{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T14:50:50Z","timestamp":1776783050215,"version":"3.51.2"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031407437","type":"print"},{"value":"9783031407444","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-40744-4_2","type":"book-chapter","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T23:03:28Z","timestamp":1693436608000},"page":"18-33","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["LM4HPC: Towards Effective Language Model Application in\u00a0High-Performance Computing"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3847-4108","authenticated-orcid":false,"given":"Le","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4977-814X","authenticated-orcid":false,"given":"Pei-Hung","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tristan","family":"Vanderbruggen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6477-0547","authenticated-orcid":false,"given":"Chunhua","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Murali","family":"Emani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0339-1006","authenticated-orcid":false,"given":"Bronis","family":"de Supinski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,1]]},"reference":[{"key":"2_CR1","unstructured":"Bubeck, S., et al.: Sparks of artificial general intelligence: early experiments with GPT-4. arXiv preprint arXiv:2303.12712 (2023)"},{"key":"2_CR2","unstructured":"Chen, M., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021)"},{"key":"2_CR3","unstructured":"Vaswani, A., et al.: Attention is all you need. Advances in Neural Information Processing Systems 30 (2017)"},{"key":"2_CR4","unstructured":"Touvron, H., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Chen, L., Mahmud, Q.I., Jannesari, A.: Multi-view learning for parallelism discovery of sequential programs. In: 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 295\u2013303. IEEE (2022)","DOI":"10.1109\/IPDPSW55747.2022.00059"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Flynn, P., Vanderbruggen, T., Liao, C., Lin, P.H., Emani, M., Shen, X.: Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines. arXiv preprint arXiv:2208.05596 (2022)","DOI":"10.1007\/978-3-031-36889-9_27"},{"key":"2_CR7","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Feng, Z., et al.: CodeBERT: a pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, W., Joty, S., Hoi, S.C.: CodeT5: identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. arXiv preprint arXiv:2109.00859 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.685"},{"key":"2_CR10","unstructured":"Li, R., et al.: StarCoder: may the source be with you! arXiv preprint arXiv:2305.06161 (2023)"},{"key":"2_CR11","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"2_CR12","unstructured":"The PY-tree-sitter project (2023). https:\/\/pypi.org\/project\/tree-sitter-builds\/. Accessed 15 May 2023"},{"key":"2_CR13","unstructured":"ProGraML: program Graphs for Machine Learning (2022). https:\/\/pypi.org\/project\/programl\/. Accessed 15 May 2023"},{"key":"2_CR14","unstructured":"Chen, L., Mahmud, Q.I., Phan, H., Ahmed, N.K., Jannesari, A.: Learning to parallelize with openMP by augmented heterogeneous AST representation. arXiv preprint arXiv:2305.05779 (2023)"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Mou, L., Li, G., Zhang, L., Wang, T., Jin, Z.: Convolutional neural networks over tree structures for programming language processing. In: Proceedings of the AAAI Conference On Artificial Intelligence, vol. 30 (2016)","DOI":"10.1609\/aaai.v30i1.10139"},{"key":"2_CR16","doi-asserted-by":"publisher","unstructured":"Lin, P.H., Liao, C.: DRB-ML-dataset (2022). https:\/\/doi.org\/10.11579\/1958879","DOI":"10.11579\/1958879"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Liao, C., Lin, P.H., Asplund, J., Schordan, M., Karlin, I.: DataRaceBench: a benchmark suite for systematic evaluation of data race detection tools. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1\u201314 (2017)","DOI":"10.1145\/3126908.3126958"},{"key":"2_CR18","unstructured":"Jin, H.Q., Frumkin, M., Yan, J.: The OpenMP implementation of NAS parallel benchmarks and its performance (1999)"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Che, S., et al.: Rodinia: a benchmark suite for heterogeneous computing. In: 2009 IEEE international symposium on workload characterization (IISWC), pp. 44\u201354. IEEE (2009)","DOI":"10.1109\/IISWC.2009.5306797"},{"key":"2_CR20","unstructured":"Chase, H.: LangChain: next Generation Language Processing (2023). https:\/\/langchain.com\/. Accessed 15 May 2023"},{"key":"2_CR21","unstructured":"Ren, S., et al.: CodeBLEU: a method for automatic evaluation of code synthesis. arXiv preprint arXiv:2009.10297 (2020)"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Chen, W., Vanderbruggen, T., Lin, P.H., Liao, C., Emani, M.: Early experience with transformer-based similarity analysis for DataRaceBench. In: 2022 IEEE\/ACM Sixth International Workshop on Software Correctness for HPC Applications (Correctness), pp. 45\u201353. IEEE (2022)","DOI":"10.1109\/Correctness56720.2022.00011"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Harel, R., Pinter, Y., Oren, G.: Learning to parallelize in a shared-memory environment with transformers. In: Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, pp. 450\u2013452 (2023)","DOI":"10.1145\/3572848.3582565"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Verma, G., et al.: HPCFAIR: Enabling FAIR AI for HPC Applications. In: 2021 IEEE\/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC), pp. 58\u201368. IEEE (2021)","DOI":"10.1109\/MLHPC54614.2021.00011"},{"key":"2_CR25","unstructured":"Yu, S., et al.: Towards seamless management of AI models in high-performance computing (2022)"},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"Jin, D., Pan, E., Oufattole, N., Weng, W.H., Fang, H., Szolovits, P.: What disease does this patient have? A large-scale open domain question answering dataset from medical exams. arXiv preprint arXiv:2009.13081 (2020)","DOI":"10.20944\/preprints202105.0498.v1"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Armengol-Estap\u00e9, J., Woodruff, J., Brauckmann, A., Magalh\u00e3es, J.W.d.S., O\u2019Boyle, M.F.: ExeBench: an ML-scale dataset of executable C functions. In: Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming, pp. 50\u201359 (2022)","DOI":"10.1145\/3520312.3534867"},{"key":"2_CR28","unstructured":"Wu, M., Waheed, A., Zhang, C., Abdul-Mageed, M., Aji, A.F.: LaMini-LM: a diverse herd of distilled models from large-scale instructions. arXiv preprint arXiv:2304.14402 (2023)"}],"container-title":["Lecture Notes in Computer Science","OpenMP: Advanced Task-Based, Device and Compiler Programming"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-40744-4_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T23:03:57Z","timestamp":1693436637000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-40744-4_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031407437","9783031407444"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-40744-4_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWOMP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on OpenMP","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bristol","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwomp2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iwomp.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"20","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"75% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}