Synthesizing Mixed-Boolean Arithmetic (MBA) expressions from input-output examples is central to program deobfuscation and also useful for compiler optimization, reverse engineering, and cryptanalysis. Existing MBA synthesizers are typically CPU-based and scale poorly on large specifications or complex targets. Recent GPU-accelerated synthesis methods achieve large speedups in qualitative settings, but they depend on caching observationally equivalent candidates; this strategy breaks down for MBA because candidate outputs are quantitative bitvectors and the behavioral space is enormous. We present SIMBA (Synthesis of Mixed-Boolean Arithmetic), a GPU-accelerated MBA synthesizer built around cache-free bottom-up enumeration. SIMBA avoids language caches entirely and uses a GPU-oriented enumeration design that keeps work local and highly parallel. In experiments, SIMBA is substantially faster than prior MBA synthesis tools, handles larger specifications, and reaches expression sizes that existing methods fail to solve. These results establish cache-free GPU synthesis as a practical and scalable approach for quantitative domains, and identify it as a strong alternative to cache-centric designs.

翻译：根据输入输出示例合成混合布尔算术表达式是程序反混淆的核心任务，同时对编译器优化、逆向工程和密码分析也具有重要意义。现有的MBA合成器通常基于CPU实现，在处理大规模规格或复杂目标时扩展性较差。近期GPU加速的合成方法在定性场景中实现了大幅加速，但其依赖于缓存观察等价候选方案；这种策略在MBA中失效，因为候选输出是量化位向量且行为空间极为庞大。我们提出SIMBA（混合布尔算术合成），这是一种基于无缓存自底向上枚举的GPU加速MBA合成器。SIMBA完全避免语言缓存，采用面向GPU的枚举设计，将工作负载保持局部化与高度并行化。实验表明，SIMBA在速度上显著超越现有MBA合成工具，能处理更大规模的规格，并达到现有方法无法解决的表达式复杂度。这些结果确立了无缓存GPU合成在量化领域的实用性与可扩展性，并表明其可成为以缓存为中心的设计的强大替代方案。