CNS Core: Small: Leveraging Hardware Counters to Improve the Performance and Energy Efficiency of Mobile Apps

据报道，许多移动应用程序要么用户感知的性能较差，例如响应时间较长，要么对电池寿命有重大影响。来自新手开发人员的编程错误已成为此类非功能性问题的常见原因。例如，一种不确定的错误是挂起错误，它可能会导致应用程序出现软挂起，即应用程序在有限但可感知的时间段内变得无响应。电池异常耗尽(ABD)是另一种类型的软件错误，据报道，它困扰着大量开发人员。ABD通常会消耗不必要的高电量，并导致不必要的电池耗尽。该项目提出了一个基于性能计数器的软件漏洞检测和诊断框架--性能计数器是指配置为监控选定事件的硬件寄存器，如缓存未命中或上下文切换。该框架执行相关性分析，以选择与给定挂起错误和ABD以及其他非功能错误(如内存膨胀)最相关的硬件事件，内存膨胀指的是移动应用程序消耗的内存明显多于必要的内存。然后将设计新的算法，利用性能计数器提供的细粒度和低开销硬件信息来帮助应用程序开发人员查明这些错误的根本原因。这个及时的项目的成功将极大地影响移动应用程序的开发，因为它为应用程序开发人员提供了一个迫切需要的工具，可以帮助他们识别无法运行的错误的根本原因，如挂起错误、ABD和内存膨胀。2020年初，可用的移动应用程序数量达到了893万个，最近的一项研究显示，据报道，40%的应用程序开发人员完全独立工作，26%的应用程序开发人员编写应用程序的经验不足两年。这些新手开发人员发布的新移动应用程序可以很有创意，在日常生活中对用户有很大帮助，但通常会带来糟糕的用户体验或较短的电池续航时间。使用建议的基于计数器的错误检测框架，应用程序开发人员可以减少关注性能/能源问题的时间，并将更多精力集中在自己的业务逻辑上，以提高工作效率。此外，通过探索非功能软件错误和性能计数器提供的硬件级别信息之间的复杂关联，该项目中开发的算法和框架对所有计算机系统一般都有价值。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Many mobile apps are reported to either have poor user-perceived performance, such as long response times, or significant battery lifetime impacts. Programming bugs from novice developers have become common reasons for such non-functional issues. For example, a type of non-deterministic bug is the hang bug, which may cause the app to have soft hangs, i.e., the app becomes unresponsive for a limited but perceivable period of time. Abnormal battery drain (ABD) is another type of software bug that has been reported to trouble a large number of developers. An ABD usually consumes an unnecessarily high amount of energy and causes undesired battery drain. This project proposes a software bug detection and diagnosis framework based on performance counters --hardware registers configured to monitor selected events like cache misses or context switches-- for mobile apps. The proposed framework performs correlation analysis to select the most relevant hardware events for given hang bugs and ABDs, as well as other non-functional bugs, such as memory bloat, which refers to a mobile app consuming significantly more memory than necessary. Novel algorithms will then be designed that leverage the fine-grained and low-overhead hardware information provided by performance counters to help app developers pinpoint the root causes of these bugs. The success of this timely project would greatly impact mobile app development by providing app developers a sorely needed tool that can help them identify the root causes of their non-functional bugs, such as hang bugs, ABD, and memory bloat. With the number of available mobile apps reaching a massive 8.93 million in early 2020, a recent study shows that 40% of app developers are reported to be working completely on their own, and 26% of app developers have less than 2 years of experience in writing apps. New mobile apps released from these novice developers can be creative and help users significantly in daily lives, but often yield poor user experiences or short battery lifetimes. With the proposed counter-based bug detection framework, app developers can spend less time concerned with performance/energy issues and focus more on their own business logic for higher productivity. In addition, the algorithms and framework developed in this project can be valuable to all computer systems in general, by exploring the intricate correlation between non-functional software bugs and hardware-level information provided by performance counters.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.