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ORCIDKim M. Hazelwood
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2020 – today
- 2024
[j11]Carole-Jean Wu, Bilge Acun, Ramya Raghavendra, Kim M. Hazelwood:
Beyond Efficiency: Scaling AI Sustainably. IEEE Micro 44(5): 37-46 (2024)- [c46]Volker Seeker, Chris Cummins, Murray Cole, Björn Franke, Kim M. Hazelwood, Hugh Leather:
Revealing Compiler Heuristics Through Automated Discovery and Optimization. CGO 2024: 55-66 - [i15]Carole-Jean Wu, Bilge Acun, Ramya Raghavendra, Kim M. Hazelwood:
Beyond Efficiency: Scaling AI Sustainably. CoRR abs/2406.05303 (2024) - 2023
- [i14]Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim M. Hazelwood, Ajitha Rajan, Hugh Leather:
BenchDirect: A Directed Language Model for Compiler Benchmarks. CoRR abs/2303.01557 (2023) - [i13]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 abs/2309.07062 (2023) - 2022
- [j10]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): 32:1-32:25 (2022) - [c45]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: 291-303 - [c44]Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim M. Hazelwood, Ajitha Rajan, Hugh Leather:
BenchPress: A Deep Active Benchmark Generator. PACT 2022: 505-516 - [c43]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: 54-64 - [c42]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: 242-253 - [c41]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 - [i12]Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim M. Hazelwood, Ajitha Rajan, Hugh Leather:
BenchPress: A Deep Active Benchmark Generator. CoRR abs/2208.06555 (2022) - 2021
- [j9]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:
Datacenter-Scale Analysis and Optimization of GPU Machine Learning Workloads. IEEE Micro 41(5): 101-112 (2021) - [j8]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): 9:1-9:23 (2021) - [c40]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: 802-814 - [c39]Rodrigo C. O. Rocha, Pavlos Petoumenos, Zheng Wang, Murray Cole, Kim M. Hazelwood, Hugh Leather:
HyFM: function merging for free. LCTES 2021: 110-121 - [c38]Paschalis Mpeis, Pavlos Petoumenos, Kim M. Hazelwood, Hugh Leather:
Developer and user-transparent compiler optimization for interactive applications. PLDI 2021: 268-281 - [i11]Zachary DeVito, Jason Ansel, Will Constable, Michael Suo, Ailing Zhang, Kim M. Hazelwood:
Using Python for Model Inference in Deep Learning. CoRR abs/2104.00254 (2021) - [i10]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 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. CoRR abs/2111.00364 (2021) - [i9]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 abs/2111.12116 (2021) - 2020
- [c37]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: 488-501 - [c36]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: 790-803 - [c35]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 - [i8]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 abs/2011.05497 (2020)
2010 – 2019
- 2019
- [c34]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: 331-344 - [c33]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. SysML 2019 - [i7]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 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 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:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i6]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 abs/1906.03109 (2019) - [i5]Yu Emma Wang, Carole-Jean Wu, Xiaodong Wang, Kim M. Hazelwood, David Brooks:
Exploiting Parallelism Opportunities with Deep Learning Frameworks. CoRR abs/1908.04705 (2019) - [i4]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 abs/1910.01500 (2019) - [i3]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 abs/1912.12953 (2019) - 2018
- [c32]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: 42:1-42:13 - [c31]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: 620-629 - [i2]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 abs/1811.05922 (2018) - [i1]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 abs/1811.09886 (2018) - 2016
- [j7]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): 54-59 (2016) - 2015
- [c30]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: 158-169 - 2014
- [c29]Svilen Kanev, Kim M. Hazelwood, Gu-Yeon Wei, David M. Brooks:
Tradeoffs between power management and tail latency in warehouse-scale applications. IISWC 2014: 31-40 - 2013
- [e2]Emery D. Berger, Kim M. Hazelwood:
5th USENIX Workshop on Hot Topics in Parallelism, HotPar'13, San Jose, CA, USA, June 24-25, 2013. USENIX Association 2013 [contents] - 2012
- [j6]Apala Guha, Kim M. Hazelwood, Mary Lou Soffa:
Memory optimization of dynamic binary translators for embedded systems. ACM Trans. Archit. Code Optim. 9(3): 22:1-22:29 (2012) - [c28]Chris Gregg, Jonathan Dorn, Kim M. Hazelwood, Kevin Skadron:
Fine-Grained Resource Sharing for Concurrent GPGPU Kernels. HotPar 2012 - [c27]Chris Gregg, Luther A. Tychonievich, James P. Cohoon, Kim M. Hazelwood:
EcoSim: a language and experience teaching parallel programming in elementary school. SIGCSE 2012: 51-56 - 2011
- [b1]Kim M. Hazelwood:
Dynamic Binary Modification: Tools, Techniques, and Applications. Synthesis Lectures on Computer Architecture, Morgan & Claypool Publishers 2011, ISBN 978-3-031-00604-3 - [c26]Perhaad Mistry, Chris Gregg, Norman Rubin, David R. Kaeli, Kim M. Hazelwood:
Analyzing program flow within a many-kernel OpenCL application. GPGPU 2011: 10 - [c25]Kim M. Hazelwood:
Process-level virtualization for runtime adaptation of embedded software. DAC 2011: 895-900 - [c24]Dan Upton, Kim M. Hazelwood:
Finding cool code: An analysis of source-level causes of temperature effects. ISPASS 2011: 117-118 - [c23]Michelle McDaniel, Kim M. Hazelwood:
Performance characterization of mobile-class nodes: Why fewer bits is better. ISPASS 2011: 131-132 - [c22]Chris Gregg, Kim M. Hazelwood:
Where is the data? Why you cannot debate CPU vs. GPU performance without the answer. ISPASS 2011: 134-144 - 2010
- [j5]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): 34-41 (2010) - [j4]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): 12:1-12:28 (2010) - [c21]Apala Guha, Kim M. Hazelwood, Mary Lou Soffa:
Balancing memory and performance through selective flushing of software code caches. CASES 2010: 1-10 - [c20]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: 2-12 - [c19]Dan Upton, Kim M. Hazelwood:
Design of a custom VEE core in a chip multiprocessor. SASP 2010: 97-100 - [c18]Apala Guha, Kim M. Hazelwood, Mary Lou Soffa:
DBT path selection for holistic memory efficiency and performance. VEE 2010: 145-156 - [e1]Andreas Moshovos, J. Gregory Steffan, Kim M. Hazelwood, David R. Kaeli:
Proceedings of the CGO 2010, The 8th International Symposium on Code Generation and Optimization, Toronto, Ontario, Canada, April 24-28, 2010. ACM 2010, ISBN 978-1-60558-635-9 [contents]
2000 – 2009
- 2009
- [j3]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): 3-4 (2009) - [j2]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): 55-63 (2009) - [c17]Kim M. Hazelwood, Greg Lueck, Robert Cohn:
Scalable support for multithreaded applications on dynamic binary instrumentation systems. ISMM 2009: 20-29 - [c16]Daniel W. Williams, Aprotim Sanyal, Dan Upton, Jason Mars, Sudeep Ghosh, Kim M. Hazelwood:
A cross-layer approach to heterogeneity and reliability. MEMOCODE 2009: 88-97 - 2008
- [c15]Arkaitz Ruiz-Alvarez, Kim M. Hazelwood:
Evaluating the impact of dynamic binary translation systems on hardware cache performance. IISWC 2008: 131-140 - [c14]Duane Merrill, Kim M. Hazelwood:
Trace fragment selection within method-based JVMs. VEE 2008: 41-50 - 2007
- [c13]Steven Wallace, Kim M. Hazelwood:
SuperPin: Parallelizing Dynamic Instrumentation for Real-Time Performance. CGO 2007: 209-220 - [c12]Apala Guha, Kim M. Hazelwood, Mary Lou Soffa:
Reducing Exit Stub Memory Consumption in Code Caches. HiPEAC 2007: 87-101 - [c11]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: 1-6 - 2006
- [j1]Kim M. Hazelwood, Michael D. Smith:
Managing bounded code caches in dynamic binary optimization systems. ACM Trans. Archit. Code Optim. 3(3): 263-294 (2006) - [c10]Kim M. Hazelwood, Artur Klauser:
A dynamic binary instrumentation engine for the ARM architecture. CASES 2006: 261-270 - [c9]Kim M. Hazelwood, Robert S. Cohn:
A Cross-Architectural Interface for Code Cache Manipulation. CGO 2006: 17-27 - 2005
- [c8]David Hiniker, Kim M. Hazelwood, Michael D. Smith:
Improving Region Selection in Dynamic Optimization Systems. MICRO 2005: 141-154 - [c7]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: 190-200 - 2004
- [c6]Kim M. Hazelwood, James E. Smith:
Exploring Code Cache Eviction Granularities in Dynamic Optimization Systems. CGO 2004: 89-99 - [c5]Kim M. Hazelwood, David M. Brooks:
Eliminating voltage emergencies via microarchitectural voltage control feedback and dynamic optimization. ISLPED 2004: 326-331 - 2003
- [c4]Kim M. Hazelwood, David Grove:
Adaptive Online Context-Sensitive Inlining. CGO 2003: 253-264 - [c3]Kim M. Hazelwood, Michael D. Smith:
Generational Cache Management of Code Traces in Dynamic Optimization Systems. MICRO 2003: 169-179 - 2002
- [c2]Kim M. Hazelwood, Michael D. Smith:
Code Cache Management Schemes for Dynamic Optimizers. Interaction between Compilers and Computer Architectures 2002: 102-110 - 2000
- [c1]Kim M. Hazelwood, Thomas M. Conte:
A Lightweight Algorithm for Dynamic If-Conversion during Dynamic Optimization. IEEE PACT 2000: 71-80
Coauthor Index
David Brooks 0001
aka: David M. Brooks
aka: David M. Brooks
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