Monitoring HPC systems yields valuable insights into user behavior, aiding resource management, collaborative research, and software design. However, privacy concerns raise the barrier for real-world HPC trace sharing between HPC facilities and researchers. Traditional anonymization methods fall short as user behavior remains identifiable. To address this, we propose a robust toolset for privacy protection of HPC traces using Differential Privacy (DP). Our toolset offers a set of DP algorithms, metrics, and visualizations to empower HPC operators to protect users' sensitive information under a privacy protection guarantee. We evaluated our toolset over real HPC systems traces for different parameters and data aggregations. Moreover, we show that machine learning models trained on privacy-preserved logs maintain accuracy compared to real data, which supports data publishing and sharing across different computing facilities.

This paper describes the deployment and operational experience of a novel incentive-based power-control strategy on the Fugaku supercomputer. Our incentive-based program, termed Fugaku Points, provides knobs to users to apply power control functions to improve the overall power efficiency of the supercomputer toward achieving HPC sustainability in terms of its environmental implications. We also discuss new operational opportunities, challenges, and future directions.

Periodic capture of comprehensive, usable I/O performance data for scientific applications requires an easy-to-use technique to record information throughout the execution without causing substantial performance effects. In this paper, we introduce a unique framework that provides low latency monitoring of I/O event data during run time. We implement a system-level infrastructure that continuously collects I/O application data from an existing I/O characterization tool to enable insights into the I/O application behavior and the components affecting it through analyses and visualizations. In this effort, we evaluate our framework by analyzing sampled I/O data captured from two HPC benchmark applications to understand the I/O behavior during the execution life of the applications. The result shows the utility of capturing I/O application performance and behavior.