scholarly journals Tools and Methods for Measuring and Tuning the Energy Efficiency of HPC Systems

2014 ◽  
Vol 22 (4) ◽  
pp. 273-283 ◽  
Author(s):  
Robert Schöne ◽  
Jan Treibig ◽  
Manuel F. Dolz ◽  
Carla Guillen ◽  
Carmen Navarrete ◽  
...  

Energy costs nowadays represent a significant share of the total costs of ownership of High Performance Computing (HPC) systems. In this paper we provide an overview on different aspects of energy efficiency measurement and optimization. This includes metrics that define energy efficiency and a description of common power and energy measurement tools. We discuss performance measurement and analysis suites that use these tools and provide users the possibility to analyze energy efficiency weaknesses in their code. We also demonstrate how the obtained power and performance data can be used to locate inefficient resource usage or to create a model to predict optimal operation points. We further present interfaces in these suites that allow an automated tuning for energy efficiency and how these interfaces are used. We finally discuss how a hard power limit will change our view on energy efficient HPC in the future.

Computation ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 20 ◽  
Author(s):  
Enrico Calore ◽  
Alessandro Gabbana ◽  
Sebastiano Fabio Schifano ◽  
Raffaele Tripiccione

In the last years, the energy efficiency of HPC systems is increasingly becoming of paramount importance for environmental, technical, and economical reasons. Several projects have investigated the use of different processors and accelerators in the quest of building systems able to achieve high energy efficiency levels for data centers and HPC installations. In this context, Arm CPU architecture has received a lot of attention given its wide use in low-power and energy-limited applications, but server grade processors have appeared on the market just recently. In this study, we targeted the Marvell ThunderX2, one of the latest Arm-based processors developed to fit the requirements of high performance computing applications. Our interest is mainly focused on the assessment in the context of large HPC installations, and thus we evaluated both computing performance and energy efficiency, using the ERT benchmark and two HPC production ready applications. We finally compared the results with other processors commonly used in large parallel systems and highlight the characteristics of applications which could benefit from the ThunderX2 architecture, in terms of both computing performance and energy efficiency. Pursuing this aim, we also describe how ERT has been modified and optimized for ThunderX2, and how to monitor power drain while running applications on this processor.


Author(s):  
Pascal Bouvry ◽  
Ghislain Landry Tsafack Chetsa ◽  
Georges Da Costa ◽  
Emmanuel Jeannot ◽  
Laurent Lefèvre ◽  
...  

Author(s):  
Miguel Bordallo López

Computer vision can be used to increase the interactivity of existing and new camera-based applications. It can be used to build novel interaction methods and user interfaces. The computing and sensing needs of this kind of applications require a careful balance between quality and performance, a practical trade-off. This chapter shows the importance of using all the available resources to hide application latency and maximize computational throughput. The experience gained during the developing of interactive applications is utilized to characterize the constraints imposed by the mobile environment, discussing the most important design goals: high performance and low power consumption. In addition, this chapter discusses the use of heterogeneous computing via asymmetric multiprocessing to improve the throughput and energy efficiency of interactive vision-based applications.


2020 ◽  
Vol 92 (1) ◽  
pp. 517-527
Author(s):  
Timothy Clements ◽  
Marine A. Denolle

Abstract We introduce SeisNoise.jl, a library for high-performance ambient seismic noise cross correlation, written entirely in the computing language Julia. Julia is a new language, with syntax and a learning curve similar to MATLAB (see Data and Resources), R, or Python and performance close to Fortran or C. SeisNoise.jl is compatible with high-performance computing resources, using both the central processing unit and the graphic processing unit. SeisNoise.jl is a modular toolbox, giving researchers common tools and data structures to design custom ambient seismic cross-correlation workflows in Julia.


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