Scaler: Efficient and Effective Cross Flow Analysis
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866916449318076416 |
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| author | Steven Tang Xiang, Mingcan Wang, Yang Wu, Bo Chen, Jianjun Liu, Tongping |
| author_facet | Steven Tang Xiang, Mingcan Wang, Yang Wu, Bo Chen, Jianjun Liu, Tongping |
| contents | Performance analysis is challenging as different components (e.g.,different libraries, and applications) of a complex system can interact with each other. However, few existing tools focus on understanding such interactions. To bridge this gap, we propose a novel analysis method "Cross Flow Analysis (XFA)" that monitors the interactions/flows across these components. We also built the Scaler profiler that provides a holistic view of the time spent on each component (e.g., library or application) and every API inside each component. This paper proposes multiple new techniques, such as Universal Shadow Table, and Relation-Aware Data Folding. These techniques enable Scaler to achieve low runtime overhead, low memory overhead, and high profiling accuracy. Based on our extensive experimental results, Scaler detects multiple unknown performance issues inside widely-used applications, and therefore will be a useful complement to existing work.
The reproduction package including the source code, benchmarks, and evaluation scripts, can be found at https://doi.org/10.5281/zenodo.13336658. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00854 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scaler: Efficient and Effective Cross Flow Analysis Steven Tang Xiang, Mingcan Wang, Yang Wu, Bo Chen, Jianjun Liu, Tongping Performance Performance analysis is challenging as different components (e.g.,different libraries, and applications) of a complex system can interact with each other. However, few existing tools focus on understanding such interactions. To bridge this gap, we propose a novel analysis method "Cross Flow Analysis (XFA)" that monitors the interactions/flows across these components. We also built the Scaler profiler that provides a holistic view of the time spent on each component (e.g., library or application) and every API inside each component. This paper proposes multiple new techniques, such as Universal Shadow Table, and Relation-Aware Data Folding. These techniques enable Scaler to achieve low runtime overhead, low memory overhead, and high profiling accuracy. Based on our extensive experimental results, Scaler detects multiple unknown performance issues inside widely-used applications, and therefore will be a useful complement to existing work. The reproduction package including the source code, benchmarks, and evaluation scripts, can be found at https://doi.org/10.5281/zenodo.13336658. |
| title | Scaler: Efficient and Effective Cross Flow Analysis |
| topic | Performance |
| url | https://arxiv.org/abs/2409.00854 |