Lasagne: a static binary translator for weak memory model architectures

Abstract

The emergence of new architectures create a recurring challenge to ensure that existing programs still work on them. Manually porting legacy code is often impractical. Static binary translation (SBT) is a process where a program's binary is automatically translated from one architecture to another, while preserving their original semantics. However, these SBT tools have limited support to various advanced architectural features. Importantly, they are currently unable to translate concurrent binaries. The main challenge arises from the mismatches of the memory consistency model specified by the different architectures, especially when porting existing binaries to a weak memory model architecture.In this paper, we propose Lasagne, an end-to-end static binary translator with precise translation rules between x86 and Arm concurrency semantics. First, we propose a concurrency model for Lasagne's intermediate representation (IR) and formally proved mappings between the IR and the two architectures. The memory ordering is preserved by introducing fences in the translated code. Finally, we propose optimizations focused on raising the level of abstraction of memory address calculations and reducing the number of fences. Our evaluation shows that Lasagne reduces the number of fences by up to about 65%, with an average reduction of 45.5%, significantly reducing their runtime overhead.

Performance Improvements via Peephole Optimization in Dynamic Binary Translation

“…Peephole optimization is widely used in binary translation optimization [21,[26][27][28][29]. Bansal et al [21] utilized peephole optimization in rule-based binary translation.…”

“…Similarly, Sun et al [27] conducted peephole optimization on a static binary translator to eliminate redundant instructions. Rocha et al [28] integrated peephole optimization to optimize redundant memory access and fence instructions during the translation of concurrent programs. However, the above approaches primarily face static binary translation, where the impact of peephole optimization's overhead is not a significant concern, whereas our paper is implemented in a dynamic binary translation.…”

A Dynamic and Static Binary Translation Method Based on Branch Prediction

“…Code generated by static translation methods usually exhibits high-quality and high-execution efficiency. However, static translation methods require an independent interpreter to execute the statically translated code blocks and cannot address the issues of self-modifying code, code mining, and precise interruptions [5][6][7][8][9][10]. Dynamic translation methods translate and execute the target code at the same time, and the translation activity occupies the program's execution time.…”

“…We repeated this process five times to obtain five run scores in order to eliminate the influence of the host machine load. The value of the boost ratio is obtained from Equation (5).…”