BSLD threshold driven power management policy for HPC centers

In this paper, we propose a power-aware parallel job scheduler assuming DVFS enabled clusters. A CPU frequency assignment algorithm is integrated into the well established EASY backfilling job scheduling policy. Running a job at lower frequency results in a reduction in power dissipation and accordingly in energy consumption. However, lower frequencies introduce a penalty in performance. Our frequency assignment algorithm has two adjustable parameters in order to enable fine grain energy-performance trade-off control. Furthermore, we have done an analysis of HPC system dimension. This paper investigates whether having more DVFS enabled processors for same load can lead to better energy efficiency and performance. Five workload traces from systems in production use with up to 9 216 processors are simulated to evaluate the proposed algorithm and the dimensioning problem. Our approach decreases CPU energy by 7%– 18% on average depending on allowed job performance penalty. Using the power-aware job scheduling for 20% larger system, CPU energy needed to execute same load can be decreased by almost 30% while having same or better job performance.Peer ReviewedPostprint (published version

BSLD threshold driven parallel job scheduling for energy efficient HPC centers

Recently, power awareness in high performance computing (HPC) community has increased significantly. While CPU power reduction of HPC applications using Dynamic Voltage Frequency Scaling (DVFS) has been explored thoroughly, CPU power management for large scale parallel systems at system level has left unexplored. In this paper we propose a power-aware parallel job scheduler assuming DVFS enabled clusters. Traditional parallel job schedulers determine when a job will be run, power aware ones should assign CPU frequency which it will be run at. We have introduced two adjustable thresholds in order to enable fine grain energy performance trade-off control. Since our power reduction approach is policy independent it can be added to any parallel job scheduling policy. Furthermore, we have done an analysis of HPC system dimension. Running an application at lower frequency on more processors can be more energy efficient than running it at the highest CPU frequency on less processors. This paper investigates whether having more DVFS enabled processors and same load can lead to better energy efficiency and performance. Five workload logs from systems in production use with up to 9 216 processors are simulated to evaluate the proposed algorithm and the dimensioning problem. Our approach decreases CPU energy by 7%- 18% on average depending on allowed job performance penalty. Applying the same frequency scaling algorithm on 20% larger system, CPU energy needed to execute same load can be decreased by almost 30% while having same or better job performance.Postprint (published version

DVFS power management in HPC systems

Recent increase in performance of High Performance Computing (HPC) systems has been followed by even higher increase in power consumption. Power draw of modern supercomputers leads to very high operating costs and reliability concerns. Furthermore, it has negative consequences on the environment. Accordingly, over the last decade there have been many works dealing with power/energy management in HPC systems. Since CPUs accounts for a high portion of the total system power consumption, our work aims at CPU power reduction. Dynamic Voltage Frequency Scaling (DVFS) is a widely used technique for CPU power management. Running an application at lower frequency/voltage reduces its power consumption. However, frequency scaling should be used carefully since it has negative effects on the application performance. We argue that the job scheduler level presents a good place for power management in an HPC center having in mind that a parallel job scheduler has a global overview of the entire system. In this thesis we propose power-aware parallel job scheduling policies where the scheduler determines the job CPU frequency, besides the job execution order. Based on the goal, the proposed policies can be classified into two groups: energy saving and power budgeting policies. The energy saving policies aim to reduce CPU energy consumption with a minimal job performance penalty. The first of the energy saving policies assigns the job frequency based on system utilization while the other makes job performance predictions. While for less loaded workloads these policies achieve energy savings, highly loaded workloads suffer from a substantial performance degradation because of higher job wait times due to an increase in load caused by longer job run times. Our results show higher potential of the DVFS technique when applied for power budgeting. The second group of policies are policies for power constrained systems. In contrast to the systems without a power limitation, in the case of a given power budget the DVFS technique even improves overall job performance reducing the average job wait time. This comes from a lower job power consumption that allows more jobs to run simultaneously. The first proposed policy from this group assigns CPU frequency using the job predicted performance and current power draw of already running jobs. The other power budgeting policy is based on an optimization problem which solution determines the job execution order, as well as power distribution among jobs selected for execution. This policy fully exploits available power and leads to further performance improvements. The last contribution of the thesis is an analysis of the DVFS technique potential for energyperformance trade-off in current and future HPC systems. Ongoing changes in technology decrease the DVFS applicability for energy savings but the technique still reduces power consumption making it useful for power constrained systems. In order to analyze DVFS potential, a model of frequency scaling impact on MPI application execution time has been proposed and validated against measurements on a large-scale system. This parametric analysis showed for which application/platform characteristic, frequency scaling leads to energy savings.El aumento de rendimiento que han experimentado los sistemas de altas prestaciones ha venido acompañado de un aumento aún mayor en el consumo de energía. El consumo de los supercomputadores actuales implica unos costes muy altos de funcionamiento. Estos costes no tienen simplemente implicaciones a nivel económico sino también implicaciones en el medio ambiente. Dado la importancia del problema, en los últimos tiempos se han realizado importantes esfuerzos de investigación para atacar el problema de la gestión eficiente de la energía que consumen los sistemas de supercomputación. Dado que la CPU supone un alto porcentaje del consumo total de un sistema, nuestro trabajo se centra en la reducción y gestión eficiente de la energía consumida por la CPU. En concreto, esta tesis se centra en la viabilidad de realizar esta gestión mediante la técnica de Dynamic Voltage Frequency Scalingi (DVFS), una técnica ampliamente utilizada con el objetivo de reducir el consumo energético de la CPU. Sin embargo, esta técnica puede implicar una reducción en el rendimiento de las aplicaciones que se ejecutan, ya que implica una reducción de la frecuencia. Si tenemos en cuenta que el contexto de esta tesis son sistemas de alta prestaciones, minimizar el impacto en la pérdida de rendimiento será uno de nuestros objetivos. Sin embargo, en nuestro contexto, el rendimiento de un trabajo viene determinado por dos factores, tiempo de ejecución y tiempo de espera, por lo que habrá que considerar los dos componentes. Los sistemas de supercomputación suelen estar gestionados por sistemas de colas. Los trabajos, dependiendo de la política que se aplique y el estado del sistema, deberán esperar más o menos tiempo antes de ser ejecutado. Dado las características del sistema objetivo de esta tesis, nosotros consideramos que el Planificador de trabajo (o Job Scheduler), es el mejor componente del sistema para incluir la gestión de la energía ya que es el único punto donde se tiene una visión global de todo el sistema. En este trabajo de tesis proponemos un conjunto de políticas de planificación que considerarán el consumo energético como un recurso más. Estas políticas decidirán que trabajo ejecutar, el número de cpus asignadas y la lista de cpus (y nodos) sino también la frecuencia a la que estas cpus se ejecutarán. Estas políticas estarán orientadas a dos objetivos: reducir la energía total consumida por un conjunto de trabajos y controlar en consumo puntual de un conjunto puntual para evitar saturaciones del sistema en aquellos centros que puedan tener una capacidad limitada (permanente o puntual). El primer grupo de políticas intentará reducir el consumo total minimizando el impacto en el rendimiento. En este grupo encontramos una primera política que asigna la frecuencia de las cpus en función de la utilización del sistema y una segunda que calcula una estimación de la penalización que sufrirá el trabajo que va a empezar para decidir si reducir o no la frecuencia. Estas políticas han mostrado unos resultados aceptables con sistemas poco cargados, pero han mostrado unas pérdidas de rendimiento significativas cuando el sistema está muy cargado. Estas pérdidas de rendimiento no han sido a nivel de incremento significativo del tiempo de ejecución de los trabajos, pero sí de las métricas de rendimiento que incluyen el tiempo de espera de los trabajos (habituales en este contexto). El segundo grupo de políticas, orientadas a sistemas con limitaciones en cuanto a la potencia que pueden consumir, han mostrado un gran potencial utilizando DVFS como mecanismo de gestión. En este caso, comparado con un sistema que no incluya esta gestión, han demostrado mejoras en el rendimiento ya que permiten ejecutar más trabajos de forma simultánea, reduciendo significativamente el tiempo de espera de los trabajos. En este segundo grupo proponemos una política basada en el rendimiento del trabajo que se va a ejecutar y una segunda que considera la asignación de todos los recursos como un problema de optimización lineal. Esta última política es la contribución más importante de la tesis ya que demuestra un buen comportamiento en todos los casos evaluados. La última contribución de la tesis es un estudio del potencial de DVFS como técnica de gestión de la energía en un futuro próximo, en función de un estudio de las características de las aplicaciones, de la reducción de DVFS en el consumo de la CPU y del peso de la CPU dentro de todo el sistema. Este estudio indica que la capacidad de DVFS de ahorrar energía será limitado pero sigue mostrando un gran potencial de cara al control del consumo energético

Optimizing job performance under a given power constraint in HPC centers

Never-ending striving for performance has resulted in a tremendous increase in power consumption of HPC centers. Power budgeting has become very important from several reasons such as reliability, operating costs and limited power draw due to the existing infrastructure. In this paper we propose a power budget guided job scheduling policy that maximize overall job performance for a given power budget. We have shown that using DVFS under a power constraint performance can be significantly improved as it allows more jobs to run simultaneously leading to shorter wait times. Aggressiveness of frequency scaling applied to a job depends on instantaneous power consumption and on the job's predicted performance. Our policy has been evaluated for four workload traces from systems in production use with up to 4 008 processors. The results show that our policy achieves up to two times better performance compared to power budgeting without DVFS. Moreover it leads to 23% lower CPU energy consumption on average. Furthermore, we have investigated how much job performance and energy efficiency can be improved under our policy and same power budget by an increase in the number of DVFS enabled processors.Peer ReviewedPostprint (published version

Adapting Batch Scheduling to Workload Characteristics: What can we expect From Online Learning?

Despite the impressive growth and size of super-computers, the computational power they provide still cannot match the demand. Efficient and fair resource allocation is a critical task. Super-computers use Resource and Job Management Systems to schedule applications, which is generally done by relying on generic index policies such as First Come First Served and Shortest Processing time First in combination with Backfilling strategies. Unfortunately, such generic policies often fail to exploit specific characteristics of real workloads. In this work, we focus on improving the performance of online schedulers. We study mixed policies, which are created by combining multiple job characteristics in a weighted linear expression, as opposed to classical pure policies which use only a single characteristic. This larger class of scheduling policies aims at providing more flexibility and adaptability. We use space coverage and black-box optimization techniques to explore this new space of mixed policies and we study how can they adapt to the changes in the workload. We perform an extensive experimental campaign through which we show that (1) even the best pure policy is far from optimal and that (2) using a carefully tuned mixed policy would allow to significantly improve the performance of the system. (3) We also provide empirical evidence that there is no one size fits all policy, by showing that the rapid workload evolution seems to prevent classical online learning algorithms from being effective.Malgré la croissance impressionnante et la taille des super-ordinateurs, le la puissance de calcul qu’ils fournissent ne peut toujours pas correspondre à la demande. Une allocation efficace et juste des ressources est essentielle tâche. Les super-ordinateurs utilisent des systèmes de gestion des ressources et des tâches pour programmer les applications, ce qui est généralement fait en s?appuyant sur des politiques d’index telles que First Come First Served et Shortest Temps de traitement D’abord en combinaison avec les stratégies de remblayage. Malheureusement, ces politiques génériques échouent souvent exploiter les caractéristiques spécifiques des charges de travail réelles. Dans ce travail, nous nous concentrons sur l’amélioration des performances des ordonnanceurs en ligne. Nous étudions des stratégies mixtes, créées en combinant plusieurs tâches caractéristiques dans une expression linéaire pondérée, par opposition à les politiques pures classiques qui n’utilisent qu’une seule caractéristique. Ce une plus grande classe de politiques de planification vise à offrir plus de flexibilité et adaptabilité. Nous utilisons la couverture d’espace et l’optimisation de la boîtenoire techniques pour explorer ce nouvel espace de politiques mixtes et nous étudions Comment peuvent-ils s’adapter aux changements de la charge de travail? Nous réalisons une vaste campagne expérimentale à travers laquelle nous montrons que (1) même la meilleure politique pure est loin d?être optimale et que (2) l?utilisation d?une politique mixte soigneusement adaptée permettrait de améliorer de manière significative les performances du système. (3) nous aussi fournir des preuves empiriques qu’il n’y a pas de politique uniforme, en montrant que l’évolution rapide de la charge de travail semble empêcher algorithmes classiques d’apprentissage en ligne d’être efficaces

Power-Aware Job Dispatching in High Performance Computing Systems

This works deals with the power-aware job dispatching problem in supercomputers; broadly speaking the dispatching consists of assigning finite capacity resources to a set of activities, with a special concern toward power and energy efficient solutions. We introduce novel optimization approaches to address its multiple aspects. The proposed techniques have a broad application range but are aimed at applications in the field of High Performance Computing (HPC) systems. Devising a power-aware HPC job dispatcher is a complex, where contrasting goals must be satisfied. Furthermore, the online nature of the problem request that solutions must be computed in real time respecting stringent limits. This aspect historically discouraged the usage of exact methods and favouring instead the adoption of heuristic techniques. The application of optimization approaches to the dispatching task is still an unexplored area of research and can drastically improve the performance of HPC systems. In this work we tackle the job dispatching problem on a real HPC machine, the Eurora supercomputer hosted at the Cineca research center, Bologna. We propose a Constraint Programming (CP) model that outperforms the dispatching software currently in use. An essential element to take power-aware decisions during the job dispatching phase is the possibility to estimate jobs power consumptions before their execution. To this end, we applied Machine Learning techniques to create a prediction model that was trained and tested on the Euora supercomputer, showing a great prediction accuracy. Then we finally develop a power-aware solution, considering the same target machine, and we devise different approaches to solve the dispatching problem while curtailing the power consumption of the whole system under a given threshold. We proposed a heuristic technique and a CP/heuristic hybrid method, both able to solve practical size instances and outperform the current state-of-the-art techniques

Adaptive Resource and Job Management for Limited Power Consumption

International audienceThe last decades have been characterized by anever growing requirement in terms of computing and storage resources.This tendency has recently put the pressure on the abilityto efficiently manage the power required to operate the hugeamount of electrical components associated with state-of-the-arthigh performance computing systems. The power consumption ofa supercomputer needs to be adjusted based on varying powerbudget or electricity availabilities. As a consequence, Resourceand Job Management Systems have to be adequately adaptedin order to efficiently schedule jobs with optimized performancewhile limiting power usage whenever needed.We introduce in this paper a new scheduling strategy thatcan adapt the executed workload to a limited power budget. Theoriginality of this approach relies upon a combination of speedscaling and node shutdown techniques for power reductions. It isimplemented into the widely used resource and job managementsystem SLURM. Finally, it is validated through large scale emulationsusing real production workload traces of the supercomputerCurie

BSLD threshold driven power management policy for HPC centers

In this paper, we propose a power-aware parallel job scheduler assuming DVFS enabled clusters. A CPU frequency assignment algorithm is integrated into the well established EASY backfilling job scheduling policy. Running a job at lower frequency results in a reduction in power dissipation and accordingly in energy consumption. However, lower frequencies introduce a penalty in performance. Our frequency assignment algorithm has two adjustable parameters in order to enable fine grain energy-performance trade-off control. Furthermore, we have done an analysis of HPC system dimension. This paper investigates whether having more DVFS enabled processors for same load can lead to better energy efficiency and performance. Five workload traces from systems in production use with up to 9 216 processors are simulated to evaluate the proposed algorithm and the dimensioning problem. Our approach decreases CPU energy by 7%– 18% on average depending on allowed job performance penalty. Using the power-aware job scheduling for 20% larger system, CPU energy needed to execute same load can be decreased by almost 30% while having same or better job performance.Peer Reviewe