Publications

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Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia's Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia's ElectroMagnetic Plasma In Realistic Environments ( EMPIRE ) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia's HPC users.

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Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia’s Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia’s ElectroMagnetic Plasma In Realistic Environments (EMPIRE) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia’s HPC users.

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Performance variation diagnosis in High-Performance Computing (HPC) systems is a challenging problem due to the size and complexity of the systems. Application performance variation leads to premature termination of jobs, decreased energy efficiency, or wasted computing resources. Manual root-cause analysis of performance variation based on system telemetry has become an increasingly time-intensive process as it relies on human experts and the size of telemetry data has grown. Recent methods use supervised machine learning models to automatically diagnose previously encountered performance anomalies in compute nodes. However, supervised machine learning models require large labeled data sets for training. This labeled data requirement is restrictive for many real-world application domains, including HPC systems, because collecting labeled data is challenging and time-consuming, especially considering anomalies that sparsely occur. This paper proposes a novel semi-supervised framework that diagnoses previously encountered performance anomalies in HPC systems using a limited number of labeled data points, which is more suitable for production system deployment. Our framework first learns performance anomalies’ characteristics by using historical telemetry data in an unsupervised fashion. In the following process, we leverage supervised classifiers to identify anomaly types. While most semi-supervised approaches do not typically use anomalous samples, our framework takes advantage of a few labeled anomalous samples to classify anomaly types. We evaluate our framework on a production HPC system and on a testbed HPC cluster. We show that our proposed framework achieves 60% F1-score on average, outperforming state-of-the-art supervised methods by 11%, and maintains an average 0.06% anomaly miss rate.

On high-performance computing (HPC) systems, job allocation strategies control the placement of a job among available nodes. As the placement changes a job's communication performance, allocation can significantly affects execution times of many HPC applications. Existing allocation strategies typically make decisions based on resource limit, network topology, communication patterns, etc. However, system network performance at runtime is seldom consulted in allocation, even though it significantly affects job execution times.In this work, we demonstrate using monitoring data to improve HPC systems' performance by proposing a NetworkData-Driven (NeDD) job allocation framework, which monitors the network performance of an HPC system at runtime and allocates resources based on both network performance and job characteristics. NeDD characterizes system network performance by collecting the network traffic statistics on each router link, and it characterizes a job's sensitivity to network congestion by collecting Message Passing Interface (MPI) statistics. During allocation, NeDD pairs network-sensitive (network-insensitive) jobs with nodes whose parent routers have low (high) network traffic. Through experiments on a large HPC system, we demonstrate that NeDD reduces the execution time of parallel applications by 11% on average and up to 34%.

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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.

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.

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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.

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The high performance computing industry is undergoing a period of substantial change. Not least because of fabrication and lithographic challenges in the manufacturing of next-generation processors. As such challenges mount, the industry is looking to generate higher performance from additional functionality in the micro-architecture space as well as a greater emphasis on efficiency in the design of networkon-chip resources and memory subsystems. Such variation in design opens opportunities for new entrants in the data center and server markets where varying compute-to-memory ratios can present end users with more efficient node designs for particular workloads. In this paper we compare the recently released Marvell ThunderX2 Arm processor - arguably the first high-performance computing capable Arm design available in the marketplace. We perform a set of micro-benchmarking and mini-application evaluation on the ThunderX2 comparing it with Intel's Haswell and Skylake Xeon server parts commonly used in contemporary HPC designs. Our findings show that no one processor performs the best across all benchmarks, but that the ThunderX2 excels in areas demanding high memory bandwidth due to the provisioning of more memory channels in its design. We conclude that the ThunderX2 is a serious contender in the HPC server segment and has the potential to offer supercomputing sites with a viable high-performance alternative to existing designs from established industry players.

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