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.

翻译：在高聚苯乙烯系统上运行的应用如果:(a) 资源使用效率低,(b) 偏离分配目的(例如加密货币开采),或(c) 遇到错误和失败;重要的是,要知道系统上运行的应用程序、如何使用系统,以及它们是否以前已经执行。要确认在噪音系统中执行过程中已知的应用,我们从Shazam对已知歌曲在拥挤的酒吧中演奏的方式中汲取灵感。我们的贡献是执行指纹词典(EFD),该词典储存与应用和输入大小信息(价值)相联系的系统计量(钥匙)执行指纹,作为关键价值识别对,相关工作往往依靠广泛的系统监测(在大时间窗口中收集的多项系统计量),并使用机器学习方法确定应用程序。我们的解决办法仅使用最初的2分钟和单一的系统度量来达到95%以上的F-核心,为相关工作提供可比的结果,但有一部分必要的数据和直截的识别机制。