On large-batch training for deep learning: Generalization gap and sharp minima NS Keskar, D Mudigere, J Nocedal, M Smelyanskiy, PTP Tang arXiv preprint arXiv:1609.04836, 2016 | 3723 | 2016 |
Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU VW Lee, C Kim, J Chhugani, M Deisher, D Kim, AD Nguyen, N Satish, ... Proceedings of the 37th annual international symposium on Computer …, 2010 | 1222 | 2010 |
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 | 799 | 2019 |
Applied machine learning at facebook: A datacenter infrastructure perspective K Hazelwood, S Bird, D Brooks, S Chintala, U Diril, D Dzhulgakov, ... 2018 IEEE international symposium on high performance computer architecture …, 2018 | 768 | 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 | 367 | 2019 |
Efficient sparse matrix-vector multiplication on x86-based many-core processors X Liu, M Smelyanskiy, E Chow, P Dubey Proceedings of the 27th international ACM conference on International …, 2013 | 343 | 2013 |
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 | 339 | 2018 |
The architectural implications of facebook's dnn-based personalized recommendation U Gupta, CJ Wu, X Wang, M Naumov, B Reagen, D Brooks, B Cottel, ... 2020 IEEE International Symposium on High Performance Computer Architecture …, 2020 | 332 | 2020 |
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 | 242 | 2020 |
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 | 227 | 2018 |
Design and implementation of the linpack benchmark for single and multi-node systems based on intel® xeon phi coprocessor A Heinecke, K Vaidyanathan, M Smelyanskiy, A Kobotov, R Dubtsov, ... 2013 IEEE 27th International Symposium on Parallel and Distributed …, 2013 | 219 | 2013 |
Exploring simd for molecular dynamics, using intel® xeon® processors and intel® xeon phi coprocessors SJ Pennycook, CJ Hughes, M Smelyanskiy, SA Jarvis 2013 IEEE 27th International symposium on parallel and distributed …, 2013 | 216 | 2013 |
qHiPSTER: The quantum high performance software testing environment M Smelyanskiy, NPD Sawaya, A Aspuru-Guzik arXiv preprint arXiv:1601.07195, 2016 | 187 | 2016 |
Practical optimization for hybrid quantum-classical algorithms GG Guerreschi, M Smelyanskiy arXiv preprint arXiv:1701.01450, 2017 | 178 | 2017 |
Petascale high order dynamic rupture earthquake simulations on heterogeneous supercomputers A Heinecke, A Breuer, S Rettenberger, M Bader, AA Gabriel, C Pelties, ... SC'14: Proceedings of the International Conference for High Performance …, 2014 | 178 | 2014 |
Anatomy of high-performance many-threaded matrix multiplication TM Smith, R Van De Geijn, M Smelyanskiy, JR Hammond, FG Van Zee 2014 IEEE 28th International Parallel and Distributed Processing Symposium …, 2014 | 174 | 2014 |
Can traditional programming bridge the ninja performance gap for parallel computing applications? N Satish, C Kim, J Chhugani, H Saito, R Krishnaiyer, M Smelyanskiy, ... ACM SIGARCH Computer Architecture News 40 (3), 440-451, 2012 | 149 | 2012 |
Convergence of recognition, mining, and synthesis workloads and its implications YK Chen, J Chhugani, P Dubey, CJ Hughes, D Kim, S Kumar, VW Lee, ... Proceedings of the IEEE 96 (5), 790-807, 2008 | 149 | 2008 |
The BLIS framework: Experiments in portability FG Van Zee, TM Smith, B Marker, TM Low, RAVD Geijn, FD Igual, ... ACM Transactions on Mathematical Software (TOMS) 42 (2), 1-19, 2016 | 132 | 2016 |
On large-batch training for deep learning: Generalization gap and sharp minima. arXiv 2016 NS Keskar, D Mudigere, J Nocedal, M Smelyanskiy, PTP Tang arXiv preprint arXiv:1609.04836, 2020 | 126 | 2020 |