As the complexity of real-world server applications continues to grow, performance optimizations for large-scale applications are becoming increasingly challenging. The success of online optimization offered by OCOLOS and Dynimize proves that binary rewriting based on edge profiling data can significantly accelerate these applications. However, no similar online binary optimizer is currently available on the AArch64 platform. In response to the growing adoption of the AArch64 platform, this article introduces AOBO, a fast-switching online binary optimizer specifically designed for AArch64. In addition to providing practical and efficient engineering support for AArch64-specific features, AOBO overcomes the challenge of lacking hardware counters for edge profiling on most commercially available AArch64 servers. In particular, AOBO embraces a novel edge weight estimation scheme to deliver more accurate edge estimation, which in turn allows AOBO’s binary rewriter to generate more efficient code. Furthermore, time spent on AOBO’s online code replacement stage is optimized to work at a subsecond level, thus enabling a fast switch from running the original binary to running the optimized one. We evaluate AOBO with CINT2017, GCC, MySQL and MongoDB, measuring the accuracy and coverage of the estimated edge weights, the performance improvements of the optimized binaries, and the online optimization cost. To make a fair comparison, we are using the performance data of the binaries generated by the default compilation scripts in the software packages as a baseline. Experimental data shows that AOBO can offer a more accurate edge weight estimation and generate binaries with superior performance. Furthermore, AOBO achieves online optimization with a very small overhead and significantly improves the performance of large-scale applications. Compared with the baselines, AOBO’s online optimization can achieve 24.7% and 31.11% performance improvement respectively for MySQL and MongoDB. Notably, application pause time is reduced from 1,599.8 milliseconds to 462.1 milliseconds for MySQL, and from 1,765.9 milliseconds to 507.1 milliseconds for MongoDB.

High-performance compilers play an important role in improving the run-time performance of a program, and it is hard and time-consuming to identify the key optimizations implemented in a high-performance compiler with traditional program analysis. In this paper, we propose a hotspot-driven semi-automated competitive analysis framework for identifying key optimizations through comparing the hotspot codes generated by any two different compilers. Our framework is platform-agnostic and works well on both AArch64 and X64 platforms, which automates the stages of hotspot detection and dynamic binary instrumentation only for selected hotspots. With the instrumented instruction characterization information, the framework users can analyze the binary code within a much smaller scope to explore practical optimizations implemented in any of the compilers compared. To demonstrate the effectiveness and practicality, we conduct experiments on SPECspeed 2017 Integer benchmarks(CINT2017) and their binaries generated by open-source GCC compiler versus proprietary Huawei BiSheng and Intel ICC compilers on AArch64 and X64 platforms respectively. Empirical studies show that our methods can identify several significant optimizations that have been implemented by proprietary compilers and as well can be implemented in open-source compilers. To Hangzhou Hongjun Microelectronics Technology(Hjmicro), the identified key optimizations shed great light on optimizing their GCC-based product compiler, which delivers 20.83% improvement for SPECrate 2017 Integer on AArch64 platform.