Register Queues: A New Hardware/Software Approach to Efficient Software Pipelining. - Mikhail Smelyanskiy; Gary S. Tyson; Edward S. Davidson (IEEE PACT, 2000)

Stack Value File: Custom Microarchitecture for the Stack. - Hsien-Hsin S. Lee; Mikhail Smelyanskiy; Chris J. Newburn; Gary S. Tyson (HPCA, 2001)

Evaluating the Use of Register Queues in Software Pipelined Loops. - Gary S. Tyson; Mikhail Smelyanskiy; Edward S. Davidson (IEEE Trans. Computers, 2001)

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 (ASAP, 2003)

Predicate-Aware Scheduling: A Technique for Reducing Resource Constraints. - Mikhail Smelyanskiy; Scott A. Mahlke; Edward S. Davidson; Hsien-Hsin S. Lee (CGO, 2003)

Probabilistic Predicate-Aware Modulo Scheduling. - Mikhail Smelyanskiy; Scott A. Mahlke; Edward S. Davidson (CGO, 2004)

Hardware/software mechanisms for increasing resource utilization on VLIW/EPIC processors. - Mikhail Smelyanskiy (2004)

Scaling performance of interior-point method on large-scale chip multiprocessor system. - Mikhail Smelyanskiy; Victor W. Lee; Daehyun Kim 001; Anthony D. Nguyen; Pradeep Dubey (SC, 2007)

Atomic Vector Operations on Chip Multiprocessors. - Sanjeev Kumar; Daehyun Kim 001; Mikhail Smelyanskiy; Yen-Kuang Chen; Jatin Chhugani; Christopher J. Hughes; Changkyu Kim; Victor W. Lee; Anthony D. Nguyen (ISCA, 2008)

An algorithm for the fast solution of symmetric linear complementarity problems. - José Luis Morales; Jorge Nocedal; Mikhail Smelyanskiy (Numerische Mathematik, 2008)

Convergence of Recognition, Mining, and Synthesis Workloads and Its Implications. - Yen-Kuang Chen; Jatin Chhugani; Pradeep Dubey; Christopher J. Hughes; Daehyun Kim 001; Sanjeev Kumar; Victor W. Lee; Anthony D. Nguyen; Mikhail Smelyanskiy (Proc. IEEE, 2008)

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 001; Alan Kyker; Victor W. Lee; Anthony D. Nguyen; et al. (IEEE Trans. Vis. Comput. Graph., 2009)

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 001; Anthony D. Nguyen; Nadathur Satish; Mikhail Smelyanskiy; Srinivas Chennupaty; Per Hammarlund; Ronak Singhal; Pradeep Dubey (ISCA, 2010)

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 (SC, 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 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures. - Daehyun Kim 001; Joshua Trzasko; Mikhail Smelyanskiy; Clifton Haider; Pradeep Dubey; Armando Manduca (Int. J. Biomed. Imaging, 2011)

High Performance Non-uniform FFT on Modern X86-based Multi-core Systems. - Dhiraj D. Kalamkar; Joshua D. Trzasko; Srinivas Sridharan 002; Mikhail Smelyanskiy; Daehyun Kim 001; Armando Manduca; Yunhong Shu; Matt A. Bernstein; Bharat Kaul; Pradeep Dubey (IPDPS, 2012)

Improving the Performance of Dynamical Simulations Via Multiple Right-Hand Sides. - Xing Liu; Edmond Chow; Karthikeyan Vaidyanathan; Mikhail Smelyanskiy (IPDPS, 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; et al. (SC Companion, 2012)

Optimization of geometric multigrid for emerging multi- and manycore processors. - Samuel Williams 001; Dhiraj D. Kalamkar; Amik Singh; Anand M. Deshpande; Brian van Straalen; Mikhail Smelyanskiy; Ann S. Almgren; Pradeep Dubey; John Shalf; Leonid Oliker (SC, 2012)

Efficient sparse matrix-vector multiplication on x86-based many-core processors. - Xing Liu; Mikhail Smelyanskiy; Edmond Chow; Pradeep Dubey (ICS, 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 (IPDPS, 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 (IPDPS, 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 (ISC, 2013)

Efficient backprojection-based synthetic aperture radar computation with many-core processors. - Jongsoo Park; Ping Tak Peter Tang; Mikhail Smelyanskiy; Daehyun Kim 001; Thomas Benson (Sci. Program., 2013)

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 (IPDPS, 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 001; Aniruddha G. Shet; Bharat Kaul; Bálint Joó; Pradeep Dubey (IPDPS, 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 (SC, 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 (SC, 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 (SC, 2014)

Sparsifying Synchronization for High-Performance Shared-Memory Sparse Triangular Solver. - Jongsoo Park; Mikhail Smelyanskiy; Narayanan Sundaram; Pradeep Dubey (ISC, 2014)

Exploring Shared-Memory Optimizations for an Unstructured Mesh CFD Application on Modern Parallel Systems. - Dheevatsa Mudigere; Srinivas Sridharan 002; Anand M. Deshpande; Jongsoo Park; Alexander Heinecke; Mikhail Smelyanskiy; Bharat Kaul; Pradeep Dubey; Dinesh K. Kaushik; David E. Keyes (IPDPS, 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 (SC, 2015)

Can traditional programming bridge the ninja performance gap for parallel computing applications? - Nadathur Satish; Changkyu Kim; Jatin Chhugani; Hideki Saito 001; Rakesh Krishnaiyer; Mikhail Smelyanskiy; Milind Girkar; Pradeep Dubey (Commun. ACM, 2015)

Sparso: Context-driven Optimizations of Sparse Linear Algebra. - Hongbo Rong; Jongsoo Park; Lingxiang Xiang; Todd A. Anderson 001; Mikhail Smelyanskiy (PACT, 2016)

High Performance Parallel Stochastic Gradient Descent in Shared Memory. - Scott Sallinen; Nadathur Satish; Mikhail Smelyanskiy; Samantika S. Sury; Christopher Ré (IPDPS, 2016)

High performance emulation of quantum circuits. - Thomas Häner; Damian S. Steiger; Mikhail Smelyanskiy; Matthias Troyer (SC, 2016)

Large Scale Distributed Hessian-Free Optimization for Deep Neural Network. - Xi He 004; Dheevatsa Mudigere; Mikhail Smelyanskiy; Martin Takác 001 (CoRR, 2016)

qHiPSTER: The Quantum High Performance Software Testing Environment. - Mikhail Smelyanskiy; Nicolas P. D. Sawaya; Alán Aspuru-Guzik (CoRR, 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)

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)

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)

Distributed Hessian-Free Optimization for Deep Neural Network. - Xi He 004; Dheevatsa Mudigere; Mikhail Smelyanskiy; Martin Takác 001 (AAAI Workshops, 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 (ICLR, 2017)

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; et al. (HPCA, 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; et al. (CoRR, 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; et al. (CoRR, 2018)

Zion: Facebook Next- Generation Large Memory Training Platform. - Misha Smelyanskiy (Hot Chips Symposium, 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 (SysML, 2019)

A Study of BFLOAT16 for Deep Learning Training. - Dhiraj D. Kalamkar; Dheevatsa Mudigere; Naveen Mellempudi; Dipankar Das 002; Kunal Banerjee 001; Sasikanth Avancha; Dharma Teja Vooturi; Nataraj Jammalamadaka; Jianyu Huang; Hector Yuen; Jiyan Yang; Jongsoo Park; et al. (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 020; Udit Gupta; Carole-Jean Wu; Alisson G. Azzolini; Dmytro Dzhulgakov; Andrey Mallevich; et al. (CoRR, 2019)

The Architectural Implications of Facebook's DNN-based Personalized Recommendation. - Udit Gupta; Xiaodong Wang 020; Maxim Naumov; Carole-Jean Wu; Brandon Reagen; David Brooks 001; Bradford Cottel; Kim M. Hazelwood; Bill Jia; Hsien-Hsin S. Lee; Andrey Malevich; Dheevatsa Mudigere; et al. (CoRR, 2019)

Scalable Distributed Training of Recommendation Models: An ASTRA-SIM + NS3 case-study with TCP/IP transport. - Saeed Rashidi; Pallavi Shurpali; Srinivas Sridharan 002; Naader Hassani; Dheevatsa Mudigere; Krishnakumar Nair; Misha Smelyanskiy; Tushar Krishna (Hot Interconnects, 2020)

The Architectural Implications of Facebook's DNN-Based Personalized Recommendation. - Udit Gupta; Carole-Jean Wu; Xiaodong Wang 020; Maxim Naumov; Brandon Reagen; David Brooks 001; Bradford Cottel; Kim M. Hazelwood; Mark Hempstead; Bill Jia; Hsien-Hsin S. Lee; Andrey Malevich; et al. (HPCA, 2020)

RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing. - Liu Ke 001; Udit Gupta; Benjamin Youngjae Cho; David Brooks 001; Vikas Chandra; Utku Diril; Amin Firoozshahian; Kim M. Hazelwood; Bill Jia; Hsien-Hsin S. Lee; Meng Li 004; Bert Maher; et al. (ISCA, 2020)

Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems. - Maxim Naumov; John Kim 001; Dheevatsa Mudigere; Srinivas Sridharan 002; Xiaodong Wang 020; Whitney Zhao; Serhat Yilmaz; Changkyu Kim; Hector Yuen; Mustafa Ozdal; Krishnakumar Nair; Isabel Gao; et al. (CoRR, 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)

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)

High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models. - Dheevatsa Mudigere; Yuchen Hao; Jianyu Huang; Andrew Tulloch; Srinivas Sridharan 002; Xing Liu; Mustafa Ozdal; Jade Nie; Jongsoo Park; Liang Luo; Jie Amy Yang; Leon Gao; et al. (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 003; Hector Yuen; Shishir Juluri; et al. (CoRR, 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)

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; et al. (IEEE Micro, 2021)

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 003; Hector Yuen; Dheevatsa Mudigere; et al. (ICDCS, 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 002; Xing Liu; Mustafa Ozdal; Jade Nie; Jongsoo Park; Liang Luo; Jie Amy Yang; et al. (ISCA, 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 (NSDI, 2022)

Unity: Accelerating DNN Training Through Joint Optimization of Algebraic Transformations and Parallelization. - Colin Unger; Zhihao Jia; Wei Wu 016; Sina Lin; Mandeep Baines; Carlos Efrain Quintero Narvaez; Vinay Ramakrishnaiah; Nirmal Prajapati; Patrick S. McCormick; Jamaludin Mohd-Yusof; Xi Luo; Dheevatsa Mudigere; et al. (OSDI, 2022)

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; et al. (CoRR, 2024)

Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning. - Alisson G. Azzolini; Hannah Brandon; Prithvijit Chattopadhyay; Huayu Chen; Jinju Chu; Yin Cui; Jenna Diamond; Yifan Ding; Francesco Ferroni; Rama Govindaraju; Jinwei Gu; Siddharth Gururani; et al. (CoRR, 2025)