Improving dynamically-generated code performance on dynamic binary translators

Wenwen Wang, Jiacheng Wu, Xiaoli Gong, Tao Li, Pen Chung Yew

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations


The recent transition in the software industry toward dynamically generated code poses a new challenge to existing dynamic binary translation (DBT) systems. A significant re-translation overhead could be introduced due to the maintenance of the consistency between the dynamically-generated guest code and the corresponding translated host code. To address this issue, this paper presents a novel approach to optimize DBT systems for guest applications with dynamically-generated code. The proposed approach can maximize the reuse of previously translated host code to mitigate the re-translation overhead. A prototype based on such an approach has been implemented on an existing DBT system HQEMU. Experimental results on a set of JavaScript applications show that it can achieve a 1.24X performance speedup on average compared to the original HQEMU.

Original languageEnglish (US)
Title of host publicationVEE 2018 - Proceedings of the 2018 International Conference on Virtual Execution Environments
PublisherAssociation for Computing Machinery, Inc
Number of pages14
ISBN (Electronic)9781450355797
StatePublished - Mar 25 2018
Event14th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, VEE 2018 - Williamsburg, United States
Duration: Mar 25 2018Mar 25 2018

Publication series

PublisherAssociation for Computing Machinery (ACM)
ISSN (Print)1523-2867


Other14th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, VEE 2018
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. 1514444. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation. This work is also partially supported by the Natural Science Foundation of Tianjin, China under Grant No. 16JCY-BJC15200 and 17JCQNJC00300, the Major Science and Technology Program of Big Data and Cloud Computing of Tianjin, China under Grant No. 15ZXDSGX00020, the National Key Research and Development Program of China under Grant No. 2016YFC0400709, the National Natural Science Foundation of China (61702286), Open Projects of State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences (CARCH201604), and Special Funding for Excellent Enterprise Technology Correspondent of Tianjin, China (17JCTPJC49500).

Publisher Copyright:
© 2018 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.


  • Binary Code Matching
  • DBT
  • JIT


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