Abstract
Keeping energy costs in budget and operating within available capacities of power distribution and cooling systems is becoming an important requirement for High Performance Computing HPC data centers. It is even more important when considering the estimated power requirements for Exascale computing. Power and energy capping are two of emerging techniques aimed towards controlling and efficient budgeting of power and energy consumption within the data center. Implementation of both techniques requires a knowledge of, potentially unknown, power and energy consumption data of the given parallel HPC applications for different numbers of compute servers nodes.This paper introduces an Adaptive Energy and Power Consumption Prediction AEPCP model capable of predicting the power and energy consumption of parallel HPC applications for different number of compute nodes. The suggested model is application specific and describes the behavior of power and energy with respect to the number of utilized compute nodes, taking as an input the available history power/energy data of an application. It provides a generic solution that can be used for each application but it produces an application specific result. The AEPCP model allows for ahead of time power and energy consumption prediction and adapts with each additional execution of the application improving the associated prediction accuracy. The model does not require any application code instrumentation and does not introduce any application performance degradation. Thus it is a high level application energy and power consumption prediction model. The validity and the applicability of the suggested AEPCP model is shown in this paper through the empirical results achieved using two application-benchmarks on the SuperMUC HPC system the 10th fastest supercomputer in the world, according to Top500 November 2013 rankings deployed at Leibniz Supercomputing Centre.
Highlights
With the ever increasing growth of applications requiring a scalable, reliable, and low cost access to high-end computing, many modern data centers have grown larger and denser making power consumption a dominating factor for the Total Cost of Ownership (TCO) of supercomputing sites [18, 19]
This paper proposes an Adaptive Energy and Power Consumption Prediction (AEPCP) model capable of predicting the Energy-to-Solution (EtS) [4, 22] and the Average Power Consumption (APC) [37] metrics for any parallel High Performance Computing (HPC) applications with respect to the given number of compute nodes
This indicates that the execution time of an application under weak scaling will show a constant behavior since the input problem size increases with the number of utilized compute nodes
Summary
With the ever increasing growth of applications requiring a scalable, reliable, and low cost access to high-end computing, many modern data centers have grown larger and denser making power consumption a dominating factor for the Total Cost of Ownership (TCO) of supercomputing sites [18, 19]. In order to determine whether the execution of job J is possible within the introduced average power consumption constraint, the information on the potential power consumption of job J with 270 compute nodes is required. Without this information the scheduling of job J could overload the available cooling capacity. [8, 13]) that implement power capping involve dynamic voltage frequency scaling [15], that will, in most cases, increase the runtime of the application [15], increasing the integral of power consumption over time (energy) Energy capping is another management technique that limits the amount of energy a system can consume when executing applications for a given time period.
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