Assessing the Overhead of ML Exceptions by Selective CPS Transformation Export

@techreport{citeulike:915736, abstract = {ML's exception handling makes it possible to describe exceptional execution flows conveniently, but it also forms a performance bottleneck. Our goal is to reduce this overhead by source-level transformation. To this end, we transform source programs into 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.}, author = {Kim, Jung-Taek and Yi, Kwangkeun and Danvy, Olivier}, citeulike-article-id = {915736}, citeulike-linkout-0 = {http://www.brics.dk/RS/98/15/}, institution = {BRICS}, keywords = {continuations, cps, exception-analysis, exceptions, return}, month = {September}, posted-at = {2006-10-28 10:47:43}, priority = {2}, title = {Assessing the Overhead of ML Exceptions by Selective CPS Transformation}, url = {http://www.brics.dk/RS/98/15/}, year = {1998} }

TY - RPRT ID - citeulike:915736 L3 - citeulike-article-id:915736 N2 - ML's exception handling makes it possible to describe exceptional execution flows conveniently, but it also forms a performance bottleneck. Our goal is to reduce this overhead by source-level transformation. To this end, we transform source programs into 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. TI - Assessing the Overhead of ML Exceptions by Selective CPS Transformation KW - continuations KW - cps KW - exception-analysis KW - exceptions KW - return AU - Kim, Jung-Taek AU - Yi, Kwangkeun AU - Danvy, Olivier PY - 1998/09// UR - http://www.brics.dk/RS/98/15/ ER -