JOURNAL ARTICLE

Towards Efficient Dynamic Binary Translation Optimizations Based on RISC Architectural Features.

  • Published In: Journal of Circuits, Systems & Computers, 2024, v. 33, n. 6. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Xie, WenBing; Tang, DaGuo; Qi, FengBin; Chai, ZhiLei; Luo, QiaoLing; Lin, Yuan 3 of 3

Abstract

Dynamic binary translation (DBT) is a core technology that enables the migration of legacy software to different instruction set architectures while maintaining the original semantics. However, the development and maintenance of an efficient cross-DBT system are challenging. Key challenges include memory access overhead, inefficient instruction simulation, and frequent context switches. In this paper, we propose three novel optimization techniques. First, we formalize a register mapping cost model and investigate a hierarchical register mapping approach to bridge the memory access overhead. Second, we accelerate floating point (FP) emulation by surrounding the use of hardware FP unit with high-efficiency non-FP code. Third, we present a function inlining approach to alleviating the overhead associated with indirect control lookup. On the system side, we implement our approach on ARM64 and SW64 architectures based on QEMU and extensively evaluate the effectiveness with the SPEC2006 benchmark suite. The experimental results show that an average of 1.28× performance speedup and 13.41% code size reduction can be achieved on SW64. Similarly, on ARM64, we achieve an average of 1.15× performance speedup and 11.48% code size reduction. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Circuits, Systems & Computers. 2024/04, Vol. 33, Issue 6, p1
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2024
  • ISSN:0218-1266
  • DOI:10.1142/S0218126624501044
  • Accession Number:176387561
  • Copyright Statement:Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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