| LDR | | 01982nam u200433 4500 |
| 001 | | 000000422245 |
| 005 | | 20190215165954 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438079755 |
| 035 | |
▼a (MiAaPQ)AAI10827562 |
| 035 | |
▼a (MiAaPQ)cmu:10266 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 621.3 |
| 100 | 1 |
▼a Cai, Ermao. |
| 245 | 10 |
▼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. |
| 502 | 1 |
▼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 |
| 710 | 20 |
▼a Carnegie Mellon University.
▼b Electrical and Computer Engineering. |
| 773 | 0 |
▼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 |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999043
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |