Publications

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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.

Sandia has invested heavily in scientifc/engineering application development and in the research, development, and deployment of large scale HPC platforms to support the com- putational needs of these applications. As application developers continually expand the capabilities of their software and spend more time on performance tuning of applications for these platforms, HPC platform resources are at a premium as they are a heavily shared resource serving the varied needs of many users. To ensure that the HPC platform resources are being used efciently and perform as designed, it is necessary to obtain reliable data on resource utilization that will allow us to investigate the occurrence, severity, and causes of performance-afecting contention between applications. The work presented in this paper was an initial step to determine if resource contention can be understood and minimized through monitoring, modeling, planning and infrastructure. This paper describes the set of metric defnitions, identifed in this research, that can be used as meaningful and poten- tially actionable indicators of performance-afecting contention between applications. These metrics were verifed using the observed slowdown of IOR, IMB, and CTH in operating scenarios that forced contention. This paper also describes system/application monitoring activities that are critical to distilling vast amounts of data into quantities that hold the key to understanding for an application's performance under production conditions and that will ultimately aid in Sandia's eforts to succeed in extreme-scale computing.

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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Understanding how resources of High Performance Compute platforms are utilized by applications both individually and as a composite is key to application and platform performance. Typical system monitoring tools do not provide sufficient fidelity while application profiling tools do not capture the complex interplay between applications competing for shared resources. To gain new insights, monitoring tools must run continuously, system wide, at frequencies appropriate to the metrics of interest while having minimal impact on application performance. We introduce the Lightweight Distributed Metric Service for scalable, lightweight monitoring of large scale computing systems and applications. We describe issues and constraints guiding deployment in Sandia National Laboratories' capacity computing environment and on the National Center for Supercomputing Applications' Blue Waters platform including motivations, metrics of choice, and requirements relating to the scale and specialized nature of Blue Waters. We address monitoring overhead and impact on application performance and provide illustrative profiling results.

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The goal of the 7X performance testing was to assure Sandia National Laboratories, Cray Inc., and the Department of Energy that Red Storm would achieve its performance requirements which were defined as a comparison between ASCI Red and Red Storm. Our approach was to identify one or more problems for each application in the 7X suite, run those problems at multiple processor sizes in the capability computing range, and compare the results between ASCI Red and Red Storm. The first part of this report describes the two computer systems, the applications in the 7X suite, the test problems, and the results of the performance tests on ASCI Red and Red Storm. During the course of the testing on Red Storm, we had the opportunity to run the test problems in both single-core mode and dual-core mode and the second part of this report describes those results. Finally, we reflect on lessons learned in undertaking a major head-to-head benchmark comparison.

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