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Continuous whole-system monitoring toward rapid understanding of production HPC applications and systems

Parallel Computing

Agelastos, Anthony M.; Allan, Benjamin A.; Brandt, James M.; Gentile, Ann C.; Lefantzi, Sophia L.; Monk, Stephen T.; Ogden, Jeffry B.; Rajan, Mahesh R.; Stevenson, Joel O.

A detailed understanding of HPC applications’ resource needs and their complex interactions with each other and HPC platform resources are critical to achieving scalability and performance. Such understanding has been difficult to achieve because typical application profiling tools do not capture the behaviors of codes under the potentially wide spectrum of actual production conditions and because typical monitoring tools do not capture system resource usage information with high enough fidelity to gain sufficient insight into application performance and demands. In this paper we present both system and application profiling results based on data obtained through synchronized system wide monitoring on a production HPC cluster at Sandia National Laboratories (SNL). We demonstrate analytic and visualization techniques that we are using to characterize application and system resource usage under production conditions for better understanding of application resource needs. Our goals are to improve application performance (through understanding application-to-resource mapping and system throughput) and to ensure that future system capabilities match their intended workloads.

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TYPE Journal Article YEAR 2016
ScopusOSTIDOI

Performance Efficiency and Effectivness of Supercomputers

Leland, Robert; Rajan, Mahesh R.; Heroux, Michael A.

Our first purpose here is to offer to a general technical and policy audience a perspective on whether the supercomputing community should focus on improving the efficiency of supercomputing systems and their use rather than on building larger and ostensibly more capable systems that are used at low efficiency. After first summarizing our content and defining some necessary terms, we give a concise answer to this question. We then set this in context by characterizing performance of current supercomputing systems on a variety of benchmark problems and actual problems drawn from workloads in the national security, industrial, and scientific context. Along the way we answer some related questions, identify some important technological trends, and offer a perspective on the significance of these trends. Our second purpose is to give a reasonably broad and transparent overview of the related issue space and thereby to better equip the reader to evaluate commentary and controversy concerning supercomputing performance. For example, questions repeatedly arise concerning the Linpack benchmark and its predictive power, so we consider this in moderate depth as an example. We also characterize benchmark and application performance for scientific and engineering use of supercomputers and offer some guidance on how to think about these. Examples here are drawn from traditional scientific computing. Other problem domains, for example, data analytics, have different performance characteristics that are better captured by different benchmark problems or applications, but the story in those domains is similar in character and leads to similar conclusions with regard to the motivating question. For more on this topic, see Large-Scale Data Analytics and Its Relationship to Simulation. 1 Director, Computing Research Center, Sandia National Laboratories 2 Distinguished Member of the Technical Staff, Sandia National Laboratories 3 Distinguished Member of the Technical Staff, Sandia National Laboratories 4 Distinguished Member of the Technical Staff , Sandia National Laboratories

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TYPE SAND Report YEAR 2016
OSTIDOI

Toward rapid understanding of production HPC applications and systems

Proceedings - IEEE International Conference on Cluster Computing, ICCC

Agelastos, Anthony M.; Allan, Benjamin A.; Brandt, James M.; Gentile, Ann C.; Lefantzi, Sophia L.; Monk, Stephen T.; Ogden, Jeffry B.; Rajan, Mahesh R.; Stevenson, Joel O.

A detailed understanding of HPC application's resource needs and their complex interactions with each other and HPC platform resources is critical to achieving scalability and performance. Such understanding has been difficult to achieve because typical application profiling tools do not capture the behaviors of codes under the potentially wide spectrum of actual production conditions and because typical monitoring tools do not capture system resource usage information with high enough fidelity to gain sufficient insight into application performance and demands. In this paper we present both system and application profiling results based on data obtained through synchronized system wide monitoring on a production HPC cluster at Sandia National Laboratories (SNL). We demonstrate analytic and visualization techniques that we are using to characterize application and system resource usage under production conditions for better understanding of application resource needs. Our goals are to improve application performance (through understanding application-to-resource mapping and system throughput) and to ensure that future system capabilities match their intended workloads.

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TYPE Conference Poster YEAR 2015
ScopusOSTI

ASC Trilab L2 Codesign Milestone 2015

Trott, Christian R.; Hammond, Simon D.; Dinge, Dennis D.; Lin, Paul L.; Vaughan, Courtenay T.; Cook, Jeanine C.; Rajan, Mahesh R.; Edwards, Harold C.; Hoekstra, Robert J.

For the FY15 ASC L2 Trilab Codesign milestone Sandia National Laboratories performed two main studies. The first study investigated three topics (performance, cross-platform portability and programmer productivity) when using OpenMP directives and the RAJA and Kokkos programming models available from LLNL and SNL respectively. The focus of this first study was the LULESH mini-application developed and maintained by LLNL. In the coming sections of the report the reader will find performance comparisons (and a demonstration of portability) for a variety of mini-application implementations produced during this study with varying levels of optimization. Of note is that the implementations utilized including optimizations across a number of programming models to help ensure claims that Kokkos can provide native-class application performance are valid. The second study performed during FY15 is a performance assessment of the MiniAero mini-application developed by Sandia. This mini-application was developed by the SIERRA Thermal-Fluid team at Sandia for the purposes of learning the Kokkos programming model and so is available in only a single implementation. For this report we studied its performance and scaling on a number of machines with the intent of providing insight into potential performance issues that may be experienced when similar algorithms are deployed on the forthcoming Trinity ASC ATS platform.

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TYPE SAND Report YEAR 2015
OSTIDOI
Results 1–25 of 64
Results 1–25 of 64