Kim M. Hazelwood

According to our database, Kim M. Hazelwood authored at least 64 papers between 2000 and 2024.

Collaborative distances:

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

2024

Revealing Compiler Heuristics Through Automated Discovery and Optimization.

Volker Seeker, Chris Cummins, Murray Cole, Björn Franke, Kim M. Hazelwood, Hugh Leather

Proceedings of the IEEE/ACM International Symposium on Code Generation and Optimization, 2024

2023

Large Language Models for Compiler Optimization.

Chris Cummins, Volker Seeker, Dejan Grubisic, Mostafa Elhoushi, Youwei Liang, Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Kim M. Hazelwood, Gabriel Synnaeve, Hugh Leather

CoRR, 2023

BenchDirect: A Directed Language Model for Compiler Benchmarks.

Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim M. Hazelwood, Ajitha Rajan, Hugh Leather

CoRR, 2023

2022

Object Intersection Captures on Interactive Apps to Drive a Crowd-sourced Replay-based Compiler Optimization.

Paschalis Mpeis, Pavlos Petoumenos, Kim M. Hazelwood, Hugh Leather

ACM Trans. Archit. Code Optim., 2022

Sustainable AI: Environmental Implications, Challenges and Opportunities.

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

Proceedings of Machine Learning and Systems 2022, 2022

F3M: Fast Focused Function Merging.

Sean Stirling, Rodrigo C. O. Rocha, Kim M. Hazelwood, Hugh Leather, Michael F. P. O'Boyle, Pavlos Petoumenos

Proceedings of the IEEE/ACM International Symposium on Code Generation and Optimization, 2022

Caviar: an e-graph based TRS for automatic code optimization.

Smail Kourta, Adel Abderahmane Namani, Fatima Benbouzid-Si Tayeb, Kim M. Hazelwood, Chris Cummins, Hugh Leather, Riyadh Baghdadi

Proceedings of the CC '22: 31st ACM SIGPLAN International Conference on Compiler Construction, Seoul, South Korea, April 2, 2022

BenchPress: A Deep Active Benchmark Generator.

Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim M. Hazelwood, Ajitha Rajan, Hugh Leather

Proceedings of the International Conference on Parallel Architectures and Compilation Techniques, 2022

Q-gym: An Equality Saturation Framework for DNN Inference Exploiting Weight Repetition.

Cheng Fu, Hanxian Huang, Bram Wasti, Chris Cummins, Riyadh Baghdadi, Kim M. Hazelwood, Yuandong Tian, Jishen Zhao, Hugh Leather

Proceedings of the International Conference on Parallel Architectures and Compilation Techniques, 2022

2021

Exploiting Parallelism Opportunities with Deep Learning Frameworks.

Yu Emma Wang, Carole-Jean Wu, Xiaodong Wang, Kim M. Hazelwood, David Brooks

ACM Trans. Archit. Code Optim., 2021

Datacenter-Scale Analysis and Optimization of GPU Machine Learning Workloads.

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

IEEE Micro, 2021

Sustainable AI: Environmental Implications, Challenges and Opportunities.

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

CoRR, 2021

Using Python for Model Inference in Deep Learning.

Zachary DeVito, Jason Ansel, Will Constable, Michael Suo, Ailing Zhang, Kim M. Hazelwood

CoRR, 2021

Developer and user-transparent compiler optimization for interactive applications.

Paschalis Mpeis, Pavlos Petoumenos, Kim M. Hazelwood, Hugh Leather

Proceedings of the PLDI '21: 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, 2021

HyFM: function merging for free.

Rodrigo C. O. Rocha, Pavlos Petoumenos, Zheng Wang, Murray Cole, Kim M. Hazelwood, Hugh Leather

Proceedings of the LCTES '21: 22nd ACM SIGPLAN/SIGBED International Conference on Languages, 2021

Understanding Training Efficiency of Deep Learning Recommendation Models at Scale.

Bilge Acun, Matthew Murphy, Xiaodong Wang, Jade Nie, Carole-Jean Wu, Kim M. Hazelwood

Proceedings of the IEEE International Symposium on High-Performance Computer Architecture, 2021

2020

MLPerf Training Benchmark.

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

Proceedings of Machine Learning and Systems 2020, 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

MLPerf Training Benchmark.

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

CoRR, 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

SysML: The New Frontier of Machine Learning Systems.

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 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

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 Machine Learning and Systems 2019, 2019

Machine Learning at Facebook: Understanding Inference at the Edge.

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

Proceedings of the 25th IEEE International Symposium on High Performance Computer Architecture, 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

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

Reducing DRAM footprint with NVM in facebook.

Assaf Eisenman, Darryl Gardner, Islam AbdelRahman, Jens Axboe, Siying Dong, Kim M. Hazelwood, Chris Petersen, Asaf Cidon, Sachin Katti

Proceedings of the Thirteenth EuroSys Conference, 2018

2016

Profiling a Warehouse-Scale Computer.

Svilen Kanev, Juan Pablo Darago, Kim M. Hazelwood, Parthasarathy Ranganathan, Tipp Moseley, Gu-Yeon Wei, David M. Brooks

IEEE Micro, 2016

2014

Tradeoffs between power management and tail latency in warehouse-scale applications.

Svilen Kanev, Kim M. Hazelwood, Gu-Yeon Wei, David M. Brooks

Proceedings of the 2014 IEEE International Symposium on Workload Characterization, 2014

2012

Memory optimization of dynamic binary translators for embedded systems.

Apala Guha, Kim M. Hazelwood, Mary Lou Soffa

ACM Trans. Archit. Code Optim., 2012

EcoSim: a language and experience teaching parallel programming in elementary school.

Chris Gregg, Luther A. Tychonievich, James P. Cohoon, Kim M. Hazelwood

Proceedings of the 43rd ACM technical symposium on Computer science education, 2012

Fine-Grained Resource Sharing for Concurrent GPGPU Kernels.

Chris Gregg, Jonathan Dorn, Kim M. Hazelwood, Kevin Skadron

Proceedings of the 4th USENIX Workshop on Hot Topics in Parallelism, 2012

2011

Dynamic Binary Modification: Tools, Techniques, and Applications

Kim M. Hazelwood

Synthesis Lectures on Computer Architecture, Morgan & Claypool Publishers, ISBN: 978-3-031-01732-2, 2011

Finding cool code: An analysis of source-level causes of temperature effects.

Dan Upton, Kim M. Hazelwood

Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, 2011

Performance characterization of mobile-class nodes: Why fewer bits is better.

Michelle McDaniel, Kim M. Hazelwood

Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, 2011

Where is the data? Why you cannot debate CPU vs. GPU performance without the answer.

Chris Gregg, Kim M. Hazelwood

Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, 2011

Process-level virtualization for runtime adaptation of embedded software.

Kim M. Hazelwood

Proceedings of the 48th Design Automation Conference, 2011

Analyzing program flow within a many-kernel OpenCL application.

Perhaad Mistry, Chris Gregg, Norman Rubin, David R. Kaeli, Kim M. Hazelwood

Proceedings of 4th Workshop on General Purpose Processing on Graphics Processing Units, 2011

2010

Eliminating voltage emergencies via software-guided code transformations.

Vijay Janapa Reddi, Simone Campanoni, Meeta Sharma Gupta, Michael D. Smith, Gu-Yeon Wei, David M. Brooks, Kim M. Hazelwood

ACM Trans. Archit. Code Optim., 2010

Analyzing Parallel Programs with Pin.

Moshe Bach, Mark Charney, Robert Cohn, Elena Demikhovsky, Tevi Devor, Kim M. Hazelwood, Aamer Jaleel, Chi-Keung Luk, Gail Lyons, Harish Patil, Ady Tal

Computer, 2010

DBT path selection for holistic memory efficiency and performance.

Apala Guha, Kim M. Hazelwood, Mary Lou Soffa

Proceedings of the 6th International Conference on Virtual Execution Environments, 2010

Design of a custom VEE core in a chip multiprocessor.

Dan Upton, Kim M. Hazelwood

Proceedings of the IEEE 8th Symposium on Application Specific Processors, 2010

Dynamic program analysis of Microsoft Windows applications.

Alex Skaletsky, Tevi Devor, Nadav Chachmon, Robert S. Cohn, Kim M. Hazelwood, Vladimir Vladimirov, Moshe Bach

Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, 2010

Balancing memory and performance through selective flushing of software code caches.

Apala Guha, Kim M. Hazelwood, Mary Lou Soffa

Proceedings of the 2010 International Conference on Compilers, 2010

2009

Multicore compilation strategies and challenges.

Mojtaba Mehrara, Thomas B. Jablin, Dan Upton, David I. August, Kim M. Hazelwood, Scott A. Mahlke

IEEE Signal Process. Mag., 2009

Challenges and opportunities at all levels: interactions among operating systems, compilers, and multicore processors.

Kim M. Hazelwood, Mohamed Zahran

ACM SIGOPS Oper. Syst. Rev., 2009

A cross-layer approach to heterogeneity and reliability.

Daniel W. Williams, Aprotim Sanyal, Dan Upton, Jason Mars, Sudeep Ghosh, Kim M. Hazelwood

Proceedings of the 7th ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE 2009), 2009

Scalable support for multithreaded applications on dynamic binary instrumentation systems.

Kim M. Hazelwood, Greg Lueck, Robert Cohn

Proceedings of the 8th International Symposium on Memory Management, 2009

2008

Trace fragment selection within method-based JVMs.

Duane Merrill, Kim M. Hazelwood

Proceedings of the 4th International Conference on Virtual Execution Environments, 2008

Evaluating the impact of dynamic binary translation systems on hardware cache performance.

Arkaitz Ruiz-Alvarez, Kim M. Hazelwood

Proceedings of the 4th International Symposium on Workload Characterization (IISWC 2008), 2008

2007

Virtual Execution Environments: Support and Tools.

Apala Guha, Jason Hiser, Naveen Kumar, Jing Yang, Min Zhao, Shukang Zhou, Bruce R. Childers, Jack W. Davidson, Kim M. Hazelwood, Mary Lou Soffa

Proceedings of the 21th International Parallel and Distributed Processing Symposium (IPDPS 2007), 2007

Reducing Exit Stub Memory Consumption in Code Caches.

Apala Guha, Kim M. Hazelwood, Mary Lou Soffa

Proceedings of the High Performance Embedded Architectures and Compilers, 2007

SuperPin: Parallelizing Dynamic Instrumentation for Real-Time Performance.

Steven Wallace, Kim M. Hazelwood

Proceedings of the Fifth International Symposium on Code Generation and Optimization (CGO 2007), 2007

2006

Managing bounded code caches in dynamic binary optimization systems.

Kim M. Hazelwood, Michael D. Smith

ACM Trans. Archit. Code Optim., 2006

A Cross-Architectural Interface for Code Cache Manipulation.

Kim M. Hazelwood, Robert S. Cohn

Proceedings of the Fourth IEEE/ACM International Symposium on Code Generation and Optimization (CGO 2006), 2006

A dynamic binary instrumentation engine for the ARM architecture.

Kim M. Hazelwood, Artur Klauser

Proceedings of the 2006 International Conference on Compilers, 2006

2005

Pin: building customized program analysis tools with dynamic instrumentation.

Chi-Keung Luk, Robert S. Cohn, Robert Muth, Harish Patil, Artur Klauser, P. Geoffrey Lowney, Steven Wallace, Vijay Janapa Reddi, Kim M. Hazelwood

Proceedings of the ACM SIGPLAN 2005 Conference on Programming Language Design and Implementation, 2005

Improving Region Selection in Dynamic Optimization Systems.

David Hiniker, Kim M. Hazelwood, Michael D. Smith

Proceedings of the 38th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO-38 2005), 2005

2004

Eliminating voltage emergencies via microarchitectural voltage control feedback and dynamic optimization.

Kim M. Hazelwood, David M. Brooks

Proceedings of the 2004 International Symposium on Low Power Electronics and Design, 2004

Exploring Code Cache Eviction Granularities in Dynamic Optimization Systems.

Kim M. Hazelwood, James E. Smith

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

2003

Generational Cache Management of Code Traces in Dynamic Optimization Systems.

Kim M. Hazelwood, Michael D. Smith

Proceedings of the 36th Annual International Symposium on Microarchitecture, 2003

Adaptive Online Context-Sensitive Inlining.

Kim M. Hazelwood, David Grove

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

2002

Code Cache Management Schemes for Dynamic Optimizers.

Kim M. Hazelwood, Michael D. Smith

Proceedings of the 6th Annual Workshop on Interaction between Compilers and Computer Architecture (INTERACT-6 2002), 2002

2000

A Lightweight Algorithm for Dynamic If-Conversion during Dynamic Optimization.

Kim M. Hazelwood, Thomas M. Conte

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