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- W4286981036 abstract "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." @default.
- W4286981036 created "2022-07-25" @default.
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- W4286981036 date "2021-09-10" @default.
- W4286981036 modified "2023-09-27" @default.
- W4286981036 title "An Execution Fingerprint Dictionary for HPC Application Recognition" @default.
- W4286981036 doi "https://doi.org/10.48550/arxiv.2109.04766" @default.
- W4286981036 hasPublicationYear "2021" @default.
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