Module-aware translation for real-life desktop applications

A dynamic binary translator is a just-in-time compiler that translates source architecture binaries into target architecture binaries on the fly. It enables the fast running of the source architecture binaries on the target architecture. Traditional dynamic binary translators invalidate their translations when a module is unloaded, so later re-loading of the same module will lead to a full retranslation. Moreover, most of the loading and unloading are performed on a few “hot ” modules, which causes the dynamic binary translator to spend a significant amount of time on repeatedly translating these “hot ” modules. Furthermore, the retranslation may lead to excessive memory consumption if the code pages containing the translated codes that have been invalidated are not timely recycled. In addition, we observed that the overhead for translating real-life desktop applications is a big challenge to the overall performance of the applications, and our detailed analysis proved that real-life desktop applications dynamically load and unload modules much more frequently as compared to popular benchmarks, such as SPEC CPU2000. To address these issues, we propose a translation reuse engine that uses a novel verification method and a module-aware memory management mechanism. The proposed approach was fully implemented in IA-32 Execution Layer (IA-32 EL) [1], a commercial dynamic binary translator that enables the execution of IA-32 applications on Intel ® Itanium ® processor family. Collected results show that the module-aware translation improves the performance of Adobe * Illustrator by 14.09 % and Microsoft* Publisher by 9.73%. The overhead brought by the translation reuse engine accounts for no more than 0.2 % of execution time

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI