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With the decline of Moore's law, optimizing program performance has become a major focus of software research.
Compiler Transformations for High-Performance Computing
David F Bacon, Susan L Graham, and Oliver J Sharp · 1994
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A Survey of Software Refactoring
Tom Mens and Tom Tourwé · 2004
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Auto-vectorization of Interleaved Data for SIMD
Dorit Nuzman, Ira Rosen, and Ayal Zaks · 2006
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Compilers: Principles, Techniques, and Tools , volume 2
Alfred V Aho, Ravi Sethi, and Jeffrey D Ullman · 2007
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The gem5 Simulator
Nathan Binkert, Bradford Beckmann, Gabriel Black, Steven K. Reinhardt, Ali Saidi, Arkaprava Basu, Joel Hestness, Derek R. Hower, Tushar Krishna, Somayeh Sardashti, Rathijit Sen, Korey Sewell, Muhammad Shoaib, Nilay Vaish, Mark D. Hill, and David A. Wood · 2011
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Combinatorial Search: From Algorithms to Systems
Youssef Hamadi · 2013
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Algorithm Selection for Combinatorial Search Problems: A Survey
Lars Kotthoff · 2016
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Decoupled Weight Decay Regularization
Ilya Loshchilov and Frank Hutter · 2017
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A Survey of Automatic Parameter Tuning Methods for Metaheuristics
Changwu Huang, Yuanxiang Li, and Xin Yao · 2019
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Billion-Scale Similarity Search with GPUs
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2019
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Automated Algorithm Selection: Survey and Perspectives
Pascal Kerschke, Holger H Hoos, Frank Neumann, and Heike Trautmann · 2019
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Compiler Auto-Vectorization with Imitation Learning
Charith Mendis, Cambridge Yang, Yewen Pu, Dr Amarasinghe, Michael Carbin, et al · 2019
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Deep Symbolic Superoptimization without Human Knowledge
Hui Shi, Yang Zhang, Xinyun Chen, Yuandong Tian, and Jishen Zhao · 2019
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A Systematic Literature Survey of Software Metrics, Code Smells and Refactoring Techniques
Mansi Agnihotri and Anuradha Chug · 2020
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Language Models are Few-shot Learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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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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GEVO: GPU Code Optimization using Evolutionary Computation
Jhe-Yu Liou, Xiaodong Wang, Stephanie Forrest, and Carole-Jean Wu · 2020
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Transformers: State-of-the-Art Natural Language Processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush · 2020
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ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations
Chris Cummins, Zacharias V Fisches, Tal Ben-Nun, Torsten Hoefler, Michael FP O’Boyle, and Hugh Leather · 2021
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Towards a Unified View of Parameter-Efficient Transfer Learning
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig · 2021
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A Learned Performance Model for Tensor Processing Units
Sam Kaufman, Phitchaya Phothilimthana, Yanqi Zhou, Charith Mendis, Sudip Roy, Amit Sabne, and Mike Burrows · 2021
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What Makes Good In-Context Examples for GPT-3?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen · 2021
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Learning to make compiler optimizations more effective
Rahim Mammadli, Marija Selakovic, Felix Wolf, and Michael Pradel · 2021
Data-Driven Offline Optimization For Architecting Hardware Accelerators
Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, and Sergey Levine · 2022
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Competition-level Code Generation with AlphaCode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals · 2022
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MemPrompt: Memory-assisted Prompt Editing with User Feedback
Aman Madaan, Niket Tandon, Peter Clark, and Yiming Yang · 2022
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CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong · 2022
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Reframing Instructional Prompts to GPTk’s Language
Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, and Hannaneh Hajishirzi · 2021
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Synchromesh: Reliable Code Generation from Pre-trained Language Models
Gabriel Poesia, Alex Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, and Sumit Gulwani · 2021
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CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks
Ruchir Puri, David Kung, Geert Janssen, Wei Zhang, Giacomo Domeniconi, Vladmir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, and Frederick Reiss · 2021
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How Fast Do Algorithms Improve? [Point of View]
Yash Sherry and Neil C. Thompson · 2021
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Learning to Superoptimize Real-world Programs
Alex Shypula, Pengcheng Yin, Jeremy Lacomis, Claire Le Goues, Edward Schwartz, and Graham Neubig · 2021
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Calibrate Before Use: Improving Few-shot Performance of Language Models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh · 2021
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MAGPIE: Machine Automated General Performance Improvement via Evolution of Software
Aymeric Blot and Justyna Petke · 2022
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Learning To Retrieve Prompts for In-Context Learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant · 2022
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An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks
Kiran Seshadri, Berkin Akin, James Laudon, Ravi Narayanaswami, and Amir Yazdanbakhsh · 2022
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Advances in the Automatic Detection of Optimization Opportunities in Computer Programs
Delaram Talaashrafi · 2022
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Natural Language Processing with Transformers
Lewis Tunstall, Leandro Von Werra, and Thomas Wolf · 2022
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A Systematic Evaluation of Large Language Models of Code
Frank F Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn · 2022
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AlpaGasus: Training A Better Alpaca with Fewer Data
Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, et al · 2023
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The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V Le, Barret Zoph, Jason Wei, et al · 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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Fine-Tuning LLMs: LoRA or Full-Parameter? An In-Depth Analysis with Llama 2 , 2023
Artur Niederfahrenhorst, Kourosh Hakhamaneshi, and Rehaan Ahmad · 2023
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hyperfine , 2023
David Peter · 2023
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Code Llama: Open Foundation Models for Code
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al · 2023
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Repository-Level Prompt Generation for Large Language Models of Code
Disha Shrivastava, Hugo Larochelle, and Daniel Tarlow · 2023
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The Wisdom of Hindsight Makes Language Models Better Instruction Followers
Tianjun Zhang, Fangchen Liu, Justin Wong, Pieter Abbeel, and Joseph E Gonzalez · 2023
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