Mikhail Smelyanskiy

According to our database, Mikhail Smelyanskiy authored at least 65 papers between 2000 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Nemotron-4 340B Technical Report.

Bo Adler, Niket Agarwal, Ashwath Aithal, Dong H. Anh, Pallab Bhattacharya, Annika Brundyn, Jared Casper, Bryan Catanzaro, Sharon Clay, Jonathan M. Cohen, Sirshak Das, Ayush Dattagupta, Olivier Delalleau, Leon Derczynski, Yi Dong, Daniel Egert, Ellie Evans, Aleksander Ficek, Denys Fridman, Shaona Ghosh, Boris Ginsburg, Igor Gitman, Tomasz Grzegorzek, Robert Hero, Jining Huang, Vibhu Jawa, Joseph Jennings, Aastha Jhunjhunwala, John Kamalu, Sadaf Khan, Oleksii Kuchaiev, Patrick LeGresley, Hui Li, Jiwei Liu, Zihan Liu, Eileen Long, Ameya Sunil Mahabaleshwarkar, Somshubra Majumdar, James Maki, Miguel Martinez, Maer Rodrigues de Melo, Ivan Moshkov, Deepak Narayanan, Sean Narenthiran, Jesus Navarro, Phong Nguyen, Osvald Nitski, Vahid Noroozi, Guruprasad Nutheti, Christopher Parisien, Jupinder Parmar, Mostofa Patwary, Krzysztof Pawelec, Wei Ping, Shrimai Prabhumoye, Rajarshi Roy, Trisha Saar, Vasanth Rao Naik Sabavat, Sanjeev Satheesh, Jane Polak Scowcroft, Jason Sewall, Pavel Shamis, Gerald Shen, Mohammad Shoeybi, Dave Sizer, Misha Smelyanskiy, Felipe Soares, Makesh Narsimhan Sreedhar, Dan Su, Sandeep Subramanian, Shengyang Sun, Shubham Toshniwal, Hao Wang, Zhilin Wang, Jiaxuan You, Jiaqi Zeng, Jimmy Zhang, Jing Zhang, Vivienne Zhang, Yian Zhang, Chen Zhu

CoRR, 2024

2022

Unity: Accelerating DNN Training Through Joint Optimization of Algebraic Transformations and Parallelization.

Colin Unger, Zhihao Jia, Wei Wu, Sina Lin, Mandeep Baines, Carlos Efrain Quintero Narvaez, Vinay Ramakrishnaiah, Nirmal Prajapati, Patrick S. McCormick, Jamaludin Mohd-Yusof, Xi Luo, Dheevatsa Mudigere, Jongsoo Park, Misha Smelyanskiy, Alex Aiken

Proceedings of the 16th USENIX Symposium on Operating Systems Design and Implementation, 2022

Check-N-Run: a Checkpointing System for Training Deep Learning Recommendation Models.

Assaf Eisenman, Kiran Kumar Matam, Steven Ingram, Dheevatsa Mudigere, Raghuraman Krishnamoorthi, Krishnakumar Nair, Misha Smelyanskiy, Murali Annavaram

Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation, 2022

Software-hardware co-design for fast and scalable training of deep learning recommendation models.

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

Proceedings of the ISCA '22: The 49th Annual International Symposium on Computer Architecture, New York, New York, USA, June 18, 2022

Supporting Massive DLRM Inference through Software Defined Memory.

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

Proceedings of the 42nd IEEE International Conference on Distributed Computing Systems, 2022

2021

Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale.

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

IEEE Micro, 2021

Differentiable NAS Framework and Application to Ads CTR Prediction.

Ravi Krishna, Aravind Kalaiah, Bichen Wu, Maxim Naumov, Dheevatsa Mudigere, Misha Smelyanskiy, Kurt Keutzer

CoRR, 2021

Supporting Massive DLRM Inference Through Software Defined Memory.

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

CoRR, 2021

High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models.

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

CoRR, 2021

FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference.

Daya Shanker Khudia, Jianyu Huang, Protonu Basu, Summer Deng, Haixin Liu, Jongsoo Park, Mikhail Smelyanskiy

CoRR, 2021

2020

Check-N-Run: A Checkpointing System for Training Recommendation Models.

Assaf Eisenman, Kiran Kumar Matam, Steven Ingram, Dheevatsa Mudigere, Raghuraman Krishnamoorthi, Murali Annavaram, Krishnakumar Nair, Misha Smelyanskiy

CoRR, 2020

Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems.

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

CoRR, 2020

RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing.

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

Proceedings of the 47th ACM/IEEE Annual International Symposium on Computer Architecture, 2020

The Architectural Implications of Facebook's DNN-Based Personalized Recommendation.

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

Proceedings of the IEEE International Symposium on High Performance Computer Architecture, 2020

2019

The Architectural Implications of Facebook's DNN-based Personalized Recommendation.

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

CoRR, 2019

Deep Learning Recommendation Model for Personalization and Recommendation Systems.

Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii, Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko, Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong, Misha Smelyanskiy

CoRR, 2019

A Study of BFLOAT16 for Deep Learning Training.

Dhiraj D. Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi, Dipankar Das, Kunal Banerjee, Sasikanth Avancha, Dharma Teja Vooturi, Nataraj Jammalamadaka, Jianyu Huang, Hector Yuen, Jiyan Yang, Jongsoo Park, Alexander Heinecke, Evangelos Georganas, Sudarshan Srinivasan, Abhisek Kundu, Misha Smelyanskiy, Bharat Kaul, Pradeep Dubey

CoRR, 2019

Bandana: Using Non-Volatile Memory for Storing Deep Learning Models.

Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim M. Hazelwood, Asaf Cidon, Sachin Katti

Proceedings of the Second Conference on Machine Learning and Systems, SysML 2019, 2019

Zion: Facebook Next- Generation Large Memory Training Platform.

Misha Smelyanskiy

Proceedings of the 2019 IEEE Hot Chips 31 Symposium (HCS), 2019

2018

Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications.

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

CoRR, 2018

Glow: Graph Lowering Compiler Techniques for Neural Networks.

Nadav Rotem, Jordan Fix, Saleem Abdulrasool, Summer Deng, Roman Dzhabarov, James Hegeman, Roman Levenstein, Bert Maher, Nadathur Satish, Jakob Olesen, Jongsoo Park, Artem Rakhov, Misha Smelyanskiy

CoRR, 2018

Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective.

Kim M. Hazelwood, Sarah Bird, David M. Brooks, Soumith Chintala, Utku Diril, Dmytro Dzhulgakov, Mohamed Fawzy, Bill Jia, Yangqing Jia, Aditya Kalro, James Law, Kevin Lee, Jason Lu, Pieter Noordhuis, Misha Smelyanskiy, Liang Xiong, Xiaodong Wang

Proceedings of the IEEE International Symposium on High Performance Computer Architecture, 2018

2017

On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang

Proceedings of the 5th International Conference on Learning Representations, 2017

Distributed Hessian-Free Optimization for Deep Neural Network.

Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takác

Proceedings of the Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

The BLIS Framework: Experiments in Portability.

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

ACM Trans. Math. Softw., 2016

Optimizations in a high-performance conjugate gradient benchmark for IA-based multi- and many-core processors.

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

Int. J. High Perform. Comput. Appl., 2016

Scaling up Hartree-Fock calculations on Tianhe-2.

Edmond Chow, Xing Liu, Sanchit Misra, Marat Dukhan, Mikhail Smelyanskiy, Jeff R. Hammond, Yunfei Du, Xiangke Liao, Pradeep Dubey

Int. J. High Perform. Comput. Appl., 2016

qHiPSTER: The Quantum High Performance Software Testing Environment.

Mikhail Smelyanskiy, Nicolas P. D. Sawaya, Alán Aspuru-Guzik

CoRR, 2016

Large Scale Distributed Hessian-Free Optimization for Deep Neural Network.

Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takác

CoRR, 2016

High performance emulation of quantum circuits.

Thomas Häner, Damian S. Steiger, Mikhail Smelyanskiy, Matthias Troyer

Proceedings of the International Conference for High Performance Computing, 2016

High Performance Parallel Stochastic Gradient Descent in Shared Memory.

Scott Sallinen, Nadathur Satish, Mikhail Smelyanskiy, Samantika S. Sury, Christopher Ré

Proceedings of the 2016 IEEE International Parallel and Distributed Processing Symposium, 2016

Sparso: Context-driven Optimizations of Sparse Linear Algebra.

Hongbo Rong, Jongsoo Park, Lingxiang Xiang, Todd A. Anderson, Mikhail Smelyanskiy

Proceedings of the 2016 International Conference on Parallel Architectures and Compilation, 2016

2015

Can traditional programming bridge the ninja performance gap for parallel computing applications?

Nadathur Satish, Changkyu Kim, Jatin Chhugani, Hideki Saito, Rakesh Krishnaiyer, Mikhail Smelyanskiy, Milind Girkar, Pradeep Dubey

Commun. ACM, 2015

High-performance algebraic multigrid solver optimized for multi-core based distributed parallel systems.

Jongsoo Park, Mikhail Smelyanskiy, Ulrike Meier Yang, Dheevatsa Mudigere, Pradeep Dubey

Proceedings of the International Conference for High Performance Computing, 2015

Exploring Shared-Memory Optimizations for an Unstructured Mesh CFD Application on Modern Parallel Systems.

Dheevatsa Mudigere, Srinivas Sridharan, Anand M. Deshpande, Jongsoo Park, Alexander Heinecke, Mikhail Smelyanskiy, Bharat Kaul, Pradeep Dubey, Dinesh K. Kaushik, David E. Keyes

Proceedings of the 2015 IEEE International Parallel and Distributed Processing Symposium, 2015

2014

Sparsifying Synchronization for High-Performance Shared-Memory Sparse Triangular Solver.

Jongsoo Park, Mikhail Smelyanskiy, Narayanan Sundaram, Pradeep Dubey

Proceedings of the Supercomputing - 29th International Conference, 2014

Efficient Shared-Memory Implementation of High-Performance Conjugate Gradient Benchmark and its Application to Unstructured Matrices.

Jongsoo Park, Mikhail Smelyanskiy, Karthikeyan Vaidyanathan, Alexander Heinecke, Dhiraj D. Kalamkar, Xing Liu, Md. Mostofa Ali Patwary, Yutong Lu, Pradeep Dubey

Proceedings of the International Conference for High Performance Computing, 2014

Lattice QCD with Domain Decomposition on Intel® Xeon Phi Co-Processors.

Simon Heybrock, Bálint Joó, Dhiraj D. Kalamkar, Mikhail Smelyanskiy, Karthikeyan Vaidyanathan, Tilo Wettig, Pradeep Dubey

Proceedings of the International Conference for High Performance Computing, 2014

Petascale High Order Dynamic Rupture Earthquake Simulations on Heterogeneous Supercomputers.

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

Proceedings of the International Conference for High Performance Computing, 2014

Improving Communication Performance and Scalability of Native Applications on Intel Xeon Phi Coprocessor Clusters.

Karthikeyan Vaidyanathan, Kiran Pamnany, Dhiraj D. Kalamkar, Alexander Heinecke, Mikhail Smelyanskiy, Jongsoo Park, Daehyun Kim, Aniruddha G. Shet, Bharat Kaul, Bálint Joó, Pradeep Dubey

Proceedings of the 2014 IEEE 28th International Parallel and Distributed Processing Symposium, 2014

Anatomy of High-Performance Many-Threaded Matrix Multiplication.

Tyler M. Smith, Robert A. van de Geijn, Mikhail Smelyanskiy, Jeff R. Hammond, Field G. Van Zee

Proceedings of the 2014 IEEE 28th International Parallel and Distributed Processing Symposium, 2014

2013

Efficient backprojection-based synthetic aperture radar computation with many-core processors.

Jongsoo Park, Ping Tak Peter Tang, Mikhail Smelyanskiy, Daehyun Kim, Thomas Benson

Sci. Program., 2013

Lattice QCD on Intel® Xeon PhiTM Coprocessors.

Bálint Joó, Dhiraj D. Kalamkar, Karthikeyan Vaidyanathan, Mikhail Smelyanskiy, Kiran Pamnany, Victor W. Lee, Pradeep Dubey, William A. Watson III

Proceedings of the Supercomputing - 28th International Supercomputing Conference, 2013

Exploring SIMD for Molecular Dynamics, Using Intel® Xeon® Processors and Intel® Xeon Phi Coprocessors.

Simon J. Pennycook, Christopher J. Hughes, Mikhail Smelyanskiy, Stephen A. Jarvis

Proceedings of the 27th IEEE International Symposium on Parallel and Distributed Processing, 2013

Design and Implementation of the Linpack Benchmark for Single and Multi-node Systems Based on Intel® Xeon Phi Coprocessor.

Alexander Heinecke, Karthikeyan Vaidyanathan, Mikhail Smelyanskiy, Alexander Kobotov, Roman Dubtsov, Greg Henry, Aniruddha G. Shet, George Chrysos, Pradeep Dubey

Proceedings of the 27th IEEE International Symposium on Parallel and Distributed Processing, 2013

Efficient sparse matrix-vector multiplication on x86-based many-core processors.

Xing Liu, Mikhail Smelyanskiy, Edmond Chow, Pradeep Dubey

Proceedings of the International Conference on Supercomputing, 2013

2012

Optimization of geometric multigrid for emerging multi- and manycore processors.

Samuel Williams, Dhiraj D. Kalamkar, Amik Singh, Anand M. Deshpande, Brian van Straalen, Mikhail Smelyanskiy, Ann S. Almgren, Pradeep Dubey, John Shalf, Leonid Oliker

Proceedings of the SC Conference on High Performance Computing Networking, 2012

Analysis and Optimization of Financial Analytics Benchmark on Modern Multi- and Many-core IA-Based Architectures.

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

Proceedings of the 2012 SC Companion: High Performance Computing, 2012

Improving the Performance of Dynamical Simulations Via Multiple Right-Hand Sides.

Xing Liu, Edmond Chow, Karthikeyan Vaidyanathan, Mikhail Smelyanskiy

Proceedings of the 26th IEEE International Parallel and Distributed Processing Symposium, 2012

High Performance Non-uniform FFT on Modern X86-based Multi-core Systems.

Dhiraj D. Kalamkar, Joshua D. Trzasko, Srinivas Sridharan, Mikhail Smelyanskiy, Daehyun Kim, Armando Manduca, Yunhong Shu, Matt A. Bernstein, Bharat Kaul, Pradeep Dubey

Proceedings of the 26th IEEE International Parallel and Distributed Processing Symposium, 2012

2011

High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures.

Daehyun Kim, Joshua Trzasko, Mikhail Smelyanskiy, Clifton Haider, Pradeep Dubey, Armando Manduca

Int. J. Biomed. Imaging, 2011

Designing and dynamically load balancing hybrid LU for multi/many-core.

Michael Deisher, Mikhail Smelyanskiy, Brian Nickerson, Victor W. Lee, Michael Chuvelev, Pradeep Dubey

Comput. Sci. Res. Dev., 2011

High-performance lattice QCD for multi-core based parallel systems using a cache-friendly hybrid threaded-MPI approach.

Mikhail Smelyanskiy, Karthikeyan Vaidyanathan, Jee W. Choi, Bálint Joó, Jatin Chhugani, Michael A. Clark, Pradeep Dubey

Proceedings of the Conference on High Performance Computing Networking, 2011

2010

Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU.

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

Proceedings of the 37th International Symposium on Computer Architecture (ISCA 2010), 2010

2009

Mapping High-Fidelity Volume Rendering for Medical Imaging to CPU, GPU and Many-Core Architectures.

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

IEEE Trans. Vis. Comput. Graph., 2009

2008

Convergence of Recognition, Mining, and Synthesis Workloads and Its Implications.

Yen-Kuang Chen, Jatin Chhugani, Pradeep Dubey, Christopher J. Hughes, Daehyun Kim, Sanjeev Kumar, Victor W. Lee, Anthony D. Nguyen, Mikhail Smelyanskiy

Proc. IEEE, 2008

An algorithm for the fast solution of symmetric linear complementarity problems.

José Luis Morales, Jorge Nocedal, Mikhail Smelyanskiy

Numerische Mathematik, 2008

Atomic Vector Operations on Chip Multiprocessors.

Sanjeev Kumar, Daehyun Kim, Mikhail Smelyanskiy, Yen-Kuang Chen, Jatin Chhugani, Christopher J. Hughes, Changkyu Kim, Victor W. Lee, Anthony D. Nguyen

Proceedings of the 35th International Symposium on Computer Architecture (ISCA 2008), 2008

2007

Scaling performance of interior-point method on large-scale chip multiprocessor system.

Mikhail Smelyanskiy, Victor W. Lee, Daehyun Kim, Anthony D. Nguyen, Pradeep Dubey

Proceedings of the ACM/IEEE Conference on High Performance Networking and Computing, 2007

2004

Hardware/software mechanisms for increasing resource utilization on VLIW/EPIC processors.

Mikhail Smelyanskiy

PhD thesis, 2004

Probabilistic Predicate-Aware Modulo Scheduling.

Mikhail Smelyanskiy, Scott A. Mahlke, Edward S. Davidson

Proceedings of the 2nd IEEE / ACM International Symposium on Code Generation and Optimization (CGO 2004), 2004

2003

Predicate-Aware Scheduling: A Technique for Reducing Resource Constraints.

Mikhail Smelyanskiy, Scott A. Mahlke, Edward S. Davidson, Hsien-Hsin S. Lee

Proceedings of the 1st IEEE / ACM International Symposium on Code Generation and Optimization (CGO 2003), 2003

Systematic Register Bypass Customization for Application-Specific Processors.

Kevin Fan, Nathan Clark, Michael L. Chu, K. V. Manjunath, Rajiv A. Ravindran, Mikhail Smelyanskiy, Scott A. Mahlke

Proceedings of the 14th IEEE International Conference on Application-Specific Systems, 2003

2001

Stack Value File: Custom Microarchitecture for the Stack.

Hsien-Hsin S. Lee, Mikhail Smelyanskiy, Chris J. Newburn, Gary S. Tyson

Proceedings of the Seventh International Symposium on High-Performance Computer Architecture (HPCA'01), 2001

2000

Register Queues: A New Hardware/Software Approach to Efficient Software Pipelining.

Mikhail Smelyanskiy, Gary S. Tyson, Edward S. Davidson

Proceedings of the 2000 International Conference on Parallel Architectures and Compilation Techniques (PACT'00), 2000