Kim M. Hazelwood

75 papers A* 11A 11B 10C 2Misc 2Journal 26Unranked 11

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Beyond Efficiency: Scaling AI Sustainably. CoRR Carole-Jean Wu, Bilge Acun, Ramya Raghavendra, Kim M. Hazelwood
2024	J	`jnl`	Beyond Efficiency: Scaling AI Sustainably. IEEE Micro Carole-Jean Wu, Bilge Acun, Ramya Raghavendra, Kim M. Hazelwood
2024	A	`conf`	Revealing Compiler Heuristics Through Automated Discovery and Optimization. CGO Volker Seeker, Chris Cummins, Murray Cole, Björn Franke, Kim M. Hazelwood, Hugh Leather
2023	J	`jnl`	BenchDirect: A Directed Language Model for Compiler Benchmarks. CoRR Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim M. Hazelwood, Ajitha Rajan, Hugh Leather
2023	J	`jnl`	Large Language Models for Compiler Optimization. CoRR Chris Cummins, Volker Seeker, Dejan Grubisic, Mostafa Elhoushi, Youwei Liang, Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Kim M. Hazelwood, Gabriel Synnaeve, Hugh Leather
2022	B	`conf`	BenchPress: A Deep Active Benchmark Generator. PACT Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim M. Hazelwood, Ajitha Rajan, Hugh Leather
2022	J	`jnl`	BenchPress: A Deep Active Benchmark Generator. CoRR Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim M. Hazelwood, Ajitha Rajan, Hugh Leather
2022	B	`conf`	Caviar: an e-graph based TRS for automatic code optimization. CC Smail Kourta, Adel Abderahmane Namani, Fatima Benbouzid-Si Tayeb, Kim M. Hazelwood, Chris Cummins, Hugh Leather, Riyadh Baghdadi
2022	A	`conf`	F3M: Fast Focused Function Merging. CGO Sean Stirling, Rodrigo C. O. Rocha, Kim M. Hazelwood, Hugh Leather, Michael F. P. O'Boyle, Pavlos Petoumenos
2022	J	`jnl`	Object Intersection Captures on Interactive Apps to Drive a Crowd-sourced Replay-based Compiler Optimization. ACM Trans. Archit. Code Optim. Paschalis Mpeis, Pavlos Petoumenos, Kim M. Hazelwood, Hugh Leather
2022	B	`conf`	Q-gym: An Equality Saturation Framework for DNN Inference Exploiting Weight Repetition. PACT Cheng Fu, Hanxian Huang, Bram Wasti, Chris Cummins, Riyadh Baghdadi, Kim M. Hazelwood, Yuandong Tian, Jishen Zhao, Hugh Leather
2022	—	`conf`	Sustainable AI: Environmental Implications, Challenges and Opportunities. MLSys 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 C. Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim M. Hazelwood
2021	J	`jnl`	Caviar: An E-graph Based TRS for Automatic Code Optimization. CoRR Smail Kourta, Adel Namani, Fatima Benbouzid-Si Tayeb, Kim M. Hazelwood, Chris Cummins, Hugh Leather, Riyadh Baghdadi
2021	J	`jnl`	Datacenter-Scale Analysis and Optimization of GPU Machine Learning Workloads. IEEE Micro Lukasz Wesolowski, Bilge Acun, Valentin Andrei, Adnan Aziz, Gisle Dankel, Christopher Gregg, Xiaoqiao Meng, Cyril Meurillon, Denis Sheahan, Lei Tian, Janet Yang, Peifeng Yu, Kim M. Hazelwood
2021	A*	`conf`	Developer and user-transparent compiler optimization for interactive applications. PLDI Paschalis Mpeis, Pavlos Petoumenos, Kim M. Hazelwood, Hugh Leather
2021	J	`jnl`	Exploiting Parallelism Opportunities with Deep Learning Frameworks. ACM Trans. Archit. Code Optim. Yu Emma Wang, Carole-Jean Wu, Xiaodong Wang, Kim M. Hazelwood, David Brooks
2021	B	`conf`	HyFM: function merging for free. LCTES Rodrigo C. O. Rocha, Pavlos Petoumenos, Zheng Wang, Murray Cole, Kim M. Hazelwood, Hugh Leather
2021	J	`jnl`	Sustainable AI: Environmental Implications, Challenges and Opportunities. CoRR Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, James Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin C. Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim M. Hazelwood
2021	A*	`conf`	Understanding Training Efficiency of Deep Learning Recommendation Models at Scale. HPCA Bilge Acun, Matthew Murphy, Xiaodong Wang, Jade Nie, Carole-Jean Wu, Kim M. Hazelwood
2021	J	`jnl`	Using Python for Model Inference in Deep Learning. CoRR Zachary DeVito, Jason Ansel, Will Constable, Michael Suo, Ailing Zhang, Kim M. Hazelwood
2020	—	`conf`	MLPerf Training Benchmark. MLSys 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
2020	A*	`conf`	RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing. ISCA 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
2020	A*	`conf`	The Architectural Implications of Facebook's DNN-Based Personalized Recommendation. HPCA 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
2020	J	`jnl`	Understanding Training Efficiency of Deep Learning Recommendation Models at Scale. CoRR Bilge Acun, Matthew Murphy, Xiaodong Wang, Jade Nie, Carole-Jean Wu, Kim M. Hazelwood
2019	—	`conf`	Bandana: Using Non-Volatile Memory for Storing Deep Learning Models. SysML Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim M. Hazelwood, Asaf Cidon, Sachin Katti
2019	J	`jnl`	Exploiting Parallelism Opportunities with Deep Learning Frameworks. CoRR Yu Emma Wang, Carole-Jean Wu, Xiaodong Wang, Kim M. Hazelwood, David Brooks
2019	J	`jnl`	MLPerf Training Benchmark. CoRR 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
2019	A*	`conf`	Machine Learning at Facebook: Understanding Inference at the Edge. HPCA 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
2019	J	`jnl`	RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing. CoRR 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
2019	J	`jnl`	SysML: The New Frontier of Machine Learning Systems. CoRR Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. 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 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 Randall 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
2019	J	`jnl`	The Architectural Implications of Facebook's DNN-based Personalized Recommendation. CoRR 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
2018	A*	`conf`	Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective. HPCA 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
2018	J	`jnl`	Bandana: Using Non-volatile Memory for Storing Deep Learning Models. CoRR Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim M. Hazelwood, Asaf Cidon, Sachin Katti
2018	J	`jnl`	Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications. CoRR 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
2018	A	`conf`	Reducing DRAM footprint with NVM in facebook. EuroSys Assaf Eisenman, Darryl Gardner, Islam AbdelRahman, Jens Axboe, Siying Dong, Kim M. Hazelwood, Chris Petersen, Asaf Cidon, Sachin Katti
2016	J	`jnl`	Profiling a Warehouse-Scale Computer. IEEE Micro Svilen Kanev, Juan Pablo Darago, Kim M. Hazelwood, Parthasarathy Ranganathan, Tipp Moseley, Gu-Yeon Wei, David M. Brooks
2015	A*	`conf`	Profiling a warehouse-scale computer. ISCA Svilen Kanev, Juan Pablo Darago, Kim M. Hazelwood, Parthasarathy Ranganathan, Tipp Moseley, Gu-Yeon Wei, David M. Brooks
2014	—	`conf`	Tradeoffs between power management and tail latency in warehouse-scale applications. IISWC Svilen Kanev, Kim M. Hazelwood, Gu-Yeon Wei, David M. Brooks
2013	—	`ed.`	5th USENIX Workshop on Hot Topics in Parallelism, HotPar'13, San Jose, CA, USA, June 24-25, 2013 HotPar Emery D. Berger, Kim M. Hazelwood
2012	A	`conf`	EcoSim: a language and experience teaching parallel programming in elementary school. SIGCSE Chris Gregg, Luther A. Tychonievich, James P. Cohoon, Kim M. Hazelwood
2012	—	`conf`	Fine-Grained Resource Sharing for Concurrent GPGPU Kernels. HotPar Chris Gregg, Jonathan Dorn, Kim M. Hazelwood, Kevin Skadron
2012	J	`jnl`	Memory optimization of dynamic binary translators for embedded systems. ACM Trans. Archit. Code Optim. Apala Guha, Kim M. Hazelwood, Mary Lou Soffa
2011	—	`conf`	Analyzing program flow within a many-kernel OpenCL application. GPGPU Perhaad Mistry, Chris Gregg, Norman Rubin, David R. Kaeli, Kim M. Hazelwood
2011	—	`book`	Dynamic Binary Modification: Tools, Techniques, and Applications Dynamic Binary Modification Kim M. Hazelwood
2011	B	`conf`	Finding cool code: An analysis of source-level causes of temperature effects. ISPASS Dan Upton, Kim M. Hazelwood
2011	B	`conf`	Performance characterization of mobile-class nodes: Why fewer bits is better. ISPASS Michelle McDaniel, Kim M. Hazelwood
2011	A*	`conf`	Process-level virtualization for runtime adaptation of embedded software. DAC Kim M. Hazelwood
2011	B	`conf`	Where is the data? Why you cannot debate CPU vs. GPU performance without the answer. ISPASS Chris Gregg, Kim M. Hazelwood
2010	J	`jnl`	Analyzing Parallel Programs with Pin. Computer Moshe Bach, Mark Charney, Robert Cohn, Elena Demikhovsky, Tevi Devor, Kim M. Hazelwood, Aamer Jaleel, Chi-Keung Luk, Gail Lyons, Harish Patil, Ady Tal
2010	Misc	`conf`	Balancing memory and performance through selective flushing of software code caches. CASES Apala Guha, Kim M. Hazelwood, Mary Lou Soffa
2010	B	`conf`	DBT path selection for holistic memory efficiency and performance. VEE Apala Guha, Kim M. Hazelwood, Mary Lou Soffa
2010	—	`conf`	Design of a custom VEE core in a chip multiprocessor. SASP Dan Upton, Kim M. Hazelwood
2010	B	`conf`	Dynamic program analysis of Microsoft Windows applications. ISPASS Alex Skaletsky, Tevi Devor, Nadav Chachmon, Robert S. Cohn, Kim M. Hazelwood, Vladimir Vladimirov, Moshe Bach
2010	J	`jnl`	Eliminating voltage emergencies via software-guided code transformations. ACM Trans. Archit. Code Optim. Vijay Janapa Reddi, Simone Campanoni, Meeta Sharma Gupta, Michael D. Smith, Gu-Yeon Wei, David M. Brooks, Kim M. Hazelwood
2010	A	`ed.`	Proceedings of the CGO 2010, The 8th International Symposium on Code Generation and Optimization, Toronto, Ontario, Canada, April 24-28, 2010 CGO Andreas Moshovos, J. Gregory Steffan, Kim M. Hazelwood, David R. Kaeli
2009	C	`conf`	A cross-layer approach to heterogeneity and reliability. MEMOCODE Daniel W. Williams, Aprotim Sanyal, Dan Upton, Jason Mars, Sudeep Ghosh, Kim M. Hazelwood
2009	J	`jnl`	Challenges and opportunities at all levels: interactions among operating systems, compilers, and multicore processors. ACM SIGOPS Oper. Syst. Rev. Kim M. Hazelwood, Mohamed Zahran
2009	J	`jnl`	Multicore compilation strategies and challenges. IEEE Signal Process. Mag. Mojtaba Mehrara, Thomas B. Jablin, Dan Upton, David I. August, Kim M. Hazelwood, Scott A. Mahlke
2009	C	`conf`	Scalable support for multithreaded applications on dynamic binary instrumentation systems. ISMM Kim M. Hazelwood, Greg Lueck, Robert Cohn
2008	—	`conf`	Evaluating the impact of dynamic binary translation systems on hardware cache performance. IISWC Arkaitz Ruiz-Alvarez, Kim M. Hazelwood
2008	B	`conf`	Trace fragment selection within method-based JVMs. VEE Duane Merrill, Kim M. Hazelwood
2007	—	`conf`	Reducing Exit Stub Memory Consumption in Code Caches. HiPEAC Apala Guha, Kim M. Hazelwood, Mary Lou Soffa
2007	A	`conf`	SuperPin: Parallelizing Dynamic Instrumentation for Real-Time Performance. CGO Steven Wallace, Kim M. Hazelwood
2007	A	`conf`	Virtual Execution Environments: Support and Tools. IPDPS Apala Guha, Jason Hiser, Naveen Kumar, Jing Yang, Min Zhao, Shukang Zhou, Bruce R. Childers, Jack W. Davidson, Kim M. Hazelwood, Mary Lou Soffa
2006	A	`conf`	A Cross-Architectural Interface for Code Cache Manipulation. CGO Kim M. Hazelwood, Robert S. Cohn
2006	Misc	`conf`	A dynamic binary instrumentation engine for the ARM architecture. CASES Kim M. Hazelwood, Artur Klauser
2006	J	`jnl`	Managing bounded code caches in dynamic binary optimization systems. ACM Trans. Archit. Code Optim. Kim M. Hazelwood, Michael D. Smith
2005	A*	`conf`	Improving Region Selection in Dynamic Optimization Systems. MICRO David Hiniker, Kim M. Hazelwood, Michael D. Smith
2005	A*	`conf`	Pin: building customized program analysis tools with dynamic instrumentation. PLDI Chi-Keung Luk, Robert S. Cohn, Robert Muth, Harish Patil, Artur Klauser, P. Geoffrey Lowney, Steven Wallace, Vijay Janapa Reddi, Kim M. Hazelwood
2004	A	`conf`	Eliminating voltage emergencies via microarchitectural voltage control feedback and dynamic optimization. ISLPED Kim M. Hazelwood, David M. Brooks
2004	A	`conf`	Exploring Code Cache Eviction Granularities in Dynamic Optimization Systems. CGO Kim M. Hazelwood, James E. Smith
2003	A	`conf`	Adaptive Online Context-Sensitive Inlining. CGO Kim M. Hazelwood, David Grove
2003	A*	`conf`	Generational Cache Management of Code Traces in Dynamic Optimization Systems. MICRO Kim M. Hazelwood, Michael D. Smith
2002	—	`conf`	Code Cache Management Schemes for Dynamic Optimizers. Interaction between Compilers and Computer Architectures Kim M. Hazelwood, Michael D. Smith
2000	—	`conf`	A Lightweight Algorithm for Dynamic If-Conversion during Dynamic Optimization. IEEE PACT Kim M. Hazelwood, Thomas M. Conte