Reducing the overhead of ML exceptions by selective CPS transformation = 선택적인 CPS 변환에 의한 ML의 예외상황 실행속도의 개선

ML``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.
Advisors
Yi, Kwang-Keunresearcher이광근researcher
Publisher
한국과학기술원
Issue Date
1999
Identifier
150934/325007 / 000973160
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전산학과, 1999.2, [ 56 p. ]

Keywords

ML; Exception; CPS 변환; 예외상황; CPS transformation

URI
http://hdl.handle.net/10203/34312
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=150934&flag=t
Appears in Collection
CS-Theses_Master(석사논문)
Files in This Item
There are no files associated with this item.
  • Hit : 81
  • Download : 0
  • Cited 0 times in thomson ci

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0