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

据报道，许多移动的应用程序要么具有较差的用户感知性能，例如响应时间长，要么具有显著的电池寿命影响。新手开发人员的编程错误已经成为此类非功能性问题的常见原因。例如，一种类型的非确定性错误是挂起错误，其可能导致应用具有软挂起，即，应用程序在有限但可感知的时间段内变得无响应。异常电池消耗（ABD）是另一种类型的软件错误，据报道，它困扰了大量的开发人员。ABD通常消耗不必要的高能量并导致不期望的电池消耗。该项目提出了一个基于性能计数器的软件错误检测和诊断框架-硬件寄存器被配置为监视选定的事件，如缓存未命中或上下文切换-用于移动的应用程序。所提出的框架执行相关性分析，以选择最相关的硬件事件为给定的挂起错误和ABD，以及其他非功能性错误，如内存膨胀，这是指一个移动的应用程序消耗显着更多的内存比必要的。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，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的应用程序的开发，为应用程序开发人员提供一个急需的工具，可以帮助他们确定其非功能性错误的根本原因，如挂起错误，ABD和内存膨胀。随着2020年初可用的移动的应用程序数量达到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2年。这些新手开发人员发布的新移动的应用程序可以创造性地帮助用户在日常生活中显着，但通常会产生糟糕的用户体验或短电池寿命。通过提出的基于计数器的错误检测框架，应用程序开发人员可以花更少的时间关注性能/能源问题，并更多地关注自己的业务逻辑，以提高生产力。此外，通过探索非功能性软件错误和性能计数器提供的硬件级信息之间的复杂关系，该项目中开发的算法和框架对所有计算机系统都有价值。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值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.