Jongsoo Park
Jongsoo Park
Research Scientist, Facebook
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
Efficient embedded computing
WJ Dally, J Balfour, D Black-Shaffer, J Chen, RC Harting, V Parikh, J Park, ...
Computer 41 (7), 27-32, 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
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
FROSTT: The formidable repository of open sparse tensors and tools
S Smith, JW Choi, J Li, R Vuduc, J Park, X Liu, G Karypis
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
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
Two-step approach to scheduling quantum circuits
GG Guerreschi, J Park
Quantum Science and Technology 3 (4), 045003, 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
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
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
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
Enabling sparse winograd convolution by native pruning
S Li, J Park, PTP Tang
arXiv preprint arXiv:1702.08597, 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
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
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
Efficient backprojection‐based synthetic aperture radar computation with many‐core processors
J Park, PTP Tang, M Smelyanskiy, D Kim, T Benson
Scientific Programming 21 (3-4), 165-179, 2013
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