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1. Lightweight Measurement and Analysis of HPC Performance Variability

   https://doi.org/10.1109/PMBS51919.2020.00011  

   Dominguez-Trujillo, Jered; Haskins, Keira; Khouzani, Soheila Jafari; Leap, Christopher; Tashakkori, Sahba; Wofford, Quincy; Estrada, Trilce; Bridges, Patrick G.; Widener, Patrick M. (November 2020, 2020 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS))

   null (Ed.)

   Performance variation deriving from hardware and software sources is common in modern scientific and data-intensive computing systems, and synchronization in parallel and distributed programs often exacerbates their impacts at scale. The decentralized and emergent effects of such variation are, unfortunately, also difficult to systematically measure, analyze, and predict; modeling assumptions which are stringent enough to make analysis tractable frequently cannot be guaranteed at meaningful application scales, and longitudinal methods at such scales can require the capture and manipulation of impractically large amounts of data. This paper describes a new, scalable, and statistically robust approach for effective modeling, measurement, and analysis of large-scale performance variation in HPC systems. Our approach avoids the need to reason about complex distributions of runtimes among large numbers of individual application processes by focusing instead on the maximum length of distributed workload intervals. We describe this approach and its implementation in MPI which makes it applicable to a diverse set of HPC workloads. We also present evaluations of these techniques for quantifying and predicting performance variation carried out on large-scale computing systems, and discuss the strengths and limitations of the underlying modeling assumptions. 

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2. Monitoring and Analysis of Power Consumption on HPC Clusters using XDMoD

   https://doi.org/10.1145/3311790.3396624  

   White, Joseph P.; Innus, Martins; Deleon, Robert L.; Jones, Matthew D.; Furlani, Thomas R. (July 2020, Proceedings of the Practice and Experience in Advanced Research Computing, ser PEARC '20)
3. Continuous HPC Performance Monitoring: Can It Run Without Affecting User Jobs?

   https://doi.org/10.1145/3708035.3736036  

   Joseph, Jyothismaria; Simakov, Nikolay A (July 2025, ACM)

   High-performance computing (HPC) resources are used in a wide range of scientic and engineering calculations. These resources have high initial and running costs. Thus, their optimal performance is crucial. There are a number of strategies to ensure the optimal state. One of them is continuous performance monitoring, where a set of applications and input parameters are executed regularly to identify performance issues proactively. Some sites hesitate to use such a strategy as it takes away the CPU cycles from actual users. The goal of this work is to identify node availability, both size- and time-wise, on busy HPC systems. Such availability spots can be used to tailor test jobs to minimize user impact. Two systems were analyzed: small - 118 nodes from the Center for Computational Research at the University at Bualo and large - 1,160 nodes from the Texas Advanced Computing Center. It was found that for days with 90% utilization and above, there are plenty of opportunities for test jobs. For example, on a small cluster, 8 nodes for 30 minutes are available for an average of 2.3 hours throughout the day. That is 9.6% of the day the scheduler has the opportunity to schedule such a job. On a large system, 32 nodes for 30 minutes were available on average 9.2 hours a day (or 38% of day). Thus, there is a space for test jobs, but it is not evident that the scheduler can benet from it, and a proper strategy must be used, for example, by lowering test job priorities. 

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4. Surface temperature monitoring in liver procurement via functional variance change-point analysis

   https://doi.org/10.1214/19-AOAS1297  

   Gao, Zhenguo; Du, Pang; Jin, Ran; Robertson, John L. (March 2020, The Annals of Applied Statistics)
5. Workflows for Performance Predictable and Reproducible HPC Applications

   https://doi.org/10.1109/CLUSTER.2019.8891043  

   Haskins, Keira; Wofford, Quincy; Bridges, Patrick G. (September 2019, 2019 IEEE International Conference on Cluster Computing (CLUSTER))

   null (Ed.)

   This poster presents an HPC application workflow system whose goal is to provide verifiably-reproducible HPC application performance. This system combines existing container, experiment, and data management techniques with HPC performance models, allowing it to both maximize performance reproducibility and inform users when application performance deviates from what should be expected even when running at scales or for lengths of time at which the application had never run. 

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