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.

With the increasing scale and complexity of High-Performance Computing (HPC) systems, performance variations in applications caused by anomalies have become significant bottlenecks in system health and operational efficiency. As we move towards exascale systems, these variations become more prominent due to the increased sharing of resources. Such variations lead to lower energy efficiency and higher operational costs. To mitigate these problems, one must quickly and accurately diagnose the root cause of the anomalies at scale. One way to evaluate system health and identify the underlying causes is by manually examining certain performance metrics in telemetry data or using rule-based methods. Due to the daily size of telemetry data reaching terabytes and the fact that the numeric telemetry data contains thousands of metrics, manual analysis of telemetry to diagnose problems becomes challenging. Given these limitations, Machine Learning (ML)-based approaches have been gaining popularity as they have been shown to be effective and practical in diagnosing previously encountered performance anomalies. One primary challenge for supervised ML models is that they require a significant amount of labeled samples during training. However, obtaining many labels for anomalies is extremely difficult and costly, considering anomalies occur infrequently and real-world numeric system telemetry data is hard to label since it contains thousands of metrics. This paper proposes a novel active learning-based framework that diagnoses performance anomalies (i.e., identifying the type of an anomaly) in HPC systems at runtime using significantly fewer labeled samples compared to state-of-the-art ML-based approaches. We show that the proposed framework achieves the same F1-score compared to a supervised approach using much fewer labeled samples (i.e., 16x fewer samples for achieving a 0.78 F1-score, 11x fewer samples for achieving a 0.82 F1-score), even when there are previously unseen applications and application inputs in the test dataset.

Performance variations caused by anomalies in modern High Performance Computing (HPC) systems lead to decreased efficiency, impaired application performance, and increased operational costs. While machine learning (ML)-based frameworks for automated anomaly detection (often based on time series telemetry data) are gaining popularity in the literature, practical deployment challenges are often overlooked. Some ML-based frameworks require extensive customization, while others need a rich set of labeled samples, none of which are feasible for a production HPC system.

Diagnosing causes of performance variations in High-Performance Computing (HPC) systems is a daunting chal-lenge due to the systems' scale and complexity. Variations in application performance result in premature job termination, lower energy efficiency, or wasted computing resources. One potential solution is manual root-cause analysis based on system telemetry data. However, this approach has become an increasingly time-consuming procedure as the process relies on human expertise and the size of telemetry data is voluminous. Recent research employs supervised machine learning (ML) models to diagnose previously encountered performance anomalies in compute nodes automatically. However, these models generally necessitate vast amounts of labeled samples that represent anomalous and healthy states of an application during training. The demand for labeled samples is constraining because gathering labeled samples is difficult and costly, especially considering anomalies that occur infrequently. This paper proposes a novel active learning-based framework that diagnoses previously encountered performance anomalies in HPC systems using significantly fewer labeled samples compared to state-of-the-art ML-based frameworks. Our framework combines an active learning-based query strategy and a supervised classifier to minimize the number of labeled samples required to achieve a target performance score. We evaluate our framework on a production HPC system and a testbed HPC cluster using real and proxy applications. We show that our framework, ALBADross, achieves a 0.95 Fl-score using 28x fewer labeled samples compared to a supervised approach with equal Fl-score, even when there are previously unseen applications and application inputs in the test dataset.

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.

Monitoring and analyzing I/O behaviors is critical to the efficient utilization of parallel storage systems. Unfortunately, with increasing I/O requirements and resource contention, I/O performance variability is becoming a significant concern. This paper investigates I/O behavior and performance variability on a large-scale high-performance computing (HPC) system using a novel methodology that identifies similarity across jobs from the same application leveraging an I/O characterization tool and then, detects potential I/O performance variability across jobs of the same application. We demonstrate and discuss how our unique methodology can be used to perform temporal and feature analyses to detect interesting I/O performance variability patterns in production HPC systems, and their implications for operating/managing large-scale systems.

We introduce a new HPC system high-speed network fabric production monitoring tool, the ibnet sampler plugin for LDMS version 4. Large-scale testing of this tool is our work in progress. When deployed appropriately, the ibnet sampler plugin can provide extensive counter data, at frequencies up to 1 Hz. This allows the LDMS monitoring system to be useful for tracking the impact of new network features on production systems. We present preliminary results concerning reliability, performance impact, and usability of the sampler.

The increasing complexity of High Performance Computing (HPC) systems has created a growing need for facilitating insight into system performance and utilization for administrators and users. The strides made in HPC system monitoring data collection have produced terabyte/day sized time-series data sets rich with critical information, but it is onerous to extract and construe meaningful information from these metrics. We have designed and developed an architecture that enables flexible, as-needed, run-time analysis and presentation capabilities for HPC monitoring data. Our architecture enables quick and efficient data filtration and analysis. Complex runtime or historical analyses can be expressed as Python-based computations. Results of analyses and a variety of HPC oriented summaries are displayed in a Grafana front-end interface. To demonstrate our architecture, we have deployed it in production for a 1500-node HPC system and have developed analyses and visualizations requested by system administrators, and later employed by users, to track key metrics about the cluster at a job, user, and system level. Our architecture is generic, applicable to any *-nix based system, and it is extensible to supporting multi-cluster HPC centers. We structure it with easily replaced modules that allow unique customization across clusters and centers. In this paper, we describe the data collection and storage infrastructure, the application created to query and analyze data from a custom database, and the visual displays created to provide clear insights into HPC system behavior.

Increasingly diverse architectures and operating systems continue to emerge in the HPC industry. As such, HPC centers are becoming more heterogeneous which introduces a variety of challenges for system administrators. Monitoring a wide array of different platforms by itself is difficult, but the problem compounds in an environment where new platforms are frequently added. Creating a standard monitoring environment across these platforms that allows for simple administration with minimal setup becomes necessary in such situations. This paper presents the solutions introduced in the HPC Development department at Sandia National Laboratories to meet these challenges. This includes our adoption of a multi-stage data-collection pipeline across our clusters that is implemented from the ground up with our Golden Image. We also discuss our infrastructure to support a heterogeneous environment and activities in progress to improve our center. These advances simplify system standup and make monitoring integration easier and faster for new systems which is necessary for our center's domain.