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
PQDT-UK & Ireland, 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
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Articles 1–10