Mohammed El Mehdi Diouri

Solving Some Mysteries in Power Monitoring of Servers: Take Care of Your Wattmeters!

Large-scale distributed systems e.g., datacenters, HPC systems, clouds, large-scale networks, etc. consume and will consume enormous amounts of energy. Therefore, accurately monitoring the power and energy consumption of these systems is increasingly more unavoidable. The main novelty of this contribution is the analysis and evaluation of different external and internal power monitoring devices tested using two different computing systems, a server and a desktop machine. Furthermore, we also provide experimental results for a variety of benchmarks which exercise intensively the main components CPU, Memory, HDDs, and NICs of the target platforms to validate the accuracy of the equipment in terms of power dispersion and energy consumption. This paper highlights that external wattmeters do not offer the same measures as internal wattmeters. Thanks to the high sampling rate and to the different measured lines, the internal wattmeters allow an improved visualization of some power fluctuations. However, a high sampling rate is not always necessary to understand the evolution of the power consumption during the execution of a benchmark.

Energy estimation for MPI broadcasting algorithms in large scale HPC systems

Future supercomputers will gather hundreds of millions of communicating cores. The movement of data in such systems will be very energy consuming. We address in this paper the issue of energy consumption of data broadcasting in such large scale systems. To this end, we propose a framework to estimate the energy consumed by different MPI broadcasting algorithms for various execution settings. Validation results show that our estimations are highly accurate and allow to select the least consuming broadcasting algorithm.

Energy efficiency in high-performance computing with and without knowledge of applications and services

The constant demand of raw performance in high-performance computing (HPC) often leads to over-provisioning in high-performance systems which in turn can result in a colossal energy waste due to workload/application variation over time. Proposing energy efficient solutions in the context of large-scale HPC is a real, unavoidable challenge. This article explores two alternative approaches (with or without knowledge of applications and services) dealing with the same goal: reducing the energy usage of large-scale infrastructures which support HPC applications. This article describes the first approach, with knowledge of applications and services, which enables users to choose the less consuming implementation of services. Based on the energy consumption estimation of the different implementations (protocols) for each service, this approach is validated on the case of fault tolerance service in HPC. The ‘without knowledge’ approach allows some intelligent framework to observe the life of HPC systems and proposes some energy reduction schemes. This framework automatically estimates the energy consumption of the HPC system in order to apply power saving schemes. Both approaches are experimentally evaluated and analysed in terms of energy efficiency.

Towards a novel smart and energy-aware service-oriented manager for extreme-scale applications

Exascale supercomputers will gather hundreds of million cores. The main problem to take care for running applications on such platforms is energy consumption since it is one major limitation if we consider that the currently fastest supercomputer consumes more than 12MW for a maximum performance of 10PFlops. Besides, we also need to overcome important challenges related to fault tolerance and data management in such extreme-scale systems. Thus, we need to take into consideration these challenges from an energy consumption point of view and to propose them as energy-aware services for exascale applications. At this end, we propose in this paper to provide accurate estimations of the energy consumption due to these services and offer some green and energy efficient solutions, leading to a smart and energy-aware service-oriented manager for exascale applications.

Energy-Aware Checkpointing Strategies

Future extreme-scale supercomputers will gather several millions of cores. The main problem that we address in this chapter is the energy consumption of these systems. Fault-tolerant methods must be deployed in such extreme-scale systems and these methods have a dramatic impact on total energy consumption. Fault-tolerant protocols have different energy consumption rates, depending on parameters such as platform characteristics, application features, and number of processes used in the execution. Currently, in order to evaluate the power consumption of fault-tolerant protocols in a given execution context, the only approach is to run the application with the different versions of fault-tolerant protocols and to monitor energy consumption. In order to avoid this time and energy consuming process, we describe in this chapter a methodology to estimate the energy consumption of the fault-tolerant protocols used for HPC applications. This methodology relies on an energy calibration of the supercomputer and a user description of the execution setting. We evaluate the accuracy of the estimations with applications and scenarios executed on a real platform with energy consumption monitoring. Results show that the energy estimations provided before the execution are highly accurate, and allow users to select the less energy consuming fault-tolerant protocol without pre-running their applications.

Providing Green Services in HPC Data Centers: A Methodology Based on Energy Estimation

A supercomputer is an infrastructure built from an interconnection of computers capable of performing tasks in parallel in order to achieve very high performance. They are used in order to run scientific applications in various fields like the prediction of severe weather phenomena and seismic waves. To meet new scientific challenges, the HPC community has set a new performance objective for the end of the decade: Exascale. To achieve such performance (1018 FLoat Operations Per Second), an exascale supercomputer will gather several millions of CPU cores running up to a billion trends and will consume several megawatts. The energy consumption issue at the exascale becomes even more worrying when we know that we already reach energy consumptions higher than 17 MW at the petascale while the DARPA set to 20 MW the threshold for exascale supercomputers. Hence, these systems that will be 30 times more performant than the current systems have to achieve an energy efficiency of 50 gigaFLOPS per watt while the current ones achieve between 2 and 3 gigaFLOPS per watt. As a consequence, reducing the energy consumption of high-performance computing infrastructures is a major challenge for the next years in order to be able to move to the exascale era.

Towards an energy estimator for fault tolerance protocols

Checkpointing protocols have different energy consumption depending on parameters like application features and platform characteristics. To select a protocol for a given execution, we propose an energy estimator that relies on an energy calibration of the considered platform and a user description of the execution settings.