Maxim Naumov
Maxim Naumov
Facebook (Research Scientist)
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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
Parallel solution of sparse triangular linear systems in the preconditioned iterative methods on the GPU
M Naumov
Nvidia Technical Report NVR-2011-001, 2011
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
Incomplete-LU and Cholesky preconditioned iterative methods using CUSPARSE and CUBLAS
M Naumov
Nvidia White Paper, 2011
AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks
A Devarakonda, M Naumov, M Garland
arXiv preprint arXiv:1712.02029, 2017
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
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
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
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
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
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
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
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
Parallel incomplete-LU and Cholesky factorization in the preconditioned iterative methods on the GPU
M Naumov
NVIDIA Technical Report NVR-2012-003, 2012
Exact calculation of entanglement in a 19-site two-dimensional spin system
Q Xu, S Kais, M Naumov, A Sameh
Physical Review A 81 (2), 022324, 2010
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
A Ginart, M Naumov, D Mudigere, J Yang, J Zou
arXiv preprint arXiv:1909.11810, 2019
A tearing-based hybrid parallel banded linear system solver
M Naumov, A Sameh
Journal of Computational and Applied Mathematics 226 (2), 306-318, 2009
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
Parallel Spectral Graph Partitioning
M Naumov, T Moon
Nvidia Technical Report NVR-2016-001, 2016
Preconditioned Block‐Iterative Methods on GPUs
M Naumov
Proc. Applied Mathematics and Mechanics 12 (1), 11-14, 2012
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