Hqemu

Hong, Ding-Yong; Hsu, Chun-Chen; Yew, Pen-Chung; Wu, Jan‐Jan; Hsu, Wei-Chung; Liu, Pangfeng; Wang, Chien‐Min; Chung, Yeh‐Ching

doi:10.1145/2259016.2259030

Cited by 84 publications

(9 citation statements)

References 20 publications

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“…A prototype based on the proposed approach has been implemented using HQEMU [15], which is a retargetable DBT system based on QEMU [3]. HQEMU keeps the original translator in QEMU, i.e., Tiny Code Generator (TCG), to translate basic blocks.…”

Section: Methodsmentioning

confidence: 99%

“…Compared to [12], the performance overhead incurred by this approach is negligible because only a simple analysis is needed. A prototype based on such an approach has been implemented on an existing DBT system HQEMU [15], which is an improved QEMU that supports binary traces and uses the LLVM compiler as a backend for code optimization. Experimental results on a set of JavaScript applications show that the proposed approach can achieve a 1.24X performance speedup on average compared to the original HQEMU.…”

Section: Introductionmentioning

confidence: 99%

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Improving Dynamically-Generated Code Performance on Dynamic Binary Translators

et al. 2018

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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 dynamicallygenerated 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. CCS Concepts • Software and its engineering → Virtual machines; Just-in-time compilers; Dynamic compilers; Runtime environments; Retargetable compilers; Scripting languages; Software reverse engineering;

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Dynamically-Generated Code Performance on Dynamic Binary Translators

et al. 2018

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“…HQEMU [16] relies on LLVM JIT to generate host binary code. The translation from QEMU's TCG Ops to LLVM IRs is still primarily based on manually constructed translation rules.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Cross-ISA DBT Through Automatically Learned Translation Rules

WangWenwen¹,

McCamantStephen²,

ZhaiAntonia³

et al. 2018

SIGPLAN Not.

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This paper presents a novel approach for dynamic binary translation (DBT) to automatically learn translation rules from guest and host binaries compiled from the same source code. The learned translation rules are then verified via binary symbolic execution and used in an existing DBT system, QEMU, to generate more efficient host binary code. Experimental results on SPEC CINT2006 show that the average time of learning a translation rule is less than two seconds. With the rules learned from a collection of benchmark programs excluding the targeted program itself, an average 1.25X performance speedup over QEMU can be achieved for SPEC CINT2006. Moreover, the translation overhead introduced by this rule-based approach is very small even for shortrunning workloads.

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“…(1) Code reuse is usually the primary reason to use native code because software vendors can integrate applications with their existing low-level (C/C++) APIs, which have been fine-tuned and thoroughly tested, without developing a new one [Lee and Jeon 2010;Kurzyniec and Sunderam 2001]. For the Android applications to use those existing library functions, developers may include ARM binaries for the Dalvik Virtual Machine (DVM) to call such functions via JNI interface.…”

Section: Address Mappingmentioning

confidence: 99%

A Retargetable Static Binary Translator for the ARM Architecture

Shen

Hsu

Yang

2014

ACM Trans. Archit. Code Optim.

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Machines designed with new but incompatible Instruction Set Architecture (ISA) may lack proper applications. Binary translation can address this incompatibility by migrating applications from one legacy ISA to a new one, although binary translation has problems such as code discovery for variable-length ISA and code location issues for handling indirect branches. Dynamic Binary Translation (DBT) has been widely adopted for migrating applications since it avoids those problems. Static Binary Translation (SBT) is a less general solution and has not been actively researched. However, SBT performs more aggressive optimizations, which could yield more compact code and better code quality. Applications translated by SBT can consume less memory, processor cycles, and power than DBT and can be started more quickly. These advantages are even more critical for embedded systems than for general systems. In this article, we designed and implemented a new SBT tool, called LLBT, which translates ARM instructions into LLVM IRs and then retargets the LLVM IRs to various ISAs, including ×86, ×86-64, ARM, and MIPS. LLBT leverages two important functionalities from LLVM: comprehensive optimizations and retargetability. More importantly, LLBT solves the code discovery problem for ARM/Thumb binaries without resorting to interpretation. LLBT also effectively reduced the size of the address mapping table, making SBT a viable solution for embedded systems. Our experiments based on the EEMBC benchmark suite show that the LLBT-generated code can run more than 6× and 2.3× faster on average than emulation with QEMU and HQEMU, respectively.

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Hqemu

Cited by 84 publications

References 20 publications

Improving Dynamically-Generated Code Performance on Dynamic Binary Translators

Improving Dynamically-Generated Code Performance on Dynamic Binary Translators

Enhancing Cross-ISA DBT Through Automatically Learned Translation Rules

A Retargetable Static Binary Translator for the ARM Architecture

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