• Energy Research

The generation of energy-efficient parallel scientific codes became very important in the time of carbon footprint reduction. In this paper, we briefly present our latest particle-in-cell code with the results of a numerical simulation of plasma dynamics in an open trap. This code can be auto-vectorized by the Fortran compiler for Intel Xeon processors with AVX-512 instructions such as Intel Xeon Phi and the highest series of all generations of Intel Xeon Scalable processors. Efficient use of processor architecture is the main feature of an energy-efficient solution. We present a step-by-step methodology of energy consumption calculation using Intel hardware features and Intel VTune software. We also give an estimated value of carbon footprint with the impact of high-performance water cooled hardware. The Power Usage Effectiveness (PUE) in the case of high-performance water cooled hardware is equal to 1.03–1.05, and is up to 1.3 in the case of air-cooled systems. This means that power consumption of liquid cooled systems is lower than that air-cooled ones by up to 25%. All these factors play an important role in the carbon footprint reduction problem.

Energy consumption is a momentous problem that severely challenges further design and application of networks. While most researches work on a local view of some aspects (e.g. some devices used in networks) of the energy consumption problems in networks, there has been scarce research on a global view to reduce the amount of energy consumed at a network level (e.g. routing, network deployment). Energy consumption problem is investigated from network routing aspect in this paper. Energy consumption optimization strategies are developed from the aspect of network routing on the network system level. Combining three traffic arrival modes and three energy adaptation modes, optimized network energy consumption models are presented first. Further some energy efficient routing algorithms are developed for specific system models including the Continuous Flow with Speed Scaling model with bandwidth constraint, and the Continuous Flow with Rate Adaptation model. A model and corresponding algorithm for bi-criteria system are also developed so that a trade-off can be made between energy consumption and network delay. While the models can help understand the energy consumption optimization problems from the aspect of network routing on the network system level, the energy efficient routing algorithms can significantly reduce the energy consumed for network packet transmission. Keywords energy consumption; system model; energy efficient algorithm; optimization; network latency; green computing Background Energy consumption is rapidly increasing with the expanding of network size. Methods to reduce the energy consumption of network elements have drawn significant research interest during the past few years. However, there has been scarce research on algorithms to globally reduce the amount of energy consumed at a network level. This paper constructs five energy consumption system models and presents some energy efficiency scheduling algorithms for some specific system models. The research results of this paper will be useful to devise the energy efficiency algorithms in network. This work is supported by National Natural of Science Foundation of China (Nos. 61020106002). This project aims to provide better energy efficiency and performance in computing networks and information systems. Our group has working on the energy efficiency in computer networks and datacenter. Many good papers have been published in respectable international conferences and journals, such as IEEE Symposium on Foundations of Computer Science (FOCS), IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC), Journal of ACM, IEEE Transaction serial, and Journal of Parallel and Distributed Computing. pub

This chapter presents an overview of the state-of-the-art research on computational effort reduction of the intra-frame prediction, and inter-frame and inter-view predictions, which are the two main topics that this book is focused.

This chapter summarizes the conclusions and the contributions described along with this book. It highlights the importance of reducing the encoding effort of the Three-Dimensional High Efficiency Video Coding (3D-HEVC) depth maps and presents a brief description of the main contributions described throughout the chapters. Besides, this chapter shows and discusses open research possibilities.