| dc.contributor.advisor | Saman P. Amarasinghe. | en_US |
| dc.contributor.author | Yu, Chris, 1981- | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. | en_US |
| dc.date.accessioned | 2006-07-13T15:20:32Z | |
| dc.date.available | 2006-07-13T15:20:32Z | |
| dc.date.copyright | 2004 | en_US |
| dc.date.issued | 2004 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.1/33392 | |
| dc.description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004. | en_US |
| dc.description | Includes bibliographical references (leaves 74-75). | en_US |
| dc.description.abstract | Function inlining is a compiler optimization where the function call is replaced by the code from the function itself. Using a form of machine learning called genetic programming, this thesis examines which factors are important in determining which function calls to inline to maximize performance. A number of different heuristics are generated for inlining decisions in the Trimaran compiler, which improve on performance from the current default inlining heuristic. Also, trends in function inlining are examined over the thousands of compilation runs that are completed. | en_US |
| dc.description.statementofresponsibility | by Chris Yu. | en_US |
| dc.format.extent | 75 leaves | en_US |
| dc.format.extent | 2815540 bytes | |
| dc.format.extent | 2818608 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | Characterizing function inlining with genetic programming | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | M.Eng. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| dc.identifier.oclc | 62560239 | en_US |
