{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T05:35:38Z","timestamp":1780378538674,"version":"3.54.1"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031226762","type":"print"},{"value":"9783031226779","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:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T00:00:00Z","timestamp":1673395200000},"content-version":"vor","delay-in-days":10,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"abstract":"Abstract<\/jats:title>Cross-platform power\/performance prediction is becoming increasingly important due to the rapid development and variety of software and hardware architectures in an era of heterogeneous multi-core. However, accurate power\/performance prediction is faced with an obstacle caused by the large gap between architectures, which is often overcome by laborious and time-consuming fine-grained program profiling on the target platform. To overcome these problems, this paper introduces $$CP^3$$<\/jats:tex-math>\n \n C<\/mml:mi>\n \n P<\/mml:mi>\n 3<\/mml:mn>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, a hierarchical Cross-platform Power\/Performance Prediction framework, which focuses on utilizing architecture differences to migrate built models to target platforms. The core of $$CP^3$$<\/jats:tex-math>\n \n C<\/mml:mi>\n \n P<\/mml:mi>\n 3<\/mml:mn>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> is the three-step hierarchical transfer learning approach, hierarchical division, partial transfer learning, and model fusion, respectively. $$CP^3$$<\/jats:tex-math>\n \n C<\/mml:mi>\n \n P<\/mml:mi>\n 3<\/mml:mn>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> firstly builds a power\/performance model on the source platform, then rebuilds it with the reduced training data on the target platform, and finally obtains a cross-platform model. We validate the effectiveness of $$CP^3$$<\/jats:tex-math>\n \n C<\/mml:mi>\n \n P<\/mml:mi>\n 3<\/mml:mn>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> using a group of benchmarks on X86- and ARM-based platforms that use three different types of commonly used processors. Evaluation results show that when applying $$CP^3$$<\/jats:tex-math>\n \n C<\/mml:mi>\n \n P<\/mml:mi>\n 3<\/mml:mn>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, only 1% of the baseline training data is required to achieve high cross-platform prediction accuracy, with power prediction error being only 0.65%, and performance prediction error being only 4.64%.<\/jats:p>","DOI":"10.1007\/978-3-031-22677-9_7","type":"book-chapter","created":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T09:04:32Z","timestamp":1673341472000},"page":"117-138","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["CP$$^{3}$$: Hierarchical Cross-Platform Power\/Performance Prediction Using a Transfer Learning Approach"],"prefix":"10.1007","author":[{"given":"Xinxin","family":"Qi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Wang, R., et al.: Brief introduction of tianHe exascale prototype system. 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