PerfDBT: Efficient Performance Regression Testing of Dynamic Binary Translation

Wu, Jin; Dong, Jian; Fang, Ruili; Wang, Wenwen; Zuo, Decheng

doi:10.1109/iccd50377.2020.00071

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…Many research studies on dynamic translation methods have been published [11][12][13][14][15][16][17]. Dynamic translation frameworks, such as QEMU [17], are widely used in simulators, virtual machines, and disassemblers, effectively meeting the requirements of cross-architecture software migration.…”

Section: Overviewmentioning

confidence: 99%

A Dynamic and Static Binary Translation Method Based on Branch Prediction

Sun,

Wu,

et al. 2023

Electronics

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Binary translation is an important technique for achieving cross-architecture software migration. However, mainstream dynamic binary translation frameworks, such as QEMU, often generate a large amount of redundant code, which degrades the efficiency of the target code. To this end, we propose a dynamic–static binary translation method based on branch prediction. It first identifies parts of translation blocks following static branch prediction techniques. Then it translates these translation blocks into less-redundant native code blocks by canonical static translation algorithms. Finally, it executes all code blocks that are translated either statically or dynamically by correctly maintaining and switching their running contexts. In order to correctly weave the two types of translation activities, the proposed method only translates the next translation block that is data-independent from the current one by the active variable analysis algorithm, and records and shares the intermediate states of the dynamic and static translation activities via a carefully designed data structure. In particular, a shadow register-based context recovery mechanism is proposed to correctly record the running context of static translation blocks, and to correctly recover the context for dynamically translating and running blocks that were not statically translated. We also designed an adaptive memory optimization mechanism to dynamically release the memory of the mispredicted translation blocks. We implemented a dynamic–static binary translation framework by extending QEMU, called BP-QEMU (QEMU with branch prediction). We evaluated the translation correctness of BP-QEMU using the testing programs for the ARM and PPC instruction sets from QEMU, and evaluated the performance of BP-QEMU using the CoreMark benchmark code. The experimental results show that BP-QEMU can translate the instructions from the ARM and PPC architectures correctly; moreover, the average execution efficiency of the CoreMark code on BP-QEMU improves by 13.3% compared to that of QEMU.

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