When Sampling Lies: Trustworthy Performance Profiling for Flat Workloads with Blink (Operational Systems)
Abstract
Performance optimization of mobile systems is critical for achieving responsive user experiences. However, accurately measuring the effects of such optimizations is challenging. We found that real-world mobile workloads exhibit a flat profile, invoking thousands of short-lived routines that contribute evenly to overall performance with no dominant bottlenecks. Sampling-based profilers such as perf struggle in this setting: skid, shadow effects, and incomplete function coverage can yield systematically incorrect results, not just high variance. We present Blink, a lightweight instrumentation framework that provides robust coverage for short-lived routines while maintaining low overhead. Blink inserts instrumentation into functions, enabling precise measurements. We show that Blink achieves 99.999% accuracy while incurring 1% overhead. Deployed in a variety of use cases at Huawei, Blink offers a practical and reliable alternative for mobile compiler performance analysis.