Analysis of power-saving techniques over a large multi-use cluster with variable workload

Reduction of power consumption for any computer system is now an important issue, although this should be carried out in a manner that is not detrimental to the users of that computer system. We present a number of policies that can be applied to multi-use clusters where computers are shared between interactive users and high-throughput computing. We evaluate policies by trace-driven simulations to determine the effects on power consumed by the high-throughput workload and impact on high-throughput users. We further evaluate these policies for higher workloads by synthetically generating workloads based around the profiled workload observed through our system. We demonstrate that these policies could save 55% of the currently used energy for our high-throughput jobs over our current cluster policies without affecting the high-throughput users’ experience

Power Management Techniques for Data Centers: A Survey

With growing use of internet and exponential growth in amount of data to be stored and processed (known as 'big data'), the size of data centers has greatly increased. This, however, has resulted in significant increase in the power consumption of the data centers. For this reason, managing power consumption of data centers has become essential. In this paper, we highlight the need of achieving energy efficiency in data centers and survey several recent architectural techniques designed for power management of data centers. We also present a classification of these techniques based on their characteristics. This paper aims to provide insights into the techniques for improving energy efficiency of data centers and encourage the designers to invent novel solutions for managing the large power dissipation of data centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy Efficiency, Green Computing, DVFS, Server Consolidatio

Evaluation of DVFS techniques on modern HPC processors and accelerators for energy-aware applications

Energy efficiency is becoming increasingly important for computing systems, in particular for large scale HPC facilities. In this work we evaluate, from an user perspective, the use of Dynamic Voltage and Frequency Scaling (DVFS) techniques, assisted by the power and energy monitoring capabilities of modern processors in order to tune applications for energy efficiency. We run selected kernels and a full HPC application on two high-end processors widely used in the HPC context, namely an NVIDIA K80 GPU and an Intel Haswell CPU. We evaluate the available trade-offs between energy-to-solution and time-to-solution, attempting a function-by-function frequency tuning. We finally estimate the benefits obtainable running the full code on a HPC multi-GPU node, with respect to default clock frequency governors. We instrument our code to accurately monitor power consumption and execution time without the need of any additional hardware, and we enable it to change CPUs and GPUs clock frequencies while running. We analyze our results on the different architectures using a simple energy-performance model, and derive a number of energy saving strategies which can be easily adopted on recent high-end HPC systems for generic applications