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With the end of Moore's Law, optimizing code for performance has become paramount for meeting ever-increasing compute demands, particularly in hyperscale data centers where even small efficiency gains translate to significant resource and energy savings.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
Earlier work this paper cites.
Llvm: a compilation framework for lifelong program analysis & transformation
C. Lattner and V. Adve · 2004
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
Earlier work this paper cites.
Using machine learning to focus iterative optimization
F. Agakov, E. Bonilla, J. Cavazos, B. Franke, G. Fursin, M.F.P. O’Boyle, J. Thomson, M. Toussaint, and C.K.I. Williams · 2006
Earlier work this paper cites.
Introduction to information retrieval
Hinrich Schütze, Christopher D Manning, and Prabhakar Raghavan · 2008
Earlier work this paper cites.
It and eco-sustainability: Developing and validating a green it readiness model
Alemayehu Molla, Vanessa A Cooper, and Siddhi Pittayachawan · 2009
Earlier work this paper cites.
Google-wide profiling: A continuous profiling infrastructure for data centers
Gang Ren, Eric Tune, Tipp Moseley, Yixin Shi, Silvius Rus, and Robert Hundt · 2010
Earlier work this paper cites.
Systematic editing: generating program transformations from an example
Na Meng, Miryung Kim, and Kathryn S. McKinley · 2011
Earlier work this paper cites.
Green cloud computing and environmental sustainability
Saurabh Kumar and Rajkumar Buyya · 2012
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Stochastic superoptimization
Eric Schkufza, Rahul Sharma, and Alex Aiken · 2013
Earlier work this paper cites.
A reconfigurable fabric for accelerating large-scale datacenter services
Andrew Putnam, Adrian M. Caulfield, Eric S. Chung, Derek Chiou, Kypros Constantinides, John Demme, Hadi Esmaeilzadeh, Jeremy Fowers, Gopi Prashanth Gopal, Jan Gray, Michael Haselman, Scott Hauck, Stephen Heil, Amir Hormati, Joo-Young Kim, Sitaram Lanka, James Larus, Eric Peterson, Simon Pope, Aaron Smith, Jason Thong, Phillip Yi Xiao, and Doug Burger · 2014
Earlier work this paper cites.
https://devblogs.microsoft.com/cppblog/speeding-up-the-incremental-developer-build-scenario/ , 2014
Speeding up the incremental developer build scenario · 2014
Earlier work this paper cites.
Profiling a warehouse-scale computer
Svilen Kanev, Juan Pablo Darago, Kim Hazelwood, Parthasarathy Ranganathan, Tipp Moseley, Gu-Yeon Wei, and David Brooks · 2015
Earlier work this paper cites.
Autofdo: Automatic feedback-directed optimization for warehouse-scale applications
Dehao Chen, David Xinliang Li, and Tipp Moseley · 2016
Earlier work this paper cites.
Why google stores billions of lines of code in a single repository
Rachel Potvin and Josh Levenberg · 2016
Earlier work this paper cites.
The flame graph
Brendan Gregg · 2016
Earlier work this paper cites.
Imbalance in the cloud: An analysis on alibaba cluster trace
Chengzhi Lu, Kejiang Ye, Guoyao Xu, Cheng-Zhong Xu, and Tongxin Bai · 2017
Earlier work this paper cites.
Resource central: Understanding and predicting workloads for improved resource management in large cloud platforms
Eli Cortez, Anand Bonde, Alexandre Muzio, Mark Russinovich, Marcus Fontoura, and Ricardo Bianchini · 2017
Earlier work this paper cites.
Learning to superoptimize programs, 2017
Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, and Pushmeet Kohli · 2017
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Analytics with smart arrays: adaptive and efficient language-independent data
Iraklis Psaroudakis, Stefan Kaestle, Matthias Grimmer, Daniel Goodman, Jean-Pierre Lozi, and Tim Harris · 2018
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Bolt: a practical binary optimizer for data centers and beyond
Maksim Panchenko, Rafael Auler, Bill Nell, and Guilherme Ottoni · 2019
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Ithemal: Accurate, portable and fast basic block throughput estimation using deep neural networks
Charith Mendis, Alex Renda, Saman Amarasinghe, and Michael Carbin · 2019
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On the fly synthesis of edit suggestions
Anders Miltner, Sumit Gulwani, Vu Le, Alan Leung, Arjun Radhakrishna, Gustavo Soares, Ashish Tiwari, and Abhishek Udupa · 2019
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Aroma: code recommendation via structural code search
Discovering faster matrix multiplication algorithms with reinforcement learning
Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Francisco J R Ruiz, Julian Schrittwieser, Grzegorz Swirszcz, et al · 2022
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Deepdev-perf: a deep learning-based approach for improving software performance
Spandan Garg, Roshanak Zilouchian Moghaddam, Colin B. Clement, Neel Sundaresan, and Chen Wu · 2022
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Cornflakes: Zero-copy serialization for microsecond-scale networking
Deepti Raghavan, Shreya Ravi, Gina Yuan, Pratiksha Thaker, Sanjari Srivastava, Micah Murray, Pedro Henrique Penna, Amy Ousterhout, Philip Levis, Matei Zaharia, and Irene Zhang · 2023
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Teaching large language models to self-debug, 2023
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou · 2023
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Gemini: a family of highly capable multimodal models
Gemini-Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
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Sifei Luan, Di Yang, Celeste Barnaby, Koushik Sen, and Satish Chandra · 2019
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There’s plenty of room at the top: What will drive computer performance after moore’s law?
Charles E. Leiserson, Neil C. Thompson, Joel S. Emer, Bradley C. Kuszmaul, Butler W. Lampson, Daniel Sanchez, and Tao B. Schardl · 2020
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Accelerating large-scale inference with anisotropic vector quantization
Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, and Sanjiv Kumar · 2020
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Codebleu: a method for automatic evaluation of code synthesis
Shuo Ren, Daya Guo, Shuai Lu, Long Zhou, Shujie Liu, Duyu Tang, Neel Sundaresan, Ming Zhou, Ambrosio Blanco, and Shuai Ma · 2020
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A hardware accelerator for protocol buffers
Sagar Karandikar, Chris Leary, Chris Kennelly, Jerry Zhao, Dinesh Parimi, Borivoje Nikolic, Krste Asanovic, and Parthasarathy Ranganathan · 2021
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Beyond malloc efficiency to fleet efficiency: a hugepage-aware memory allocator
A.H. Hunter, Chris Kennelly, Paul Turner, Darryl Gove, Tipp Moseley, and Parthasarathy Ranganathan · 2021
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A deep learning based cost model for automatic code optimization
Riyadh Baghdadi, Massinissa Merouani, Mohamed-Hicham Leghettas, Kamel Abdous, Taha Arbaoui, Karima Benatchba, and Saman amarasinghe · 2021
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Large sequence models for software development activities
Petros Maniatis and Daniel Tarlow · 2023
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Retrieval-based prompt selection for code-related few-shot learning
Noor Nashid, Mifta Sintaha, and Ali Mesbah · 2023
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Faster sorting algorithms discovered using deep reinforcement learning
Daniel J Mankowitz, Andrea Michi, Anton Zhernov, Marco Gelmi, Marco Selvi, Cosmin Paduraru, Edouard Leurent, Shariq Iqbal, Jean-Baptiste Lespiau, Alex Ahern, et al · 2023
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Triangulating python performance issues with Scalene
Emery D. Berger, Sam Stern, and Juan Altmayer Pizzorno · 2023
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Supersonic: Learning to generate source code optimizations in c/c++, 2023
Zimin Chen, Sen Fang, and Martin Monperrus · 2023
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https://aws.amazon.com/ec2/nitro/ , 2024
Aws nitro system · 2024
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Learning performance-improving code edits
Alexander G Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob R. Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, and Amir Yazdanbakhsh · 2024
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Codeplan: Repository-level coding using llms and planning
Ramakrishna Bairi, Atharv Sonwane, Aditya Kanade, Vageesh D. C., Arun Iyer, Suresh Parthasarathy, Sriram Rajamani, B. Ashok, and Shashank Shet · 2024
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https://cloud.google.com/vertex-ai/generative-ai/docs/models/tune-gemini-overview , 2024
Generative ai on vertex ai: Overview of model tuning for gemini · 2024
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Code llama: Open foundation models for code, 2024
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Romain Sauvestre, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, and Gabriel Synnaeve · 2024
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Large language models for software engineering: A systematic literature review
Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, and Haoyu Wang · 2024
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Structured chain-of-thought prompting for code generation
Jia Li, Ge Li, Yongmin Li, and Zhi Jin · 2024
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