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    Wang Jun, Pang Jianmin, Fu Liguo, Yue Feng, Zhang Jiahao. An Efficient Feedback Static Binary Translator for Solving Indirect Branch[J]. Journal of Computer Research and Development, 2019, 56(4): 742-754. DOI: 10.7544/issn1000-1239.2019.20170412
    Citation: Wang Jun, Pang Jianmin, Fu Liguo, Yue Feng, Zhang Jiahao. An Efficient Feedback Static Binary Translator for Solving Indirect Branch[J]. Journal of Computer Research and Development, 2019, 56(4): 742-754. DOI: 10.7544/issn1000-1239.2019.20170412

    An Efficient Feedback Static Binary Translator for Solving Indirect Branch

    • In order to solve the problem of indirect branch efficiently in static binary translation, a feedback static binary translation method is proposed, with two-level address mapping table to realize the fast mapping of indirect branch target address. This method can solve the problem of less code optimization and more redundant code in existing linear traversal translation. Firstly, the two-level address mapping table is used to address the code location quickly, using array address to store the target platform code block address in the order of the source platform base block start address and using array index to save the index position of the basic block start address in array address. Then, the monitoring feedback mechanism is added to the target executable program to carry on the code discovery, and the uncertain indirect branch target address would be returned so that the source code can be divided to new basic blocks and re-translated. The feedback static binary translation framework FD-QEMU is implemented based on QEMU(quick emulator), an open source binary translator. As the experimental results on SPEC2006 and NBENCH show, compared with QEMU, the speedup ratio of FD-SQEMU (feedback static QEMU) is 3.97 and 6.94 times on average; compared with SQEMU, a static translator with all instructions’ address mapping originally proposed by our group, the average acceleration ratio of FD-SQEMU is 1.18 times, and the maximum speedup ratio is 1.36 times, which verifies the effectiveness of the framework and method proposed in this paper.
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