Reducing the overhead of ML exceptions by selective CPS transformation선택적인 CPS 변환에 의한 ML의 예외상황 실행속도의 개선

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 414
  • Download : 0
DC FieldValueLanguage
dc.contributor.advisorYi, Kwang-Keun-
dc.contributor.advisor이광근-
dc.contributor.authorKim, Jung-Taek-
dc.contributor.author김정택-
dc.date.accessioned2011-12-13T05:59:37Z-
dc.date.available2011-12-13T05:59:37Z-
dc.date.issued1999-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=150934&flag=dissertation-
dc.identifier.urihttp://hdl.handle.net/10203/34312-
dc.description학위논문(석사) - 한국과학기술원 : 전산학과, 1999.2, [ 56 p. ]-
dc.description.abstractML``s exception handling makes it possible to describe exceptional execution flows conveniently. Sometimes, current implementation of exception handling introduce unnecessary overhead. Our goal is to reduce this overhead by source-level transformation. To this end, we transform source programs into variant of continuation-passing style(CPS), replacing handle and raise expressions by continuation-catching and throwing expressions, respectively. CPS-transforming every expression, however, introduces a new cost. We therefore use an exception analysis to transform expressions selectively: if an expression is statically determined to involve exceptions then it is CPS-transformed; otherwise, it is left in direct style. In this article, we formalize this selective CPS transformation, prove its correctness, and present early experimental data indicating its effect on ML programs.eng
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectML-
dc.subjectException-
dc.subjectCPS 변환-
dc.subject예외상황-
dc.subjectCPS transformation-
dc.titleReducing the overhead of ML exceptions by selective CPS transformation-
dc.title.alternative선택적인 CPS 변환에 의한 ML의 예외상황 실행속도의 개선-
dc.typeThesis(Master)-
dc.identifier.CNRN150934/325007-
dc.description.department한국과학기술원 : 전산학과, -
dc.identifier.uid000973160-
dc.contributor.localauthorYi, Kwang-Keun-
dc.contributor.localauthor이광근-
Appears in Collection
CS-Theses_Master(석사논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0