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Existing methods often struggle to balance these factors under scalability constraints, as hardware limitations tightly bound the available sampling resources. This article focuses on the issue of high-precision real-time processor power forecasting while maintaining (or minimally increasing) the total overhead of multiprocessor power forecasting, particularly when the parallelism scale ranges from\n                    <jats:italic toggle=\"yes\">P<\/jats:italic>\n                    to\n                    <jats:italic toggle=\"yes\">2P<\/jats:italic>\n                    processors or when the problem size scales from\n                    <jats:italic toggle=\"yes\">M<\/jats:italic>\n                    to\n                    <jats:italic toggle=\"yes\">2M<\/jats:italic>\n                    . We propose\n                    <jats:sc>CML-PowF<\/jats:sc>\n                    , a low-overhead multiprocessor real-time power forecasting approach based on data clustering.\n                    <jats:sc>CML-PowF<\/jats:sc>\n                    integrates two key algorithms:\n                    <jats:sc>Alg-CEF<\/jats:sc>\n                    , which conducts cluster matching on the runtime characteristics of the program at the\n                    <jats:italic toggle=\"yes\">P<\/jats:italic>\n                    \/\n                    <jats:italic toggle=\"yes\">M<\/jats:italic>\n                    scale, and models the tradeoff among forecasting error, time span, and sampling overhead.\n                    <jats:sc>Alg-MSF<\/jats:sc>\n                    , which leverages execution patterns from smaller-scale runs to determine the optimal sampling overhead and forecasting time span at the\n                    <jats:italic toggle=\"yes\">2P<\/jats:italic>\n                    \/\n                    <jats:italic toggle=\"yes\">2M<\/jats:italic>\n                    scale. We evaluate\n                    <jats:sc>CML-PowF<\/jats:sc>\n                    on x86 and ARM platforms with up to 32 computing nodes (2,048 cores). Results show that it achieves 3\u20136% forecasting error at large scales with only 0.2\u20130.5% degradation compared to the\n                    <jats:italic toggle=\"yes\">P<\/jats:italic>\n                    \/\n                    <jats:italic toggle=\"yes\">M<\/jats:italic>\n                    scale, without increasing total sampling overhead. Integrated with the\n                    <jats:sc>PowC<\/jats:sc>\n                    control system,\n                    <jats:sc>CML-PowF<\/jats:sc>\n                    effectively maintains real-time processor power below target thresholds.\n                  <\/jats:p>","DOI":"10.1145\/3810247","type":"journal-article","created":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T09:14:34Z","timestamp":1779527674000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["CML-PowF: Data Clustering Matching Based Low-overhead Multiple CPU Real-time Power Forecasting"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7661-7922","authenticated-orcid":false,"given":"Rongyu","family":"Deng","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9941-9885","authenticated-orcid":false,"given":"Juan","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1000-0158","authenticated-orcid":false,"given":"Zihan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5072-6093","authenticated-orcid":false,"given":"Yuan","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6073-9849","authenticated-orcid":false,"given":"Yong","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5463-8765","authenticated-orcid":false,"given":"Aolin","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2680-9911","authenticated-orcid":false,"given":"Zhaoyang","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0712-8575","authenticated-orcid":false,"given":"Yida","family":"Gu","sequence":"additional","affiliation":[{"name":"State Key Lab of Processors, Institute of Computing Technology, CAS","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5422-4497","authenticated-orcid":false,"given":"Dingwen","family":"Tao","sequence":"additional","affiliation":[{"name":"State Key Lab of Processors, Institute of Computing Technology, CAS","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,26]]},"reference":[{"issue":"4","key":"e_1_3_2_2_2","first-page":"941","article-title":"Energy wall for exascale supercomputing","volume":"35","author":"Wang Zhiyuan","year":"2016","unstructured":"Zhiyuan Wang, Yuhua Tang, Juan Chen, Jingling Xue, Yun Zhou, and Yong Dong. 2016. 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