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.

This Innovative Practice Full Paper presents BlocklyPar, a set of three tutorial games to move from sequential to parallel programming using a block-based visual language. Block-based tutorial games are attractive tools for introducing programming to novices. A few of existing tools can express multiple tasks running at the same time, but none of them address parallel programming concepts and terms used in the field of parallel computing. Our tutorial games are targeted for first-year Computer Science students, as a resource to anticipate parallel computing using a self-taught approach with engaging challenges. The challenges involve university students' day-to-day tasks to make the games more meaningful for the audience, thus collaborating with the idea that everyday tasks can benefit from parallel approaches. The first game introduces the programming environment and the sequential blocks; the second introduces the concepts of tasks, resources allocation, and parallel task execution; and the third presents the concepts of computational load distribution and performance metrics for evaluating improvements in a parallel solution. The concepts are expressed through animation components and three new programming blocks. We have conducted preliminary tests with Computer Science students for evaluating the platform usage and parallel programming concepts assessed. The results suggest that the games contribute to the student's learning on parallelism as an extension of practicing sequential programming. It can also motivate students to design parallel solutions to explore today's multi-core and multiprocessor computers.