| Deep learning recommendation model for personalization and recommendation systems M Naumov, D Mudigere, HJM Shi, J Huang, N Sundaraman, J Park, ... arXiv preprint arXiv:1906.00091, 2019 | 752 | 2019 |
| A study of BFLOAT16 for deep learning training D Kalamkar, D Mudigere, N Mellempudi, D Das, K Banerjee, S Avancha, ... arXiv preprint arXiv:1905.12322, 2019 | 339 | 2019 |
| Glow: Graph lowering compiler techniques for neural networks N Rotem, J Fix, S Abdulrasool, G Catron, S Deng, R Dzhabarov, N Gibson, ... arXiv preprint arXiv:1805.00907, 2018 | 326 | 2018 |
| Faster cnns with direct sparse convolutions and guided pruning J Park, S Li, W Wen, PTP Tang, H Li, Y Chen, P Dubey arXiv preprint arXiv:1608.01409, 2016 | 304* | 2016 |
| Efficient embedded computing WJ Dally, J Balfour, D Black-Shaffer, J Chen, RC Harting, V Parikh, J Park, ... Computer 41 (7), 27-32, 2008 | 258 | 2008 |
| Navigating the maze of graph analytics frameworks using massive graph datasets N Satish, N Sundaram, MMA Patwary, J Seo, J Park, MA Hassaan, ... Proceedings of the 2014 ACM SIGMOD international conference on Management of …, 2014 | 246 | 2014 |
| Deep learning inference in facebook data centers: Characterization, performance optimizations and hardware implications J Park, M Naumov, P Basu, S Deng, A Kalaiah, D Khudia, J Law, P Malani, ... arXiv preprint arXiv:1811.09886, 2018 | 220 | 2018 |
| FROSTT: The formidable repository of open sparse tensors and tools S Smith, JW Choi, J Li, R Vuduc, J Park, X Liu, G Karypis | 163 | 2017 |
| Distributed socialite: A datalog-based language for large-scale graph analysis J Seo, J Park, J Shin, MS Lam Proceedings of the VLDB Endowment 6 (14), 1906-1917, 2013 | 155 | 2013 |
| An energy-efficient processor architecture for embedded systems J Balfour, W Dally, D Black-Schaffer, V Parikh, JS Park IEEE Computer Architecture Letters 7 (1), 29-32, 2008 | 107 | 2008 |
| Software-hardware co-design for fast and scalable training of deep learning recommendation models D Mudigere, Y Hao, J Huang, Z Jia, A Tulloch, S Sridharan, X Liu, ... Proceedings of the 49th Annual International Symposium on Computer …, 2022 | 100 | 2022 |
| Two-step approach to scheduling quantum circuits GG Guerreschi, J Park Quantum Science and Technology 3 (4), 045003, 2018 | 99* | 2018 |
| Sparsifying synchronization for high-performance shared-memory sparse triangular solver J Park, M Smelyanskiy, N Sundaram, P Dubey Supercomputing: 29th International Conference, ISC 2014, Leipzig, Germany …, 2014 | 94 | 2014 |
| Unity: Accelerating {DNN} training through joint optimization of algebraic transformations and parallelization C Unger, Z Jia, W Wu, S Lin, M Baines, CEQ Narvaez, V Ramakrishnaiah, ... 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2022 | 85 | 2022 |
| Parallel efficient sparse matrix-matrix multiplication on multicore platforms MMA Patwary, NR Satish, N Sundaram, J Park, MJ Anderson, ... International Conference on High Performance Computing, 48-57, 2015 | 84 | 2015 |
| Enabling sparse winograd convolution by native pruning S Li, J Park, PTP Tang arXiv preprint arXiv:1702.08597, 2017 | 69 | 2017 |
| Automating wavefront parallelization for sparse matrix computations A Venkat, MS Mohammadi, J Park, H Rong, R Barik, MM Strout, M Hall SC'16: Proceedings of the International Conference for High Performance …, 2016 | 68 | 2016 |
| Efficient shared-memory implementation of high-performance conjugate gradient benchmark and its application to unstructured matrices J Park, M Smelyanskiy, K Vaidyanathan, A Heinecke, DD Kalamkar, X Liu, ... SC'14: Proceedings of the International Conference for High Performance …, 2014 | 68 | 2014 |
| Sparsity-aware hardware accelerators AU Diril, JS Park, N Rotem, M Smelyanskiy US Patent 10,482,156, 2019 | 63 | 2019 |
| Hardware/software co-optimization to improve performance and energy for inter-VM communication for NFVs and other producer-consumer workloads R Wang, AJ Herdrich, YC Liu, HH Hum, JS Park, CJ Hughes, ... US Patent 10,817,425, 2020 | 61 | 2020 |
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