SHF: Medium: Spectral Profiling: Understanding Software Performance without Code Instrumentation

SHF：中：频谱分析：无需代码检测即可了解软件性能

• 批准号: 1563991
• 负责人: Alessandro Orso
• 金额: $ 85万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1563991&HistoricalAwards=false
• 关键词: SHF; Medium; Spectral; Profiling; Understanding

Dynamic analyses such as profiling program execution are widely used because they can measure various aspects of the runtime behavior of a software system and have a wide range of applications in software engineering. These analyses are typically carried out by adding probes to the software, which imposes space/time overhead, is intrusive, and can negatively affect software behavior. To address these issues, we propose a novel approach that allows for analyzing software behavior accurately and non-intrusively by leveraging the electromagnetic emissions produced by a computer as it executes code. Our approach can collect runtime information about a software system by simply placing a device next to the system. It can thus not only enable profiling for a variety of software systems for which this was previously impossible (e.g., embedded systems), but also benefit dynamic analyses in more traditional contexts.This project will combine various machine learning and static analysis techniques to build likely electromagnetic signatures for different code patterns, investigate which code granularity provides the most accurate matching of electromagnetic emissions to code, and explore adaptive and hierarchical techniques for performing this matching at runtime. This research is inherently interdisciplinary and promises to break new ground and have broader impact in several combined areas, including software engineering, programming languages, computer architecture, and electromagnetics. Unlike previous work on electromagnetic emissions analysis, our approach will collect runtime information that is fine-grained enough to measure the execution of short sequences of statements, if not individual instructions. This will let us apply our approach to several software engineering tasks. In fact, if successful, this research will both provide a solid conceptual foundation, which other researchers will be able to leverage, and investigate a set of specific techniques and tools that build on this foundation to support tasks such as zero-overhead performance measurement, debugging, and anomaly detection.

诸如剖析程序执行的动态分析被广泛使用，因为它们可以测量软件系统的运行时行为的各个方面，并且在软件工程中具有广泛的应用。这些分析通常通过向软件添加探测器来执行，这会增加空间/时间开销，具有侵入性，并且会对软件行为产生负面影响。为了解决这些问题，我们提出了一种新的方法，允许分析软件行为准确和非侵入性地利用计算机执行代码时产生的电磁辐射。我们的方法可以通过简单地将设备放置在系统旁边来收集有关软件系统的运行时信息。因此，它不仅能够为以前不可能的各种软件系统（例如，这个项目将结合联合收割机各种机器学习和静态分析技术，为不同的代码模式构建可能的电磁签名，研究哪种代码粒度提供最准确的电磁辐射与代码匹配，并探索在运行时执行这种匹配的自适应和分层技术。这项研究本质上是跨学科的，有望开辟新天地，并在软件工程、编程语言、计算机体系结构和电磁学等多个综合领域产生更广泛的影响。与以前的工作电磁辐射分析，我们的方法将收集运行时信息，是细粒度的，足以衡量执行短序列的语句，如果不是个别指令。这将使我们能够将我们的方法应用于几个软件工程任务。事实上，如果成功的话，这项研究将提供一个坚实的概念基础，其他研究人员将能够利用这个基础，并研究一套特定的技术和工具，以支持零开销性能测量，调试和异常检测等任务。