Doctor of Philosophy (Ph.D.)
Production software packages have become increasingly complex with millions of lines of code, sophisticated control and data flow, and references to a hierarchy of external libraries. This complexity often introduces performance inefficiencies across software stacks, making it practically impossible for users to pinpoint them manually. Performance profiling tools (a.k.a. profilers) abound in the tools community to aid software developers in understanding program behavior. Classical profiling techniques focus on identifying hotspots. The hotspot analysis is indispensable; however, it can hardly diagnose whether a resource is being used in a productive manner that contributes to the overall efficiency of a program. Consequently, a significant burden is on developers to make a judgment call on whether there is scope to optimize a hotspot. Derived metrics, e.g., cache miss ratio, offer slightly better intuition into hotspots but are still not panaceas. Hence, there is a need for profilers that investigate resource wastage instead of usage. To overcome the critical missing pieces in prior work and complement existing profilers, we propose novel fine- and coarse-grained profilers to pinpoint varieties of performance inefficiencies and provide optimization guidance for a wide range of software covering benchmarks, enterprise applications, and large-scale parallel applications running on supercomputers and data centers. Fine-grained profilers are indispensable to understand performance inefficiencies comprehensively. We propose a whole-program profiler called LoadSpy, which works on binary executables to detect and quantify wasteful memory operations in their context and scope. Our observation, which is justified by myriad case studies, is that wasteful memory operations are often an indicator of various forms of performance inefficiencies, such as suboptimal choices of algorithms or data structures, missed compiler optimizations, and developers’ inattention to performance. Guided by LoadSpy, we are able to optimize a large number of well-known benchmarks and real-world applications, yielding significant speedups. Despite deep performance insights offered by fine-grained profilers, the high overhead keeps them away from widespread adoption, particularly in production. By contrast, coarse-grained profilers introduce low overhead at the cost of poor performance insights. Hence, another research topic is how we benefit from both, that is, the combination of deep insights of fine-grained profilers and low overhead of coarse-grained ones. The first effort to do so is proposing a lightweight profiler called JXPerf. It abandons heavyweight instrumentation by combining hardware performance monitoring units and debug registers available in commodity CPUs to detect wasteful memory operations. Compared with LoadSpy, JXPerf reduces the runtime overhead from 10x to 7% on average. The lightweight nature makes it useful in production. Another effort is proposing a lightweight profiler called FVSampler, the first nonintrusive profiler to study function execution variance.
© The Author
Su, Pengfei, "Understanding Performance Inefficiencies In Native And Managed Languages" (2020). Dissertations, Theses, and Masters Projects. William & Mary. Paper 1616444357.