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 | 703 | 2019 |
The architectural implications of Facebook's DNN-based personalized recommendation U Gupta, CJ Wu, X Wang, M Naumov, B Reagen, D Brooks, B Cottel, ... IEEE International Symposium on High Performance Computer Architecture (HPCA …, 2020 | 294 | 2020 |
Atomistic simulation of realistically sized nanodevices using NEMO 3-D—Part I: Models and benchmarks G Klimeck, SS Ahmed, H Bae, N Kharche, S Clark, B Haley, S Lee, ... IEEE Transactions on Electron Devices 54 (9), 2079-2089, 2007 | 294 | 2007 |
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 | 210 | 2018 |
Recnmp: Accelerating personalized recommendation with near-memory processing L Ke, U Gupta, BY Cho, D Brooks, V Chandra, U Diril, A Firoozshahian, ... 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture …, 2020 | 209 | 2020 |
Parallel solution of sparse triangular linear systems in the preconditioned iterative methods on the GPU M Naumov Nvidia Technical Report NVR-2011-001, 2011 | 192* | 2011 |
CUSPARSE Library: A Set of Basic Linear Algebra Subroutines for Sparse Matrices M Naumov, LS Chien, P Vandermersch, U Kapasi GPU Technology Conference (GTC), 2010 | 192* | 2010 |
AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks A Devarakonda, M Naumov, M Garland arXiv preprint arXiv:1712.02029, 2017 | 176 | 2017 |
AmgX: A Library for GPU Accelerated Algebraic Multigrid and Preconditioned Iterative Methods M Naumov, M Arsaev, P Castonguay, J Cohen, J Demouth, J Eaton, ... SIAM Journal on Scientific Computing 37 (5), S602-S626, 2015 | 164 | 2015 |
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 | 119* | 2022 |
Compositional embeddings using complementary partitions for memory-efficient recommendation systems HJM Shi, D Mudigere, M Naumov, J Yang Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 114 | 2020 |
Incomplete-LU and Cholesky preconditioned iterative methods using CUSPARSE and CUBLAS M Naumov Nvidia White Paper, 2011 | 106 | 2011 |
Mixed dimension embeddings with application to memory-efficient recommendation systems AA Ginart, M Naumov, D Mudigere, J Yang, J Zou 2021 IEEE International Symposium on Information Theory (ISIT), 2786-2791, 2021 | 102 | 2021 |
Bandana: Using Non-volatile Memory for Storing Deep Learning Models A Eisenman, M Naumov, D Gardner, M Smelyanskiy, S Pupyrev, ... Conference on Machine Learning and Systems (MLSys), 2019 | 89 | 2019 |
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems M Naumov, J Kim, D Mudigere, S Sridharan, X Wang, W Zhao, S Yilmaz, ... arXiv preprint arXiv:2003.09518, 2020 | 84 | 2020 |
Multimillion Atom Simulation of Electronic and Optical Properties of Nanoscale Devices Using NEMO 3-D S Ahmed, N Kharche, R Rahman, M Usman, S Lee, H Ryu, H Bae, ... Encyclopedia of Complexity and Systems Science, 1-69, 2015 | 75* | 2015 |
Parallel Graph Coloring with Applications to the Incomplete-LU Factorization on the GPU M Naumov, P Castonguay, J Cohen Nvidia Technical Report NVR-2015-001, 2015 | 62 | 2015 |
Parallel incomplete-LU and Cholesky factorization in the preconditioned iterative methods on the GPU M Naumov NVIDIA Technical Report NVR-2012-003, 2012 | 32 | 2012 |
On Periodic Functions as Regularizers for Quantization of Neural Networks M Naumov, U Diril, J Park, B Ray, J Jablonski, A Tulloch arXiv preprint arXiv:1811.09862, 2018 | 27 | 2018 |
Parallel Spectral Graph Partitioning M Naumov, T Moon Nvidia Technical Report NVR-2016-001, 2016 | 25 | 2016 |