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1. Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations

Author

Boito, Francieli, Brandt, Jim, Cardellini, Valeria, Carns, Philip, Ciorba, Florina M., Egan, Hilary, Eleliemy, Ahmed, Gentile, Ann, Gruber, Thomas, Hanson, Jeff, Haus, Utz-Uwe, Huck, Kevin, Ilsche, Thomas, Jakobsche, Thomas, Jones, Terry, Karlsson, Sven, Mueen, Abdullah, Ott, Michael, Patki, Tapasya, Peng, Ivy, Raghavan, Krishnan, Simms, Stephen, Shoga, Kathleen, Showerman, Michael, Tiwari, Devesh, Wilde, Torsten, and Yamamoto, Keiji

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Many High Performance Computing (HPC) facilities have developed and deployed frameworks in support of continuous monitoring and operational data analytics (MODA) to help improve efficiency and throughput. Because of the complexity and scale of systems and workflows and the need for low-latency response to address dynamic circumstances, automated feedback and response have the potential to be more effective than current human-in-the-loop approaches which are laborious and error prone. Progress has been limited, however, by factors such as the lack of infrastructure and feedback hooks, and successful deployment is often site- and case-specific. In this position paper we report on the outcomes and plans from a recent Dagstuhl Seminar, seeking to carve a path for community progress in the development of autonomous feedback loops for MODA, based on the established formalism of similar (MAPE-K) loops in autonomous computing and self-adaptive systems. By defining and developing such loops for significant cases experienced across HPC sites, we seek to extract commonalities and develop conventions that will facilitate interoperability and interchangeability with system hardware, software, and applications across different sites, and will motivate vendors and others to provide telemetry interfaces and feedback hooks to enable community development and pervasive deployment of MODA autonomy loops.

Published
2024

2. Application-aware Congestion Mitigation for High-Performance Computing Systems

Author

Patke, Archit, Jha, Saurabh, Qiu, Haoran, Brandt, Jim, Gentile, Ann, Greenseid, Joe, Kalbarczyk, Zbigniew, and Iyer, Ravishankar

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Networking and Internet Architecture
Abstract

High-performance computing (HPC) systems frequently experience congestion leading to significant application performance variation. However, the impact of congestion on application runtime differs from application to application depending on their network characteristics (such as bandwidth and latency requirements). We leverage this insight to develop Netscope, an automated ML-driven framework that considers those network characteristics to dynamically mitigate congestion. We evaluate Netscope on four Cray Aries systems, including a production supercomputer on real scientific applications. Netscope has a lower training cost and accurately estimates the impact of congestion on application runtime with a correlation between 0.7and 0.9 for common scientific applications. Moreover, we find that Netscope reduces tail runtime variability by up to 14.9 times while improving median system utility by 12%.

Published
2020

3. A Study of Network Congestion in Two Supercomputing High-Speed Interconnects

Author

Jha, Saurabh, Patke, Archit, Brandt, Jim, Gentile, Ann, Showerman, Mike, Roman, Eric, Kalbarczyk, Zbigniew T., Kramer, William T., and Iyer, Ravishankar K.

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Networking and Internet Architecture
Abstract

Network congestion in high-speed interconnects is a major source of application run time performance variation. Recent years have witnessed a surge of interest from both academia and industry in the development of novel approaches for congestion control at the network level and in application placement, mapping, and scheduling at the system-level. However, these studies are based on proxy applications and benchmarks that are not representative of field-congestion characteristics of high-speed interconnects. To address this gap, we present (a) an end-to-end framework for monitoring and analysis to support long-term field-congestion characterization studies, and (b) an empirical study of network congestion in petascale systems across two different interconnect technologies: (i) Cray Gemini, which uses a 3-D torus topology, and (ii) Cray Aries, which uses the DragonFly topology., Comment: Accepted for HOTI2019

Published
2019

4. Understanding Fault Scenarios and Impacts through Fault Injection Experiments in Cielo

Author

Formicola, Valerio, Jha, Saurabh, Chen, Daniel, Deng, Fei, Bonnie, Amanda, Mason, Mike, Brandt, Jim, Gentile, Ann, Kaplan, Larry, Repik, Jason, Enos, Jeremy, Showerman, Mike, Greiner, Annette, Kalbarczyk, Zbigniew, Iyer, Ravishankar K., and Krammer, Bill

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

We present a set of fault injection experiments performed on the ACES (LANL/SNL) Cray XE supercomputer Cielo. We use this experimental campaign to improve the understanding of failure causes and propagation that we observed in the field failure data analysis of NCSA's Blue Waters. We use the data collected from the logs and from network performance counter data 1) to characterize the fault-error-failure sequence and recovery mechanisms in the Gemini network and in the Cray compute nodes, 2) to understand the impact of failures on the system and the user applications at different scale, and 3) to identify and recreate fault scenarios that induce unrecoverable failures, in order to create new tests for system and application design. The faults were injected through special input commands to bring down network links, directional connections, nodes, and blades. We present extensions that will be needed to apply our methodologies of injection and analysis to the Cray XC (Aries) systems., Comment: Presented at Cray User Group 2017

Published
2019

5. Large-Scale System Monitoring Experiences and Recommendations Workshop paper: HPCMASPA 2018

Author

Ahlgren, Ville, Andersson, Stefan, Brandt, Jim, Cardo, Nicholas P, Chunduri, Sudheer, Enos, Jeremy, Fields, Parks, Gentile, Ann, Gerber, Richard, Gienger, Michael, Greenseid, Joe, Greiner, Annette, Hadri, Bilel, He, Yun, Hoppe, Dennis, Kaila, Urpo, Kelly, Kaki, Klein, Mark, Kristiansen, Alex, Leak, Steve, Mason, Mike, Pedretti, Kevin, Piccinali, Jean-Guillaume, Repik, Jason, Rogers, Jim, Salminen, Susanna, Showerman, Mike, Whitney, Cary, and Williams, Jim

Subjects
Distributed Computing and Systems Software, Information and Computing Sciences, HPC monitoring, monitoring architecture, system administration
Abstract

Monitoring of High Performance Computing (HPC) platforms is critical to successful operations, can provide insights into performance-impacting conditions, and can inform methodologies for improving science throughput. However, monitoring systems are not generally considered core capabilities in system requirements specifications nor in vendor development strategies. In this paper we present work performed at a number of large-scale HPC sites towards developing monitoring capabilities that fill current gaps in ease of problem identification and root cause discovery. We also present our collective views, based on the experiences presented, on needs and requirements for enabling development by vendors or users of effective sharable end-to-end monitoring capabilities.

Published
2018

6. E2EWatch: An End-to-End Anomaly Diagnosis Framework for Production HPC Systems

Author

Aksar, Burak, Schwaller, Benjamin, Aaziz, Omar, Leung, Vitus J., Brandt, Jim, Egele, Manuel, Coskun, Ayse K., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sousa, Leonel, editor, Roma, Nuno, editor, and Tomás, Pedro, editor

Published
2021
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7. Proctor: A Semi-Supervised Performance Anomaly Diagnosis Framework for Production HPC Systems

Author

Aksar, Burak, Zhang, Yijia, Ates, Emre, Schwaller, Benjamin, Aaziz, Omar, Leung, Vitus J., Brandt, Jim, Egele, Manuel, Coskun, Ayse K., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Chamberlain, Bradford L., editor, Varbanescu, Ana-Lucia, editor, Ltaief, Hatem, editor, and Luszczek, Piotr, editor

Published
2021
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8. ALAMO: Autonomous Lightweight Allocation, Management, and Optimization

Author

Brightwell, Ron, Ferreira, Kurt B., Grant, Ryan E., Levy, Scott, Lofstead, Jay, Olivier, Stephen L., Pedretti, Kevin T., Younge, Andrew J., Gentile, Ann, Brandt, Jim, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Nichols, Jeffrey, editor, Verastegui, Becky, editor, Maccabe, Arthur ‘Barney’, editor, Hernandez, Oscar, editor, Parete-Koon, Suzanne, editor, and Ahearn, Theresa, editor

Published
2020
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10. Taxonomist: Application Detection Through Rich Monitoring Data

Author

Ates, Emre, Tuncer, Ozan, Turk, Ata, Leung, Vitus J., Brandt, Jim, Egele, Manuel, Coskun, Ayse K., Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Aldinucci, Marco, editor, Padovani, Luca, editor, and Torquati, Massimo, editor

Published
2018
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11. Diagnosing Performance Variations in HPC Applications Using Machine Learning

Author

Tuncer, Ozan, Ates, Emre, Zhang, Yijia, Turk, Ata, Brandt, Jim, Leung, Vitus J., Egele, Manuel, Coskun, Ayse K., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Kunkel, Julian M., editor, Yokota, Rio, editor, Balaji, Pavan, editor, and Keyes, David, editor

Published
2017
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12. Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations

Author

Boito, Francieli, primary, Brandt, Jim, additional, Cardellini, Valeria, additional, Carns, Philip, additional, Ciorba, Florina M., additional, Egan, Hilary, additional, Eleliemy, Ahmed, additional, Gentile, Ann, additional, Gruber, Thomas, additional, Hanson, Jeff, additional, Haus, Utz-Uwe, additional, Huck, Kevin, additional, Ilsche, Thomas, additional, Jakobsche, Thomas, additional, Jones, Terry, additional, Karlsson, Sven, additional, Mueen, Abdullah, additional, Ott, Michael, additional, Patki, Tapasya, additional, Peng, Ivy, additional, Raghavan, Krishnan, additional, Simms, Stephen, additional, Shoga, Kathleen, additional, Showerman, Michael, additional, Tiwari, Devesh, additional, Wilde, Torsten, additional, and Yamamoto, Keiji, additional

Published
2023
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15. Mathematicians' and Math Educators' Views on 'Doing Mathematics'

Author

Brandt, Jim, Lunt, Jana, and Meilstrup, Gretchen Rimmasch

Abstract

Educators often argue that mathematics should be taught so that the students in the course are actually "doing mathematics." Is there a consensus among mathematicians and mathematics educators as to the meaning of "doing mathematics?" In an effort to answer this question, we administered a survey to hundreds of university-level mathematics and mathematics education faculty members. Participants ranked the importance of various mathematical activities and also responded to several open-ended questions. Responses to the open-ended questions were analyzed qualitatively to identify patterns. In this paper, we discuss the patterns we observed in analyzing the survey data, with a particular focus on the similarities and differences between mathematicians and mathematics educators.

Published
2016
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16. Driving HPC Operations With Holistic Monitoring and Operational Data Analytics (Dagstuhl Seminar 23171)

Author

Jim Brandt and Florina Ciorba and Ann Gentile and Michael Ott and Torsten Wilde, Brandt, Jim, Ciorba, Florina, Gentile, Ann, Ott, Michael, Wilde, Torsten, Jim Brandt and Florina Ciorba and Ann Gentile and Michael Ott and Torsten Wilde, Brandt, Jim, Ciorba, Florina, Gentile, Ann, Ott, Michael, and Wilde, Torsten

Abstract

Advances in analytic approaches have brought the vision of efficient High Performance Computing (HPC) operations enabled by dynamic analysis driving automated feedback and adaptation within reach. Many HPC centers have started the development and deployment of frameworks to enable continuous and holistic monitoring, archiving, and analysis of performance data from their production machines and related infrastructures. The impact of such frameworks rests upon the ability to effectively analyze such data and to take action based on analysis results. Analytic techniques have been successfully developed and applied in other domains but their features may not apply directly to HPC operations data and situations. Response options are limited in HPC implementations. Leveraging, adapting, and extending analysis techniques and response options would open up new avenues for research and development of actionable analytics that can drive more intelligent operations through both manual and automated response to conditions of interest. This Dagstuhl Seminar 23171 brought together practitioners and researchers in the areas of HPC system management and monitoring, analytics, and computer science to collaboratively work on developing community solutions for revolutionizing HPC system operations. The topics discussed in this seminar spanned use cases, data and analytic approaches required to address the use cases, use of analysis results to improve performance and operations, and research in the development and use of autonomous feedback loops.

Published
2023
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18. Runtime Performance Anomaly Diagnosis in Production HPC Systems Using Active Learning

Author

Aksar, Burak, Sencan, Efe, Schwaller, Benjamin, Aaziz, Omar, Leung, Vitus J., Brandt, Jim, Kulis, Brian, Egele, Manuel, and Coskun, Ayse K.

Abstract

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.

Published
2024
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27. HPAS

Author

Ates, Emre, primary, Zhang, Yijia, additional, Aksar, Burak, additional, Brandt, Jim, additional, Leung, Vitus J., additional, Egele, Manuel, additional, and Coskun, Ayse K., additional

Published
2019
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33. Large-Scale System Monitoring Experiences and Recommendations

Author

Ahlgren, Ville, primary, Andersson, Stefan, additional, Brandt, Jim, additional, Cardo, Nicholas, additional, Chunduri, Sudheer, additional, Enos, Jeremy, additional, Fields, Parks, additional, Gentile, Ann, additional, Gerber, Richard, additional, Gienger, Michael, additional, Greenseid, Joe, additional, Greiner, Annette, additional, Hadri, Bilel, additional, He, Yun, additional, Hoppe, Dennis, additional, Kaila, Urpo, additional, Kelly, Kaki, additional, Klein, Mark, additional, Kristiansen, Alex, additional, Leak, Steve, additional, Mason, Mike, additional, Pedretti, Kevin, additional, Piccinali, Jean-Guillaume, additional, Repik, Jason, additional, Rogers, Jim, additional, Salminen, Susanna, additional, Showerman, Mike, additional, Whitney, Cary, additional, and Williams, Jim, additional

Published
2018
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35. Holistic Measurement-Driven System Assessment

Author

Jha, Saurabh, primary, Brandt, Jim, additional, Gentile, Ann, additional, Kalbarczyk, Zbigniew, additional, Bauer, Greg, additional, Enos, Jeremy, additional, Showerman, Michael, additional, Kaplan, Larry, additional, Bode, Brett, additional, Greiner, Annette, additional, Bonnie, Amanda, additional, Mason, Mike, additional, Iyer, Ravishankar K., additional, and Kramer, William, additional

Published
2017
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37. HPCMASPA Introduction and Committees

Author

Allan, Benjamin, primary, Brandt, Jim, additional, Gentile, Ann, additional, Lueninghoener, Cory, additional, Naksinehaboon, Nichamon, additional, Norris, Boyana, additional, and Taerat, Narate, additional

Published
2016
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