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We explore the novel application of Large Language Models to code optimization.
“A New Algorithm for Data Compression”
Philip Gage · 1994
Earlier work this paper cites.
“Differential Testing for Software”
William McKeeman · 1998
Earlier work this paper cites.
“Iterative Compilation in a Non-linear Optimisation Space”
François Bodin et al · 1998
Earlier work this paper cites.
“Combined Selection of Tile Sizes and Unroll Factors using Iterative Compilation”
T. Kisuki, P.M.W. Knijnenburg and M.F.P. O’Boyle · 2000
Earlier work this paper cites.
“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”
Chris Lattner and Vikram Adve · 2004
Earlier work this paper cites.
“Evaluating Iterative Compilation”
G.. Fursin, M… O’Boyle and P… Knijnenburg · 2005
Earlier work this paper cites.
“Using Machine Learning to Focus Iterative Optimization”
F. Agakov et al · 2006
Earlier work this paper cites.
“Finding and Understanding Bugs in C Compilers”
Xuejun Yang, Yang Chen, Eric Eide and John Regehr · 2011
Earlier work this paper cites.
“Convolutional Neural Networks Over Tree Structures for Programming Language Processing”
Lili Mou et al · 2016
Earlier work this paper cites.
“Attention Is All You Need”
Ashish Vaswani et al · 2017
Earlier work this paper cites.
“Decoupled Weight Decay Regularization”
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
“Proximal Policy Optimization Algorithms”
John Schulman et al · 2017
Earlier work this paper cites.
“Minimizing the Cost of Iterative Compilation with Active Learning”
William. Ogilvie, Pavlos Petoumenos, Zheng Wang and Hugh Leather · 2017
Earlier work this paper cites.
“End-to-End Deep Learning of Optimization Heuristics”
Chris Cummins, Pavlos Petoumenos, Zheng Wang and Hugh Leather · 2017
Earlier work this paper cites.
“Machine Learning in Compiler Optimisation”
Zheng Wang and Michael O’Boyle · 2018
Earlier work this paper cites.
“CodeSearchNet Challenge: Evaluating the State of Semantic Code Search”
Hamel Husain et al · 2019
Earlier work this paper cites.
“Unsupervised Translation of Programming Languages”
Marie-Anne Lachaux, Baptiste Roziere, Lowik Chanussot and Guillaume Lample · 2020
Earlier work this paper cites.
“Machine Learning in Compilers: Past, Present and Future”
Hugh Leather and Chris Cummins · 2020
Earlier work this paper cites.
“The Pile: An 800GB Dataset of Diverse Text for Language Modeling”
Leo Gao et al · 2020
Earlier work this paper cites.
“Overview of the PAN@FIRE 2020 task on the authorship identification of SOurce COde (AI-SOCO)”
Ali Fadel et al · 2020
Earlier work this paper cites.
“Random Testing for C and C++ Compilers with YARPGen”
Vsevolod Livinskii, Dmitry Babokin and John Regehr · 2020
Earlier work this paper cites.
“AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning”
Ameer Haj-Ali et al · 2020
Earlier work this paper cites.
“NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learning”
Ameer Haj-Ali et al · 2020
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“Automated conformance testing for JavaScript engines via deep compiler fuzzing”
Guixin Ye et al · 2021
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“Less Training, More Repairing Please: Revisiting Automated Program Repair via Zero-shot Learning”
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