一种用于HPC应用识别的执行指纹字典
An Execution Fingerprint Dictionary for HPC Application Recognition
摘要：
在HPC系统上运行的应用程序会浪费时间和精力，如果它们：（a）低效地使用资源，（b）偏离分配目的（例如加密货币挖掘），或（c）遇到错误和故障。重要的是要知道哪些应用程序正在系统上运行，它们如何使用系统，以及它们以前是否被执行过。为了在嘈杂的系统上识别已知的应用程序，我们从Shazam识别拥挤的酒吧中播放的已知歌曲的方式中汲取灵感。我们的贡献是一个执行指纹字典（EFD），存储执行指纹的系统指标（键）链接到应用程序和输入大小信息（值）作为应用程序识别的键值对。相关工作通常依赖于广泛的系统监控（在大的时间窗口内收集的许多系统指标），并采用机器学习方法来识别应用程序。我们的解决方案仅使用前2分钟和单个系统指标来实现95%以上的F分数，提供与相关工作相当的结果，但只需要一小部分必要的数据和简单的识别机制。
英文摘要：
Applications running on HPC systems waste time and energy if they: (a) use resources inefficiently, (b) deviate from allocation purpose (e.g. cryptocurrency mining), or (c) encounter errors and failures. It is important to know which applications are running on the system, how they use the system, and whether they have been executed before. To recognize known applications during execution on a noisy system, we draw inspiration from the way Shazam recognizes known songs playing in a crowded bar. Our contribution is an Execution Fingerprint Dictionary (EFD) that stores execution fingerprints of system metrics (keys) linked to application and input size information (values) as key-value pairs for application recognition. Related work often relies on extensive system monitoring (many system metrics collected over large time windows) and employs machine learning methods to identify applications. Our solution only uses the first 2 minutes and a single system metric to achieve F-scores above 95 percent, providing comparable results to related work but with a fraction of the necessary data and a straightforward mechanism of recognition.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
