{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-preliminary-study-of-neural-network-based","title":"A Preliminary Study of Neural Network-based Approximation for HPC Applications","arxiv_id":"1812.07561","date":"2018-12-18","proceeding":null,"authors":["Wenqian Dong","Anzheng Guolu","Dong Li"],"abstract":"Machine learning, as a tool to learn and model complicated (non)linear\nrelationships between input and output data sets, has shown preliminary success\nin some HPC problems. Using machine learning, scientists are able to augment\nexisting simulations by improving accuracy and significantly reducing\nlatencies. Our ongoing research work is to create a general framework to apply\nneural network-based models to HPC applications. In particular, we want to use\nthe neural network to approximate and replace code regions within the HPC\napplication to improve performance (i.e., reducing the execution time) of the\nHPC application. In this paper, we present our preliminary study and results.\nUsing two applications (the Newton-Raphson method and the Lennard-Jones (LJ)\npotential in LAMMP) for our case study, we achieve up to 2.7x and 2.46x\nspeedup, respectively.","url_abs":"http://arxiv.org/abs/1812.07561v1","url_pdf":"http://arxiv.org/pdf/1812.07561v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-preliminary-study-of-neural-network-based","repo_url":"https://github.com/daniel-e/papr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}