SC20 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Lightweight Measurement and Analysis of HPC Performance Variability


Workshop:PMBS20: The 11th International Workshop on Performance Modeling, Benchmarking and Simulation of High-Performance Computer Systems

Authors: Jered Dominguez-Trujillo, Keira Haskins, Soheila Jafari Khouzani, Christoper Leap, Sahba Tashakkori, Quincy Wofford, and Trilce Estrada (University of New Mexico); Patrick Widener (Sandia National Laboratories); and Patrick Bridges (University of New Mexico)


Abstract: 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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