Mikhail Smelyanskiy

Publication Activity (10 Years)

Years Active: 2000-2022
Publications (10 Years): 24

Top Topics

Personalized Recommendation

Parallel Computing

Top Venues

Publications

Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang, Zhihao Jia, Andrew Tulloch, Srinivas Sridharan, Xing Liu, Mustafa Ozdal, Jade Nie, Jongsoo Park, Liang Luo, Jie Amy Yang, Leon Gao, Dmytro Ivchenko, Aarti Basant, Yuxi Hu, Jiyan Yang, Ehsan K. Ardestani, Xiaodong Wang, Rakesh Komuravelli, Ching-Hsiang Chu, Serhat Yilmaz, Huayu Li, Jiyuan Qian, Zhuobo Feng, Yinbin Ma, Junjie Yang, Ellie Wen, Hong Li, Lin Yang, Chonglin Sun, Whitney Zhao, Dimitry Melts, Krishna Dhulipala, K. R. Kishore, Tyler Graf, Assaf Eisenman, Kiran Kumar Matam, Adi Gangidi, Guoqiang Jerry Chen, Manoj Krishnan, Avinash Nayak, Krishnakumar Nair, Bharath Muthiah, Mahmoud khorashadi, Pallab Bhattacharya, Petr Lapukhov, Maxim Naumov, Ajit Mathews, Lin Qiao, Mikhail Smelyanskiy, Bill Jia, Vijay Rao
Software-hardware co-design for fast and scalable training of deep learning recommendation models. ISCA (2022)
Ehsan K. Ardestani, Changkyu Kim, Seung Jae Lee, Luoshang Pan, Jens Axboe, Valmiki Rampersad, Banit Agrawal, Fuxun Yu, Ansha Yu, Trung Le, Hector Yuen, Dheevatsa Mudigere, Shishir Juluri, Akshat Nanda, Manoj Wodekar, Krishnakumar Nair, Maxim Naumov, Chris Petersen, Mikhail Smelyanskiy, Vijay Rao
Supporting Massive DLRM Inference through Software Defined Memory. ICDCS (2022)
Zhaoxia Deng, Jongsoo Park, Ping Tak Peter Tang, Haixin Liu, Jie Yang, Hector Yuen, Jianyu Huang, Daya Shanker Khudia, Xiaohan Wei, Ellie Wen, Dhruv Choudhary, Raghuraman Krishnamoorthi, Carole-Jean Wu, Nadathur Satish, Changkyu Kim, Maxim Naumov, Sam Naghshineh, Mikhail Smelyanskiy
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale. IEEE Micro 41 (5) (2021)
Daya Shanker Khudia, Jianyu Huang, Protonu Basu, Summer Deng, Haixin Liu, Jongsoo Park, Mikhail Smelyanskiy
FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference. CoRR (2021)
Ehsan K. Ardestani, Changkyu Kim, Seung Jae Lee, Luoshang Pan, Valmiki Rampersad, Jens Axboe, Banit Agrawal, Fuxun Yu, Ansha Yu, Trung Le, Hector Yuen, Shishir Juluri, Akshat Nanda, Manoj Wodekar, Dheevatsa Mudigere, Krishnakumar Nair, Maxim Naumov, Chris Peterson, Mikhail Smelyanskiy, Vijay Rao
Supporting Massive DLRM Inference Through Software Defined Memory. CoRR (2021)
Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang, Andrew Tulloch, Srinivas Sridharan, Xing Liu, Mustafa Ozdal, Jade Nie, Jongsoo Park, Liang Luo, Jie Amy Yang, Leon Gao, Dmytro Ivchenko, Aarti Basant, Yuxi Hu, Jiyan Yang, Ehsan K. Ardestani, Xiaodong Wang, Rakesh Komuravelli, Ching-Hsiang Chu, Serhat Yilmaz, Huayu Li, Jiyuan Qian, Zhuobo Feng, Yinbin Ma, Junjie Yang, Ellie Wen, Hong Li, Lin Yang, Chonglin Sun, Whitney Zhao, Dimitry Melts, Krishna Dhulipala, K. R. Kishore, Tyler Graf, Assaf Eisenman, Kiran Kumar Matam, Adi Gangidi, Guoqiang Jerry Chen, Manoj Krishnan, Avinash Nayak, Krishnakumar Nair, Bharath Muthiah, Mahmoud khorashadi, Pallab Bhattacharya, Petr Lapukhov, Maxim Naumov, Lin Qiao, Mikhail Smelyanskiy, Bill Jia, Vijay Rao
High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models. CoRR (2021)
Zhaoxia Deng, Jongsoo Park, Ping Tak Peter Tang, Haixin Liu, Jie Yang, Hector Yuen, Jianyu Huang, Daya Shanker Khudia, Xiaohan Wei, Ellie Wen, Dhruv Choudhary, Raghuraman Krishnamoorthi, Carole-Jean Wu, Nadathur Satish, Changkyu Kim, Maxim Naumov, Sam Naghshineh, Mikhail Smelyanskiy
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale. CoRR (2021)
Udit Gupta, Carole-Jean Wu, Xiaodong Wang, Maxim Naumov, Brandon Reagen, David Brooks, Bradford Cottel, Kim M. Hazelwood, Mark Hempstead, Bill Jia, Hsien-Hsin S. Lee, Andrey Malevich, Dheevatsa Mudigere, Mikhail Smelyanskiy, Liang Xiong, Xuan Zhang
The Architectural Implications of Facebook's DNN-Based Personalized Recommendation. HPCA (2020)
Maxim Naumov, John Kim, Dheevatsa Mudigere, Srinivas Sridharan, Xiaodong Wang, Whitney Zhao, Serhat Yilmaz, Changkyu Kim, Hector Yuen, Mustafa Ozdal, Krishnakumar Nair, Isabel Gao, Bor-Yiing Su, Jiyan Yang, Mikhail Smelyanskiy
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems. CoRR (2020)
Liu Ke, Udit Gupta, Benjamin Youngjae Cho, David Brooks, Vikas Chandra, Utku Diril, Amin Firoozshahian, Kim M. Hazelwood, Bill Jia, Hsien-Hsin S. Lee, Meng Li, Bert Maher, Dheevatsa Mudigere, Maxim Naumov, Martin Schatz, Mikhail Smelyanskiy, Xiaodong Wang, Brandon Reagen, Carole-Jean Wu, Mark Hempstead, Xuan Zhang
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing. ISCA (2020)
Udit Gupta, Xiaodong Wang, Maxim Naumov, Carole-Jean Wu, Brandon Reagen, David Brooks, Bradford Cottel, Kim M. Hazelwood, Bill Jia, Hsien-Hsin S. Lee, Andrey Malevich, Dheevatsa Mudigere, Mikhail Smelyanskiy, Liang Xiong, Xuan Zhang
The Architectural Implications of Facebook's DNN-based Personalized Recommendation. CoRR (2019)
Liu Ke, Udit Gupta, Carole-Jean Wu, Benjamin Youngjae Cho, Mark Hempstead, Brandon Reagen, Xuan Zhang, David M. Brooks, Vikas Chandra, Utku Diril, Amin Firoozshahian, Kim M. Hazelwood, Bill Jia, Hsien-Hsin S. Lee, Meng Li, Bert Maher, Dheevatsa Mudigere, Maxim Naumov, Martin Schatz, Mikhail Smelyanskiy, Xiaodong Wang
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing. CoRR (2019)
Jongsoo Park, Maxim Naumov, Protonu Basu, Summer Deng, Aravind Kalaiah, Daya Shanker Khudia, James Law, Parth Malani, Andrey Malevich, Nadathur Satish, Juan Miguel Pino, Martin Schatz, Alexander Sidorov, Viswanath Sivakumar, Andrew Tulloch, Xiaodong Wang, Yiming Wu, Hector Yuen, Utku Diril, Dmytro Dzhulgakov, Kim M. Hazelwood, Bill Jia, Yangqing Jia, Lin Qiao, Vijay Rao, Nadav Rotem, Sungjoo Yoo, Mikhail Smelyanskiy
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications. CoRR (2018)
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima. ICLR (2017)
Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takác
Distributed Hessian-Free Optimization for Deep Neural Network. AAAI Workshops (2017)
Thomas Häner, Damian S. Steiger, Mikhail Smelyanskiy, Matthias Troyer
High performance emulation of quantum circuits. SC (2016)
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima. CoRR (2016)
Field G. Van Zee, Tyler M. Smith, Bryan Marker, Tze Meng Low, Robert A. van de Geijn, Francisco D. Igual, Mikhail Smelyanskiy, Xianyi Zhang, Michael Kistler, Vernon Austel, John A. Gunnels, Lee Killough
The BLIS Framework: Experiments in Portability. ACM Trans. Math. Softw. 42 (2) (2016)
Scott Sallinen, Nadathur Satish, Mikhail Smelyanskiy, Samantika S. Sury, Christopher Ré
High Performance Parallel Stochastic Gradient Descent in Shared Memory. IPDPS (2016)
Edmond Chow, Xing Liu, Sanchit Misra, Marat Dukhan, Mikhail Smelyanskiy, Jeff R. Hammond, Yunfei Du, Xiangke Liao, Pradeep Dubey
Scaling up Hartree-Fock calculations on Tianhe-2. Int. J. High Perform. Comput. Appl. 30 (1) (2016)
Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takác
Large Scale Distributed Hessian-Free Optimization for Deep Neural Network. CoRR (2016)
Jongsoo Park, Mikhail Smelyanskiy, Karthikeyan Vaidyanathan, Alexander Heinecke, Dhiraj D. Kalamkar, Md. Mostofa Ali Patwary, Vadim O. Pirogov, Pradeep Dubey, Xing Liu, Carlos Rosales, Cyril Mazauric, Christopher S. Daley
Optimizations in a high-performance conjugate gradient benchmark for IA-based multi- and many-core processors. Int. J. High Perform. Comput. Appl. 30 (1) (2016)
Mikhail Smelyanskiy, Nicolas P. D. Sawaya, Alán Aspuru-Guzik
qHiPSTER: The Quantum High Performance Software Testing Environment. CoRR (2016)
Hongbo Rong, Jongsoo Park, Lingxiang Xiang, Todd A. Anderson, Mikhail Smelyanskiy
Sparso: Context-driven Optimizations of Sparse Linear Algebra. PACT (2016)
Dheevatsa Mudigere, Srinivas Sridharan, Anand M. Deshpande, Jongsoo Park, Alexander Heinecke, Mikhail Smelyanskiy, Bharat Kaul, Pradeep Dubey, Dinesh K. Kaushik, David E. Keyes
Exploring Shared-Memory Optimizations for an Unstructured Mesh CFD Application on Modern Parallel Systems. IPDPS (2015)
Jongsoo Park, Mikhail Smelyanskiy, Ulrike Meier Yang, Dheevatsa Mudigere, Pradeep Dubey
High-performance algebraic multigrid solver optimized for multi-core based distributed parallel systems. SC (2015)
Nadathur Satish, Changkyu Kim, Jatin Chhugani, Hideki Saito, Rakesh Krishnaiyer, Mikhail Smelyanskiy, Milind Girkar, Pradeep Dubey
Can traditional programming bridge the ninja performance gap for parallel computing applications? Commun. ACM 58 (5) (2015)
Karthikeyan Vaidyanathan, Kiran Pamnany, Dhiraj D. Kalamkar, Alexander Heinecke, Mikhail Smelyanskiy, Jongsoo Park, Daehyun Kim, Aniruddha G. Shet, Bharat Kaul, Bálint Joó, Pradeep Dubey
Improving Communication Performance and Scalability of Native Applications on Intel Xeon Phi Coprocessor Clusters. IPDPS (2014)
Jongsoo Park, Mikhail Smelyanskiy, Karthikeyan Vaidyanathan, Alexander Heinecke, Dhiraj D. Kalamkar, Xing Liu, Md. Mostofa Ali Patwary, Yutong Lu, Pradeep Dubey
Efficient Shared-Memory Implementation of High-Performance Conjugate Gradient Benchmark and its Application to Unstructured Matrices. SC (2014)
Simon Heybrock, Bálint Joó, Dhiraj D. Kalamkar, Mikhail Smelyanskiy, Karthikeyan Vaidyanathan, Tilo Wettig, Pradeep Dubey
Lattice QCD with Domain Decomposition on Intel® Xeon Phi Co-Processors. SC (2014)
Jongsoo Park, Mikhail Smelyanskiy, Narayanan Sundaram, Pradeep Dubey
Sparsifying Synchronization for High-Performance Shared-Memory Sparse Triangular Solver. ISC (2014)
Alexander Heinecke, Alexander Breuer, Sebastian Rettenberger, Michael Bader, Alice-Agnes Gabriel, Christian Pelties, Arndt Bode, William Barth, Xiangke Liao, Karthikeyan Vaidyanathan, Mikhail Smelyanskiy, Pradeep Dubey
Petascale High Order Dynamic Rupture Earthquake Simulations on Heterogeneous Supercomputers. SC (2014)
Tyler M. Smith, Robert A. van de Geijn, Mikhail Smelyanskiy, Jeff R. Hammond, Field G. Van Zee
Anatomy of High-Performance Many-Threaded Matrix Multiplication. IPDPS (2014)
Bálint Joó, Dhiraj D. Kalamkar, Karthikeyan Vaidyanathan, Mikhail Smelyanskiy, Kiran Pamnany, Victor W. Lee, Pradeep Dubey, William A. Watson III
Lattice QCD on Intel® Xeon PhiTM Coprocessors. ISC (2013)
Xing Liu, Mikhail Smelyanskiy, Edmond Chow, Pradeep Dubey
Efficient sparse matrix-vector multiplication on x86-based many-core processors. ICS (2013)
Alexander Heinecke, Karthikeyan Vaidyanathan, Mikhail Smelyanskiy, Alexander Kobotov, Roman Dubtsov, Greg Henry, Aniruddha G. Shet, George Chrysos, Pradeep Dubey
Design and Implementation of the Linpack Benchmark for Single and Multi-node Systems Based on Intel® Xeon Phi Coprocessor. IPDPS (2013)
Simon J. Pennycook, Christopher J. Hughes, Mikhail Smelyanskiy, Stephen A. Jarvis
Exploring SIMD for Molecular Dynamics, Using Intel® Xeon® Processors and Intel® Xeon Phi Coprocessors. IPDPS (2013)
Jongsoo Park, Ping Tak Peter Tang, Mikhail Smelyanskiy, Daehyun Kim, Thomas Benson
Efficient backprojection-based synthetic aperture radar computation with many-core processors. Sci. Program. 21 (3-4) (2013)
Xing Liu, Edmond Chow, Karthikeyan Vaidyanathan, Mikhail Smelyanskiy
Improving the Performance of Dynamical Simulations Via Multiple Right-Hand Sides. IPDPS (2012)
Nadathur Satish, Changkyu Kim, Jatin Chhugani, Hideki Saito, Rakesh Krishnaiyer, Mikhail Smelyanskiy, Milind Girkar, Pradeep Dubey
Can traditional programming bridge the Ninja performance gap for parallel computing applications? ISCA (2012)
Jongsoo Park, Ping Tak Peter Tang, Mikhail Smelyanskiy, Daehyun Kim, Thomas Benson
Efficient backprojection-based synthetic aperture radar computation with many-core processors. SC (2012)
Mikhail Smelyanskiy, Jason Sewall, Dhiraj D. Kalamkar, Nadathur Satish, Pradeep Dubey, Nikita Astafiev, Ilya Burylov, Andrey Nikolaev, Sergey Maidanov, Shuo Li, Sunil Kulkarni, Charles H. Finan, Ekaterina Gonina
Analysis and Optimization of Financial Analytics Benchmark on Modern Multi- and Many-core IA-Based Architectures. SC Companion (2012)
Samuel Williams, Dhiraj D. Kalamkar, Amik Singh, Anand M. Deshpande, Brian van Straalen, Mikhail Smelyanskiy, Ann S. Almgren, Pradeep Dubey, John Shalf, Leonid Oliker
Optimization of geometric multigrid for emerging multi- and manycore processors. SC (2012)
Dhiraj D. Kalamkar, Joshua D. Trzasko, Srinivas Sridharan, Mikhail Smelyanskiy, Daehyun Kim, Armando Manduca, Yunhong Shu, Matt A. Bernstein, Bharat Kaul, Pradeep Dubey
High Performance Non-uniform FFT on Modern X86-based Multi-core Systems. IPDPS (2012)
Daehyun Kim, Joshua Trzasko, Mikhail Smelyanskiy, Clifton Haider, Pradeep Dubey, Armando Manduca
High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures. Int. J. Biomed. Imaging 2011 (2011)
Mikhail Smelyanskiy, Karthikeyan Vaidyanathan, Jee W. Choi, Bálint Joó, Jatin Chhugani, Michael A. Clark, Pradeep Dubey
High-performance lattice QCD for multi-core based parallel systems using a cache-friendly hybrid threaded-MPI approach. SC (2011)
Michael Deisher, Mikhail Smelyanskiy, Brian Nickerson, Victor W. Lee, Michael Chuvelev, Pradeep Dubey
Designing and dynamically load balancing hybrid LU for multi/many-core. Comput. Sci. Res. Dev. 26 (3-4) (2011)
Victor W. Lee, Changkyu Kim, Jatin Chhugani, Michael Deisher, Daehyun Kim, Anthony D. Nguyen, Nadathur Satish, Mikhail Smelyanskiy, Srinivas Chennupaty, Per Hammarlund, Ronak Singhal, Pradeep Dubey
Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU. ISCA (2010)
Mikhail Smelyanskiy, David R. Holmes III, Jatin Chhugani, Alan Larson, Doug Carmean, Dennis P. Hanson, Pradeep Dubey, Kurt Augustine, Daehyun Kim, Alan Kyker, Victor W. Lee, Anthony D. Nguyen, Larry Seiler, Richard A. Robb
Mapping High-Fidelity Volume Rendering for Medical Imaging to CPU, GPU and Many-Core Architectures. IEEE Trans. Vis. Comput. Graph. 15 (6) (2009)
Yen-Kuang Chen, Jatin Chhugani, Pradeep Dubey, Christopher J. Hughes, Daehyun Kim, Sanjeev Kumar, Victor W. Lee, Anthony D. Nguyen, Mikhail Smelyanskiy
Convergence of Recognition, Mining, and Synthesis Workloads and Its Implications. Proc. IEEE 96 (5) (2008)
José Luis Morales, Jorge Nocedal, Mikhail Smelyanskiy
An algorithm for the fast solution of symmetric linear complementarity problems. Numerische Mathematik 111 (2) (2008)
Sanjeev Kumar, Daehyun Kim, Mikhail Smelyanskiy, Yen-Kuang Chen, Jatin Chhugani, Christopher J. Hughes, Changkyu Kim, Victor W. Lee, Anthony D. Nguyen
Atomic Vector Operations on Chip Multiprocessors. ISCA (2008)
Mikhail Smelyanskiy, Victor W. Lee, Daehyun Kim, Anthony D. Nguyen, Pradeep Dubey
Scaling performance of interior-point method on large-scale chip multiprocessor system. SC (2007)
Mikhail Smelyanskiy, Scott A. Mahlke, Edward S. Davidson
Probabilistic Predicate-Aware Modulo Scheduling. CGO (2004)
Kevin Fan, Nathan Clark, Michael L. Chu, K. V. Manjunath, Rajiv A. Ravindran, Mikhail Smelyanskiy, Scott A. Mahlke
Systematic Register Bypass Customization for Application-Specific Processors. ASAP (2003)
Mikhail Smelyanskiy, Scott A. Mahlke, Edward S. Davidson, Hsien-Hsin S. Lee
Predicate-Aware Scheduling: A Technique for Reducing Resource Constraints. CGO (2003)
Hsien-Hsin S. Lee, Mikhail Smelyanskiy, Chris J. Newburn, Gary S. Tyson
Stack Value File: Custom Microarchitecture for the Stack. HPCA (2001)
Mikhail Smelyanskiy, Gary S. Tyson, Edward S. Davidson
Register Queues: A New Hardware/Software Approach to Efficient Software Pipelining. IEEE PACT (2000)