Kim M. Hazelwood

Publication Activity (10 Years)

Years Active: 2000-2024
Publications (10 Years): 33

Top Topics

Personalized Recommendation

Automated Discovery

Top Venues

Publications

Volker Seeker, Chris Cummins, Murray Cole, Björn Franke, Kim M. Hazelwood, Hugh Leather
Revealing Compiler Heuristics Through Automated Discovery and Optimization. CGO (2024)
Carole-Jean Wu, Bilge Acun, Ramya Raghavendra, Kim M. Hazelwood
Beyond Efficiency: Scaling AI Sustainably. CoRR (2024)
Chris Cummins, Volker Seeker, Dejan Grubisic, Mostafa Elhoushi, Youwei Liang, Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Kim M. Hazelwood, Gabriel Synnaeve, Hugh Leather
Large Language Models for Compiler Optimization. CoRR (2023)
Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim M. Hazelwood, Ajitha Rajan, Hugh Leather
BenchDirect: A Directed Language Model for Compiler Benchmarks. CoRR (2023)
Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim M. Hazelwood, Ajitha Rajan, Hugh Leather
BenchPress: A Deep Active Benchmark Generator. CoRR (2022)
Paschalis Mpeis, Pavlos Petoumenos, Kim M. Hazelwood, Hugh Leather
Object Intersection Captures on Interactive Apps to Drive a Crowd-sourced Replay-based Compiler Optimization. ACM Trans. Archit. Code Optim. 19 (3) (2022)
Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim M. Hazelwood, Ajitha Rajan, Hugh Leather
BenchPress: A Deep Active Benchmark Generator. PACT (2022)
Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, Jinshi Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim M. Hazelwood
Sustainable AI: Environmental Implications, Challenges and Opportunities. MLSys (2022)
Smail Kourta, Adel Abderahmane Namani, Fatima Benbouzid-Si Tayeb, Kim M. Hazelwood, Chris Cummins, Hugh Leather, Riyadh Baghdadi
Caviar: an e-graph based TRS for automatic code optimization. CC (2022)
Sean Stirling, Rodrigo C. O. Rocha, Kim M. Hazelwood, Hugh Leather, Michael F. P. O'Boyle, Pavlos Petoumenos
F3M: Fast Focused Function Merging. CGO (2022)
Cheng Fu, Hanxian Huang, Bram Wasti, Chris Cummins, Riyadh Baghdadi, Kim M. Hazelwood, Yuandong Tian, Jishen Zhao, Hugh Leather
Q-gym: An Equality Saturation Framework for DNN Inference Exploiting Weight Repetition. PACT (2022)
Zachary DeVito, Jason Ansel, Will Constable, Michael Suo, Ailing Zhang, Kim M. Hazelwood
Using Python for Model Inference in Deep Learning. CoRR (2021)
Yu Emma Wang, Carole-Jean Wu, Xiaodong Wang, Kim M. Hazelwood, David Brooks
Exploiting Parallelism Opportunities with Deep Learning Frameworks. ACM Trans. Archit. Code Optim. 18 (1) (2021)
Paschalis Mpeis, Pavlos Petoumenos, Kim M. Hazelwood, Hugh Leather
Developer and user-transparent compiler optimization for interactive applications. PLDI (2021)
Rodrigo C. O. Rocha, Pavlos Petoumenos, Zheng Wang, Murray Cole, Kim M. Hazelwood, Hugh Leather
HyFM: function merging for free. LCTES (2021)
Smail Kourta, Adel Namani, Fatima Benbouzid-Si Tayeb, Kim M. Hazelwood, Chris Cummins, Hugh Leather, Riyadh Baghdadi
Caviar: An E-graph Based TRS for Automatic Code Optimization. CoRR (2021)
Bilge Acun, Matthew Murphy, Xiaodong Wang, Jade Nie, Carole-Jean Wu, Kim M. Hazelwood
Understanding Training Efficiency of Deep Learning Recommendation Models at Scale. HPCA (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)
Peter Mattson, Christine Cheng, Gregory F. Diamos, Cody Coleman, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debo Dutta, Udit Gupta, Kim M. Hazelwood, Andy Hock, Xinyuan Huang, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia
MLPerf Training Benchmark. MLSys (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)
Bilge Acun, Matthew Murphy, Xiaodong Wang, Jade Nie, Carole-Jean Wu, Kim M. Hazelwood
Understanding Training Efficiency of Deep Learning Recommendation Models at Scale. CoRR (2020)
Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debojyoti Dutta, Udit Gupta, Kim M. Hazelwood, Andrew Hock, Xinyuan Huang, Bill Jia, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Guokai Ma, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia
MLPerf Training Benchmark. CoRR (2019)
Yu Emma Wang, Carole-Jean Wu, Xiaodong Wang, Kim M. Hazelwood, David Brooks
Exploiting Parallelism Opportunities with Deep Learning Frameworks. CoRR (2019)
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)
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan R. Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
SysML: The New Frontier of Machine Learning Systems. CoRR (2019)
Carole-Jean Wu, David Brooks, Kevin Chen, Douglas Chen, Sy Choudhury, Marat Dukhan, Kim M. Hazelwood, Eldad Isaac, Yangqing Jia, Bill Jia, Tommer Leyvand, Hao Lu, Yang Lu, Lin Qiao, Brandon Reagen, Joe Spisak, Fei Sun, Andrew Tulloch, Peter Vajda, Xiaodong Wang, Yanghan Wang, Bram Wasti, Yiming Wu, Ran Xian, Sungjoo Yoo, Peizhao Zhang
Machine Learning at Facebook: Understanding Inference at the Edge. HPCA (2019)
Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim M. Hazelwood, Asaf Cidon, Sachin Katti
Bandana: Using Non-Volatile Memory for Storing Deep Learning Models. MLSys (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)
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
Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective. HPCA (2018)
Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim M. Hazelwood, Asaf Cidon, Sachin Katti
Bandana: Using Non-volatile Memory for Storing Deep Learning Models. CoRR (2018)
Assaf Eisenman, Darryl Gardner, Islam AbdelRahman, Jens Axboe, Siying Dong, Kim M. Hazelwood, Chris Petersen, Asaf Cidon, Sachin Katti
Reducing DRAM footprint with NVM in facebook. EuroSys (2018)
Svilen Kanev, Juan Pablo Darago, Kim M. Hazelwood, Parthasarathy Ranganathan, Tipp Moseley, Gu-Yeon Wei, David M. Brooks
Profiling a Warehouse-Scale Computer. IEEE Micro 36 (3) (2016)
Svilen Kanev, Juan Pablo Darago, Kim M. Hazelwood, Parthasarathy Ranganathan, Tipp Moseley, Gu-Yeon Wei, David M. Brooks
Profiling a warehouse-scale computer. ISCA (2015)
Svilen Kanev, Kim M. Hazelwood, Gu-Yeon Wei, David M. Brooks
Tradeoffs between power management and tail latency in warehouse-scale applications. IISWC (2014)
Chris Gregg, Jonathan Dorn, Kim M. Hazelwood, Kevin Skadron
Fine-Grained Resource Sharing for Concurrent GPGPU Kernels. HotPar (2012)
Apala Guha, Kim M. Hazelwood, Mary Lou Soffa
Memory optimization of dynamic binary translators for embedded systems. ACM Trans. Archit. Code Optim. 9 (3) (2012)
Chris Gregg, Luther A. Tychonievich, James P. Cohoon, Kim M. Hazelwood
EcoSim: a language and experience teaching parallel programming in elementary school. SIGCSE (2012)
Chris Gregg, Kim M. Hazelwood
Where is the data? Why you cannot debate CPU vs. GPU performance without the answer. ISPASS (2011)
Dan Upton, Kim M. Hazelwood
Finding cool code: An analysis of source-level causes of temperature effects. ISPASS (2011)
Michelle McDaniel, Kim M. Hazelwood
Performance characterization of mobile-class nodes: Why fewer bits is better. ISPASS (2011)
Perhaad Mistry, Chris Gregg, Norman Rubin, David R. Kaeli, Kim M. Hazelwood
Analyzing program flow within a many-kernel OpenCL application. GPGPU (2011)
Kim M. Hazelwood
Process-level virtualization for runtime adaptation of embedded software. DAC (2011)
Vijay Janapa Reddi, Simone Campanoni, Meeta Sharma Gupta, Michael D. Smith, Gu-Yeon Wei, David M. Brooks, Kim M. Hazelwood
Eliminating voltage emergencies via software-guided code transformations. ACM Trans. Archit. Code Optim. 7 (2) (2010)
Dan Upton, Kim M. Hazelwood
Design of a custom VEE core in a chip multiprocessor. SASP (2010)
Apala Guha, Kim M. Hazelwood, Mary Lou Soffa
Balancing memory and performance through selective flushing of software code caches. CASES (2010)
Alex Skaletsky, Tevi Devor, Nadav Chachmon, Robert S. Cohn, Kim M. Hazelwood, Vladimir Vladimirov, Moshe Bach
Dynamic program analysis of Microsoft Windows applications. ISPASS (2010)
Moshe Bach, Mark Charney, Robert Cohn, Elena Demikhovsky, Tevi Devor, Kim M. Hazelwood, Aamer Jaleel, Chi-Keung Luk, Gail Lyons, Harish Patil, Ady Tal
Analyzing Parallel Programs with Pin. Computer 43 (3) (2010)
Apala Guha, Kim M. Hazelwood, Mary Lou Soffa
DBT path selection for holistic memory efficiency and performance. VEE (2010)
Mojtaba Mehrara, Thomas B. Jablin, Dan Upton, David I. August, Kim M. Hazelwood, Scott A. Mahlke
Multicore compilation strategies and challenges. IEEE Signal Process. Mag. 26 (6) (2009)
Daniel W. Williams, Aprotim Sanyal, Dan Upton, Jason Mars, Sudeep Ghosh, Kim M. Hazelwood
A cross-layer approach to heterogeneity and reliability. MEMOCODE (2009)
Kim M. Hazelwood, Mohamed Zahran
Challenges and opportunities at all levels: interactions among operating systems, compilers, and multicore processors. ACM SIGOPS Oper. Syst. Rev. 43 (2) (2009)
Kim M. Hazelwood, Greg Lueck, Robert Cohn
Scalable support for multithreaded applications on dynamic binary instrumentation systems. ISMM (2009)
Duane Merrill, Kim M. Hazelwood
Trace fragment selection within method-based JVMs. VEE (2008)
Arkaitz Ruiz-Alvarez, Kim M. Hazelwood
Evaluating the impact of dynamic binary translation systems on hardware cache performance. IISWC (2008)
Apala Guha, Jason Hiser, Naveen Kumar, Jing Yang, Min Zhao, Shukang Zhou, Bruce R. Childers, Jack W. Davidson, Kim M. Hazelwood, Mary Lou Soffa
Virtual Execution Environments: Support and Tools. IPDPS (2007)
Steven Wallace, Kim M. Hazelwood
SuperPin: Parallelizing Dynamic Instrumentation for Real-Time Performance. CGO (2007)
Apala Guha, Kim M. Hazelwood, Mary Lou Soffa
Reducing Exit Stub Memory Consumption in Code Caches. HiPEAC (2007)
Kim M. Hazelwood, Michael D. Smith
Managing bounded code caches in dynamic binary optimization systems. ACM Trans. Archit. Code Optim. 3 (3) (2006)
Kim M. Hazelwood, Artur Klauser
A dynamic binary instrumentation engine for the ARM architecture. CASES (2006)
Kim M. Hazelwood, Robert S. Cohn
A Cross-Architectural Interface for Code Cache Manipulation. CGO (2006)
David Hiniker, Kim M. Hazelwood, Michael D. Smith
Improving Region Selection in Dynamic Optimization Systems. MICRO (2005)
Chi-Keung Luk, Robert S. Cohn, Robert Muth, Harish Patil, Artur Klauser, P. Geoffrey Lowney, Steven Wallace, Vijay Janapa Reddi, Kim M. Hazelwood
Pin: building customized program analysis tools with dynamic instrumentation. PLDI (2005)
Kim M. Hazelwood, David M. Brooks
Eliminating voltage emergencies via microarchitectural voltage control feedback and dynamic optimization. ISLPED (2004)
Kim M. Hazelwood, James E. Smith
Exploring Code Cache Eviction Granularities in Dynamic Optimization Systems. CGO (2004)
Kim M. Hazelwood, Michael D. Smith
Generational Cache Management of Code Traces in Dynamic Optimization Systems. MICRO (2003)
Kim M. Hazelwood, David Grove
Adaptive Online Context-Sensitive Inlining. CGO (2003)
Kim M. Hazelwood, Michael D. Smith
Code Cache Management Schemes for Dynamic Optimizers. Interaction between Compilers and Computer Architectures (2002)
Kim M. Hazelwood, Thomas M. Conte
A Lightweight Algorithm for Dynamic If-Conversion during Dynamic Optimization. IEEE PACT (2000)