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With easier access to powerful compute resources, there is a growing trend in the field of AI for software development to develop larger and larger language models (LLMs) to address a variety of programming tasks.
Open MPI: Goals, concept, and design of a next generation MPI implementation
Edgar Gabriel, Graham E Fagg, George Bosilca, Thara Angskun, Jack J Dongarra, Jeffrey M Squyres, Vishal Sahay, Prabhanjan Kambadur, Brian Barrett, Andrew Lumsdaine, et al · 2004
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2015
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Parallel programming with OpenACC
Rob Farber · 2016
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SYCL: Single-source C++ accelerator programming
Ruyman Reyes and Victor Lomüller · 2016
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Identifying pitfalls in automatic parallelization of nas parallel benchmarks
S Prema, R Jehadeesan, and BK Panigrahi · 2017
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A little linguistics goes a long way: Unsupervised segmentation with limited language specific guidance
Alexander Erdmann, Salam Khalifa, Mai Oudah, Nizar Habash, and Houda Bouamor · 2019
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A study on popular auto-parallelization frameworks
S Prema, Rupesh Nasre, R Jehadeesan, and BK Panigrahi · 2019
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Gpt-3: Its nature, scope, limits, and consequences
Luciano Floridi and Massimo Chiriatti · 2020
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Graphcodebert: Pre-training code representations with data flow
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al · 2020
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Source-to-source parallelization compilers for scientific shared-memory multi-core and accelerated multiprocessing: analysis, pitfalls, enhancement and potential
Re’em Harel, Idan Mosseri, Harel Levin, Lee-or Alon, Matan Rusanovsky, and Gal Oren · 2020
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Compar: optimized multi-compiler for automatic openmp s2s parallelization
Idan Mosseri, Lee-or Alon, Re’Em Harel, and Gal Oren · 2020
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Negative perceptions about the applicability of source-to-source compilers in hpc: A literature review
Reed Milewicz, Peter Pirkelbauer, Prema Soundararajan, Hadia Ahmed, and Tony Skjellum · 2021
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Recent advances in natural language processing via large pre-trained language models: A survey
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth · 2021
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Hpc: Where we are today and a look into the future
Jack Dongarra · 2022
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre · 2022
Cited alongside, same era.
Reinventing high performance computing: challenges and opportunities
Compile: A large ir dataset from production sources, 2023
Aiden Grossman, Ludger Paehler, Konstantinos Parasyris, Tal Ben-Nun, Jacob Hegna, William Moses, Jose M Monsalve Diaz, Mircea Trofin, and Johannes Doerfert · 2023
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Learning to parallelize in a shared-memory environment with transformers
Re’em Harel, Yuval Pinter, and Gal Oren · 2023
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Intel Developer Cloud
Intel · 2023
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Neural machine translation for code generation
Dharma KC and Clayton T Morrison · 2023
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Starcoder: may the source be with you!
Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, et al · 2023
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Daniel Reed, Dennis Gannon, and Jack Dongarra · 2022
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Code translation with compiler representations
Marc Szafraniec, Baptiste Roziere, Hugh Leather, Francois Charton, Patrick Labatut, and Gabriel Synnaeve · 2022
Cited alongside, same era.
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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A survey on pretrained language models for neural code intelligence
Yichen Xu and Yanqiao Zhu · 2022
Cited alongside, same era.
Wenqing Zheng, SP Sharan, AJAY KUMAR JAISWAL, Kevin Wang, Yihan Xi, and Zhangyang Wang · 2022
Cited alongside, same era.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
Cited alongside, same era.
Codetf: One-stop transformer library for state-of-the-art code llm
Nghi DQ Bui, Hung Le, Yue Wang, Junnan Li, Akhilesh Deepak Gotmare, and Steven CH Hoi · 2023
Cited alongside, same era.
Lm4hpc: Towards effective language model application in high-performance computing
Le Chen, Pei-Hung Lin, Tristan Vanderbruggen, Chunhua Liao, Murali Emani, and Bronis de Supinski
Cited in the paper.
Daniel Nichols, Aniruddha Marathe, Harshitha Menon, Todd Gamblin, and Abhinav Bhatele · 2023
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Hpc forecast: Cloudy and uncertain
Daniel Reed, Dennis Gannon, and Jack Dongarra · 2023
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Mpi-rical: Data-driven mpi distributed parallelism assistance with transformers
Nadav Schneider, Tal Kadosh, Niranjan Hasabnis, Timothy Mattson, Yuval Pinter, and Gal Oren · 2023
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Multigraph learning for parallelism discovery in sequential programs
Yuanyuan Shen, Manman Peng, Qiang Wu, and Guoqi Xie · 2023
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A comprehensive capability analysis of gpt-3 and gpt-3.5 series models
Junjie Ye, Xuanting Chen, Nuo Xu, Can Zu, Zekai Shao, Shichun Liu, Yuhan Cui, Zeyang Zhou, Chao Gong, Yang Shen, et al · 2023
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
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