Abstract
The size and complexity of leading-edge high performance computing (HPC) systems and their electrical and cooling facilities have been continuously increasing over the years, following the increase in both their computational power and heat generation. Operational data analysis for monitoring the overall HPC system health and operational behavior has become highly important for a reliable and stable long-term operation as well as for operational optimizations. Operational log data collected from the HPC system and its facility can be composed by a wide range of information measured and sampled over time from different kind of sensors, resulting multivariate time-series log data. In our introduced visual analytics method, the HPC log data is represented as third-order tensor (3D array) data with three axes corresponding to time, space, and measured values. By applying multiple dimensionality reduction steps, characteristic time and space can be identified and be interactively selected for assisting the understanding of the HPC system state and operational behavior.
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This work was partially supported by JSPS KAKENHI (Grand Number 20H04194)
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Fujita, K., Sakamoto, N., Fujiwara, T., Nonaka, J., Tsukamoto, T. (2022). A Visual Analytics Method for Time-Series Log Data Using Multiple Dimensionality Reduction. In: Chang, BY., Choi, C. (eds) Methods and Applications for Modeling and Simulation of Complex Systems. AsiaSim 2021. Communications in Computer and Information Science, vol 1636. Springer, Singapore. https://doi.org/10.1007/978-981-19-6857-0_3
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DOI: https://doi.org/10.1007/978-981-19-6857-0_3
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