{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T18:34:47Z","timestamp":1758652487296,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,12]]},"DOI":"10.1145\/3673038.3673059","type":"proceedings-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T18:29:01Z","timestamp":1723141741000},"page":"669-678","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid Machine Learning Method for Cross-Platform Performance Prediction of Parallel Applications"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2092-9585","authenticated-orcid":false,"given":"Kaveh","family":"Mahdavi","sequence":"first","affiliation":[{"name":"Computer Science, University of Stirling, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2024,8,12]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"[1] Barcelona Supercomputing Center Performance Analysis Tools. Available at: https:\/\/tools.bsc.es\/"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/jlpea12030037"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2503210.2503247"},{"key":"e_1_3_2_1_4_1","volume-title":"2011 IEEE 17th International Conference on Parallel and Distributed Systems (pp. 332-339)","author":"Casas M.","year":"2011","unstructured":"[4] Casas, M., Servat, H., Huck, K., Gimenez, J., & Labarta, J. (2011, December). Trace spectral analysis toward dynamic levels of detail. In 2011 IEEE 17th International Conference on Parallel and Distributed Systems (pp. 332-339)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/155332.155333"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/2388996.2389109"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3127024.3127040"},{"key":"e_1_3_2_1_8_1","volume-title":"Workshop on wide area networks and high performance computing (pp. 171-187)","author":"Hoisie A.","year":"2007","unstructured":"[8] Hoisie, A., Lubeck, O., & Wasserman, H. (2007, September). Performance analysis of wavefront algorithms on very-large scale distributed systems. In Workshop on wide area networks and high performance computing (pp. 171-187). London: Springer London."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2006.1598116"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-24685-5_67"},{"key":"e_1_3_2_1_11_1","volume-title":"Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30","author":"Ke G.","year":"2017","unstructured":"[11] Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W.,... & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1229428.1229479"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2010.5470350"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3337821.3337922"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN55064.2022.9892921"},{"key":"e_1_3_2_1_16_1","volume-title":"UPC, Departament d\u2019Arquitectura de Computadors. DOI 10.5821\/dissertation-2117-375586.","author":"Mahdavi K.","year":"2022","unstructured":"[16] Mahdavi, K. (2022). Enhanced clustering analysis pipeline for performance analysis of parallel applications. Tesi doctoral, UPC, Departament d\u2019Arquitectura de Computadors. DOI 10.5821\/dissertation-2117-375586."},{"key":"e_1_3_2_1_17_1","volume-title":"K-nearest neighbor classification. Data mining in agriculture, 83-106","author":"Mucherino A.","year":"2009","unstructured":"[17] Mucherino, A., Papajorgji, P. J., Pardalos, P. M., Mucherino, A., Papajorgji, P. J., & Pardalos, P. M. (2009). K-nearest neighbor classification. Data mining in agriculture, 83-106."},{"key":"e_1_3_2_1_18_1","volume-title":"PACE-a toolset for the performance prediction of parallel and distributed systems. IJHPCA, 14(3), (pp. 228-251)","author":"Nudd G. R.","year":"2000","unstructured":"[18] Nudd, G. R., & Kerbyson, D. J. (2000). PACE-a toolset for the performance prediction of parallel and distributed systems. IJHPCA, 14(3), (pp. 228-251)."},{"key":"e_1_3_2_1_19_1","unstructured":"[19] LightGBM documentation https:\/\/lightgbm.readthedocs.io"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1964218.1964225"},{"key":"e_1_3_2_1_21_1","volume-title":"International conference on machine learning (pp. 1143-1151)","author":"Romano S.","year":"2014","unstructured":"[21] Romano, S., Bailey, J., Nguyen, V., & Verspoor, K. (2014, June). Standardized mutual information for clustering comparisons: one step further in adjustment for chance. In International conference on machine learning (pp. 1143-1151). PMLR."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-58667-0_19"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1177\/109434200001400407"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/1362622.1362674"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2019.01.006"},{"issue":"1","key":"e_1_3_2_1_26_1","first-page":"26","article-title":"Hyperparameter optimization for machine learning models based on Bayesian optimization","volume":"17","author":"Wu J.","year":"2019","unstructured":"[26] Wu, J., Chen, X. Y., Zhang, H., Xiong, L. D., Lei, H., & Deng, S. H. (2019). Hyperparameter optimization for machine learning models based on Bayesian optimization. Journal of Electronic Science and Technology, 17(1), 26-40.","journal-title":"Journal of Electronic Science and Technology"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3588993.3597262"},{"key":"e_1_3_2_1_28_1","volume-title":"18th Int. In Parallel and Distributed Processing Symposium, IPDPS., (pp. 78-87)","author":"Zheng G.","year":"2004","unstructured":"[28] Zheng, G., Kakulapati, G., & BigSim, L. K. (2004, April). A Parallel Simulator for Performance Prediction of Extremely Large Parallel Machines Proc. 18th Int. In Parallel and Distributed Processing Symposium, IPDPS., (pp. 78-87)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3339186.3339197"}],"event":{"name":"ICPP '24: the 53rd International Conference on Parallel Processing","acronym":"ICPP '24","location":"Gotland Sweden"},"container-title":["Proceedings of the 53rd International Conference on Parallel Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3673038.3673059","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3673038.3673059","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T17:30:50Z","timestamp":1758648650000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3673038.3673059"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,12]]},"references-count":29,"alternative-id":["10.1145\/3673038.3673059","10.1145\/3673038"],"URL":"https:\/\/doi.org\/10.1145\/3673038.3673059","relation":{},"subject":[],"published":{"date-parts":[[2024,8,12]]},"assertion":[{"value":"2024-08-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}