No abstract

Virtual functions make code easier for programmers to reuse but also make it harder for compilers to analyze. We investigate the ability of three static analysis algorithms to improve C++ programs by resolving virtual function calls, thereby reducing compiled code size and reducing program complexity so as to improve both human and automated program understanding and analysis. In measurements of seven programs of significant size (5000 to 20000 lines of code each) we found that on average the most precise of the three algorithms resolved 71% of the virtual function calls and reduced compiled code size by 25%. This algorithm is very fast: it analyzes 3300 source lines per second on an 80 MHz PowerPC 601. Because of its accuracy and speed, this algorithm is an excellent candidate for inclusion in production C++ compilers.

This paper presents a surprising result: changing a seemingly innocuous aspect of an experimental setup can cause a systems researcher to draw wrong conclusions from an experiment. What appears to be an innocuous aspect in the experimental setup may in fact introduce a significant bias in an evaluation. This phenomenon is called measurement bias in the natural and social sciences.Our results demonstrate that measurement bias is significant and commonplace in computer system evaluation. By significant we mean that measurement bias can lead to a performance analysis that either over-states an effect or even yields an incorrect conclusion. By commonplace we mean that measurement bias occurs in all architectures that we tried (Pentium 4, Core 2, and m5 O3CPU), both compilers that we tried (gcc and Intel's C compiler), and most of the SPEC CPU2006 C programs. Thus, we cannot ignore measurement bias. Nevertheless, in a literature survey of 133 recent papers from ASPLOS, PACT, PLDI, and CGO, we determined that none of the papers with experimental results adequately consider measurement bias.Inspired by similar problems and their solutions in other sciences, we describe and demonstrate two methods, one for detecting (causal analysis) and one for avoiding (setup randomization) measurement bias.

Virtual functions make code easier for programmers to reuse but also make it harder for compilers to analyze. We investigate the ability of three static analysis algorithms to improve C++ programs by resolving virtual function calls, thereby reducing compiled code size and reducing program complexity so as to improve both human and automated program understanding and analysis. In measurements of seven programs of significant size (5000 to 20000 lines of code each) we found that on average the most precise of the three algorithms resolved 71% of the virtual function calls and reduced compiled code size by 25%. This algorithm is very fast: it analyzes 3300 source lines per second on an 80 MHz Pow-erPC 601. Because of its accuracy and speed, this algorithm is an excellent candidate for inclusion in production C++ compilers.

No abstract