A Lightweight Algorithm for Dynamic If-Conversion during Dynamic Optimization. - Kim M. Hazelwood; Thomas M. Conte (IEEE PACT, 2000)

Code Cache Management Schemes for Dynamic Optimizers. - Kim M. Hazelwood; Michael D. Smith 001 (Interaction between Compilers and Computer Architectures, 2002)

Adaptive Online Context-Sensitive Inlining. - Kim M. Hazelwood; David Grove (CGO, 2003)

Generational Cache Management of Code Traces in Dynamic Optimization Systems. - Kim M. Hazelwood; Michael D. Smith 001 (MICRO, 2003)

Exploring Code Cache Eviction Granularities in Dynamic Optimization Systems. - Kim M. Hazelwood; James E. Smith (CGO, 2004)

Eliminating voltage emergencies via microarchitectural voltage control feedback and dynamic optimization. - Kim M. Hazelwood; David M. Brooks (ISLPED, 2004)

Improving Region Selection in Dynamic Optimization Systems. - David Hiniker; Kim M. Hazelwood; Michael D. Smith 001 (MICRO, 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 (PLDI, 2005)

A dynamic binary instrumentation engine for the ARM architecture. - Kim M. Hazelwood; Artur Klauser (CASES, 2006)

A Cross-Architectural Interface for Code Cache Manipulation. - Kim M. Hazelwood; Robert S. Cohn (CGO, 2006)

Managing bounded code caches in dynamic binary optimization systems. - Kim M. Hazelwood; Michael D. Smith 001 (ACM Trans. Archit. Code Optim., 2006)

SuperPin: Parallelizing Dynamic Instrumentation for Real-Time Performance. - Steven Wallace; Kim M. Hazelwood (CGO, 2007)

Reducing Exit Stub Memory Consumption in Code Caches. - Apala Guha; Kim M. Hazelwood; Mary Lou Soffa (HiPEAC, 2007)

Virtual Execution Environments: Support and Tools. - Apala Guha; Jason Hiser; Naveen Kumar 002; Jing Yang 003; Min Zhao 009; Shukang Zhou; Bruce R. Childers; Jack W. Davidson; Kim M. Hazelwood; Mary Lou Soffa (IPDPS, 2007)

Evaluating the impact of dynamic binary translation systems on hardware cache performance. - Arkaitz Ruiz-Alvarez; Kim M. Hazelwood (IISWC, 2008)

Trace fragment selection within method-based JVMs. - Duane Merrill; Kim M. Hazelwood (VEE, 2008)

Scalable support for multithreaded applications on dynamic binary instrumentation systems. - Kim M. Hazelwood; Greg Lueck; Robert Cohn (ISMM, 2009)

A cross-layer approach to heterogeneity and reliability. - Daniel W. Williams; Aprotim Sanyal; Dan Upton; Jason Mars; Sudeep Ghosh; Kim M. Hazelwood (MEMOCODE, 2009)

Challenges and opportunities at all levels: interactions among operating systems, compilers, and multicore processors. - Kim M. Hazelwood; Mohamed Zahran 001 (ACM SIGOPS Oper. Syst. Rev., 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)

Balancing memory and performance through selective flushing of software code caches. - Apala Guha; Kim M. Hazelwood; Mary Lou Soffa (CASES, 2010)

Dynamic program analysis of Microsoft Windows applications. - Alex Skaletsky; Tevi Devor; Nadav Chachmon; Robert S. Cohn; Kim M. Hazelwood; Vladimir Vladimirov; Moshe Bach (ISPASS, 2010)

Design of a custom VEE core in a chip multiprocessor. - Dan Upton; Kim M. Hazelwood (SASP, 2010)

DBT path selection for holistic memory efficiency and performance. - Apala Guha; Kim M. Hazelwood; Mary Lou Soffa (VEE, 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)

Eliminating voltage emergencies via software-guided code transformations. - Vijay Janapa Reddi; Simone Campanoni; Meeta Sharma Gupta; Michael D. Smith 001; Gu-Yeon Wei; David M. Brooks; Kim M. Hazelwood (ACM Trans. Archit. Code Optim., 2010)

Analyzing program flow within a many-kernel OpenCL application. - Perhaad Mistry; Chris Gregg; Norman Rubin; David R. Kaeli; Kim M. Hazelwood (GPGPU, 2011)

Process-level virtualization for runtime adaptation of embedded software. - Kim M. Hazelwood (DAC, 2011)

Where is the data? Why you cannot debate CPU vs. GPU performance without the answer. - Chris Gregg; Kim M. Hazelwood (ISPASS, 2011)

Performance characterization of mobile-class nodes: Why fewer bits is better. - Michelle McDaniel; Kim M. Hazelwood (ISPASS, 2011)

Finding cool code: An analysis of source-level causes of temperature effects. - Dan Upton; Kim M. Hazelwood (ISPASS, 2011)

Dynamic Binary Modification: Tools, Techniques, and Applications - Kim M. Hazelwood (Dynamic Binary Modification, 2011)

Fine-Grained Resource Sharing for Concurrent GPGPU Kernels. - Chris Gregg; Jonathan Dorn; Kim M. Hazelwood; Kevin Skadron (HotPar, 2012)

EcoSim: a language and experience teaching parallel programming in elementary school. - Chris Gregg; Luther A. Tychonievich; James P. Cohoon; Kim M. Hazelwood (SIGCSE, 2012)

Memory optimization of dynamic binary translators for embedded systems. - Apala Guha; Kim M. Hazelwood; Mary Lou Soffa (ACM Trans. Archit. Code Optim., 2012)

Tradeoffs between power management and tail latency in warehouse-scale applications. - Svilen Kanev; Kim M. Hazelwood; Gu-Yeon Wei; David M. Brooks (IISWC, 2014)

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)

Reducing DRAM footprint with NVM in facebook. - Assaf Eisenman; Darryl Gardner; Islam AbdelRahman; Jens Axboe; Siying Dong; Kim M. Hazelwood; Chris Petersen 002; Asaf Cidon; Sachin Katti (EuroSys, 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; et al. (HPCA, 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)

Machine Learning at Facebook: Understanding Inference at the Edge. - Carole-Jean Wu; David Brooks 001; Kevin Chen; Douglas Chen; Sy Choudhury; Marat Dukhan; Kim M. Hazelwood; Eldad Isaac; Yangqing Jia; Bill Jia; Tommer Leyvand; Hao Lu; et al. (HPCA, 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)

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; 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)

MLPerf Training Benchmark. - Peter Mattson; Christine Cheng; Cody Coleman; Greg Diamos; Paulius Micikevicius; David A. Patterson 001; Hanlin Tang; Gu-Yeon Wei; Peter Bailis; Victor Bittorf; David Brooks 001; Dehao Chen; et al. (CoRR, 2019)

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)

MLPerf Training Benchmark. - Peter Mattson; Christine Cheng; Gregory F. Diamos; Cody Coleman; Paulius Micikevicius; David A. Patterson 001; Hanlin Tang; Gu-Yeon Wei; Peter Bailis; Victor Bittorf; David Brooks 001; Dehao Chen; et al. (MLSys, 2020)

Understanding Training Efficiency of Deep Learning Recommendation Models at Scale. - Bilge Acun; Matthew Murphy; Xiaodong Wang 020; Jade Nie; Carole-Jean Wu; Kim M. Hazelwood (HPCA, 2021)

HyFM: function merging for free. - Rodrigo C. O. Rocha; Pavlos Petoumenos; Zheng Wang 001; Murray Cole; Kim M. Hazelwood; Hugh Leather (LCTES, 2021)

Developer and user-transparent compiler optimization for interactive applications. - Paschalis Mpeis; Pavlos Petoumenos; Kim M. Hazelwood; Hugh Leather (PLDI, 2021)

Using Python for Model Inference in Deep Learning. - Zachary DeVito; Jason Ansel; Will Constable; Michael Suo; Ailing Zhang; Kim M. Hazelwood (CoRR, 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; et al. (CoRR, 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; et al. (IEEE Micro, 2021)

Exploiting Parallelism Opportunities with Deep Learning Frameworks. - Yu Emma Wang; Carole-Jean Wu; Xiaodong Wang 020; Kim M. Hazelwood; David Brooks 001 (ACM Trans. Archit. Code Optim., 2021)

Q-gym: An Equality Saturation Framework for DNN Inference Exploiting Weight Repetition. - Cheng Fu 002; Hanxian Huang; Bram Wasti; Chris Cummins; Riyadh Baghdadi; Kim M. Hazelwood; Yuandong Tian; Jishen Zhao; Hugh Leather (PACT, 2022)

BenchPress: A Deep Active Benchmark Generator. - Foivos Tsimpourlas; Pavlos Petoumenos; Min Xu; Chris Cummins; Kim M. Hazelwood; Ajitha Rajan; Hugh Leather (PACT, 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 (CC, 2022)

F3M: Fast Focused Function Merging. - Sean Stirling; Rodrigo C. O. Rocha; Kim M. Hazelwood; Hugh Leather; Michael F. P. O'Boyle; Pavlos Petoumenos (CGO, 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; et al. (MLSys, 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)

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)

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)

Revealing Compiler Heuristics Through Automated Discovery and Optimization. - Volker Seeker; Chris Cummins; Murray Cole; Björn Franke; Kim M. Hazelwood; Hugh Leather (CGO, 2024)

Beyond Efficiency: Scaling AI Sustainably. - Carole-Jean Wu; Bilge Acun; Ramya Raghavendra; Kim M. Hazelwood (IEEE Micro, 2024)