MARC보기
LDR01906nmm uu200433 4500
001000000334025
00520240805175005
008181129s2018 |||||||||||||||||c||eng d
020 ▼a 9780438079755
035 ▼a (MiAaPQ)AAI10827562
035 ▼a (MiAaPQ)cmu:10266
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 621.3
1001 ▼a Cai, Ermao.
24510 ▼a Power/Performance Modeling and Optimization: Using and Characterizing Machine Learning Applications.
260 ▼a [S.l.] : ▼b Carnegie Mellon University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 135 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
500 ▼a Adviser: Diana Marculescu.
5021 ▼a Thesis (Ph.D.)--Carnegie Mellon University, 2018.
520 ▼a Energy and power are the main design constraints for modern high-performance computing systems. Indeed, energy efficiency plays a critical role in performance improvement or energy saving for either state-of-the-art general purpose hardware plat
520 ▼a In this thesis, we study these effects and propose to combine machine learning techniques and domain knowledge to learn the performance, power, and energy models for high-performance computing systems. For technology-aware multi-core system desi
590 ▼a School code: 0041.
650 4 ▼a Computer engineering.
650 4 ▼a Artificial intelligence.
650 4 ▼a Computer science.
690 ▼a 0464
690 ▼a 0800
690 ▼a 0984
71020 ▼a Carnegie Mellon University. ▼b Electrical and Computer Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-11B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0041
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999043 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자