| A state-of-the-art survey on deep learning theory and architectures MZ Alom, TM Taha, C Yakopcic, S Westberg, P Sidike, MS Nasrin, ... Electronics 8 (3), 292, 2019 | 1571 | 2019 |
| Communication quantization for data-parallel training of deep neural networks N Dryden, T Moon, SA Jacobs, B Van Essen 2016 2nd Workshop on Machine Learning in HPC Environments (MLHPC), 1-8, 2016 | 211 | 2016 |
| Accelerating a random forest classifier: Multi-core, GP-GPU, or FPGA? B Van Essen, C Macaraeg, M Gokhale, R Prenger 2012 IEEE 20th International Symposium on Field-Programmable Custom …, 2012 | 208 | 2012 |
| SPR: an architecture-adaptive CGRA mapping tool S Friedman, A Carroll, B Van Essen, B Ylvisaker, C Ebeling, S Hauck Proceedings of the ACM/SIGDA international symposium on Field programmable …, 2009 | 149 | 2009 |
| Truenorth ecosystem for brain-inspired computing: scalable systems, software, and applications J Sawada, F Akopyan, AS Cassidy, B Taba, MV Debole, P Datta, ... SC'16: Proceedings of the International Conference for High Performance …, 2016 | 97 | 2016 |
| LBANN: Livermore big artificial neural network HPC toolkit B Van Essen, H Kim, R Pearce, K Boakye, B Chen Proceedings of the workshop on machine learning in high-performance …, 2015 | 94 | 2015 |
| CANDLE/Supervisor: A workflow framework for machine learning applied to cancer research JM Wozniak, R Jain, P Balaprakash, J Ozik, NT Collier, J Bauer, F Xia, ... BMC bioinformatics 19 (18), 59-69, 2018 | 87 | 2018 |
| Extreme heterogeneity 2018-productive computational science in the era of extreme heterogeneity: Report for DOE ASCR workshop on extreme heterogeneity JS Vetter, R Brightwell, M Gokhale, P McCormick, R Ross, J Shalf, ... | 72 | 2022 |
| Deep learning: A guide for practitioners in the physical sciences BK Spears, J Brase, PT Bremer, B Chen, J Field, J Gaffney, M Kruse, ... Physics of Plasmas 25 (8), 080901, 2018 | 72 | 2018 |
| DI-MMAP—a scalable memory-map runtime for out-of-core data-intensive applications B Van Essen, H Hsieh, S Ames, R Pearce, M Gokhale Cluster Computing 18 (1), 15-28, 2015 | 67 | 2015 |
| On the role of NVRAM in data-intensive architectures: an evaluation B Van Essen, R Pearce, S Ames, M Gokhale 2012 IEEE 26th International Parallel and Distributed Processing Symposium …, 2012 | 62 | 2012 |
| Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins HI Ingólfsson, C Neale, TS Carpenter, R Shrestha, CA López, TH Tran, ... Proceedings of the National Academy of Sciences 119 (1), 2022 | 59 | 2022 |
| Improving strong-scaling of CNN training by exploiting finer-grained parallelism N Dryden, N Maruyama, T Benson, T Moon, M Snir, B Van Essen 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2019 | 55 | 2019 |
| Channel and filter parallelism for large-scale CNN training N Dryden, N Maruyama, T Moon, T Benson, M Snir, B Van Essen Proceedings of the International Conference for High Performance Computing …, 2019 | 49 | 2019 |
| Aluminum: An asynchronous, GPU-aware communication library optimized for large-scale training of deep neural networks on HPC systems N Dryden, N Maruyama, T Moon, T Benson, A Yoo, M Snir, B Van Essen Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States), 2018 | 45 | 2018 |
| The case for strong scaling in deep learning: Training large 3d cnns with hybrid parallelism Y Oyama, N Maruyama, N Dryden, E McCarthy, P Harrington, J Balewski, ... IEEE Transactions on Parallel and Distributed Systems 32 (7), 1641-1652, 2020 | 42 | 2020 |
| Effective quantization approaches for recurrent neural networks MZ Alom, AT Moody, N Maruyama, BC Van Essen, TM Taha 2018 International Joint Conference on Neural Networks (IJCNN), 1-8, 2018 | 40 | 2018 |
| Large-scale deep learning on the YFCC100M dataset K Ni, R Pearce, K Boakye, B Van Essen, D Borth, B Chen, E Wang arXiv preprint arXiv:1502.03409, 2015 | 38 | 2015 |
| Method of securing programmable logic configuration data BC Van Essen, JW Kidd, CM Petersen, HH Schmit US Patent 7,197,647, 2007 | 37 | 2007 |
| DI-MMAP: A high performance memory-map runtime for data-intensive applications B Van Essen, H Hsieh, S Ames, M Gokhale 2012 SC Companion: High Performance Computing, Networking Storage and …, 2012 | 36 | 2012 |
