VClinic: A Portable and Efficient Framework for Fine-Grained Value Profilers
Xin You, Hailong Yang, Kelun Lei, Zhongzhi Luan, Depei Qian · 2023
Fine-grained value profilers reveal a promising way to accurately detect value-related software inefficiencies with binary instrumentation. Due to the architecture-dependent implementation details of binary instrumentation, existing value profilers suffer from poor portability as well as high engineering efforts to achieve efficiency across platforms. In this paper, we propose VClinic, a portable and efficient fine-grained value profiling framework for analyzing highly optimized binaries on both X86 and ARM platforms. VClinic exploits operand-centric two-level designs in its implementation to provide the common building blocks required for value profilers. By constructing four representative value profilers with VClinic, we demonstrate that VClinic can ease the development of value profilers with portability and efficiency across platforms. Guided by the value profilers built upon VClinic, we can achieve up to 89.94% and 74.66% speedup for real-world programs on X86 and ARM platforms, respectively.