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Code Optim."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>\n                    I\/O performance has become a major bottleneck for many data-intensive applications. Each layer of the parallel I\/O stack provides parameters that can optimize I\/O performance, but determining the optimal performance parameters based on the operating configuration is a challenge. Previous work has required separate performance models for different programs for tuning, which is very costly in term of measurement data. We propose BLG-Tuning: a\n                    <jats:underline>B<\/jats:underline>\n                    enchmark-based\n                    <jats:underline>L<\/jats:underline>\n                    ow-cost\n                    <jats:underline>G<\/jats:underline>\n                    eneral-purpose I\/O Modeling and Tuning. BLG-Tuning maps application I\/O loads to benchmark parameters and uses the benchmark-trained performance model to achieve I\/O performance prediction and thus avoid the additional computing and communication overhead for measurement. For applications, BLG-Tuning collects the application characteristics to calibrate the performance model and improve prediction accuracy. Experience shows that BLG-Tuning predicts the I\/O time of MADbench2, Flash-IO, S3D-IO, BT-IO, and LAMMPS with MAPE of 22.2%, 18.2%, 29.3%, 21.5%, and 34.8%, respectively. After tuning, the five applications obtain I\/O speedup from 5.6\u00d7 to 27.3\u00d7.\n                  <\/jats:p>","DOI":"10.1145\/3806050","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T11:20:36Z","timestamp":1774956036000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["BLG-Tuning: Benchmark-Based Low-Cost General-Purpose I\/O Modeling and Tuning"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5064-2376","authenticated-orcid":false,"given":"Ziheng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5106-2339","authenticated-orcid":false,"given":"Chaoqun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4483-0318","authenticated-orcid":false,"given":"Yifan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5923-3780","authenticated-orcid":false,"given":"Yuchao","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8430-2325","authenticated-orcid":false,"given":"Yuping","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9003-2625","authenticated-orcid":false,"given":"Xiaoshe","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,25]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/236017.236027"},{"key":"e_1_3_1_3_2","first-page":"587","volume-title":"Proceedings of the IEEE International Conference on High Performance Computing and Communications (HPCC)","author":"Agarwal Megha","year":"2021","unstructured":"Megha Agarwal, Pragya Jain, Divyansh Singhvi, and Preeti Malakar. 2021. 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