Energy Efficiency in the ICT - Profiling Power Consumption in Desktop Computer Systems

Energy awareness in the ICT has become an important issue. Focusing on software, recent work suggested the existence of a relationship between power consumption, software configuration and usage patterns in computer systems. The aim of this work was collecting and analysing power consumption data of general-purpose computer systems, simulating common usage scenarios, in order to extract a power consumption profile for each scenario. We selected two desktop systems of different generations as test machines. Meanwhile, we developed 11 usage scenarios, and conducted several test runs of them, collecting power consumption data by means of a power meter. Our analysis resulted in an estimation of a power consumption value for each scenario and software application used, obtaining that each single scenario introduced an overhead from 2 to 11 Watts, which corresponds to a percentage increase that can reach up to 20% on recent and more powerful systems. We determined that software and its usage patterns impact consistently on the power consumption of computer systems. Further work will be devoted to evaluate how power consumption is affected by the usage of specific system resource

Complexity plots

In this paper, we present a novel visualization technique for assisting in observation and analysis of algorithmic\ud complexity. In comparison with conventional line graphs, this new technique is not sensitive to the units of\ud measurement, allowing multivariate data series of different physical qualities (e.g., time, space and energy) to be juxtaposed together conveniently and consistently. It supports multivariate visualization as well as uncertainty visualization. It enables users to focus on algorithm categorization by complexity classes, while reducing visual impact caused by constants and algorithmic components that are insignificant to complexity analysis. It provides an effective means for observing the algorithmic complexity of programs with a mixture of algorithms and blackbox software through visualization. Through two case studies, we demonstrate the effectiveness of complexity plots in complexity analysis in research, education and application

A novel haptic model and environment for maxillofacial surgical operation planning and manipulation

This paper presents a practical method and a new haptic model to support manipulations of bones and their segments during the planning of a surgical operation in a virtual environment using a haptic interface. To perform an effective dental surgery it is important to have all the operation related information of the patient available beforehand in order to plan the operation and avoid any complications. A haptic interface with a virtual and accurate patient model to support the planning of bone cuts is therefore critical, useful and necessary for the surgeons. The system proposed uses DICOM images taken from a digital tomography scanner and creates a mesh model of the filtered skull, from which the jaw bone can be isolated for further use. A novel solution for cutting the bones has been developed and it uses the haptic tool to determine and define the bone-cutting plane in the bone, and this new approach creates three new meshes of the original model. Using this approach the computational power is optimized and a real time feedback can be achieved during all bone manipulations. During the movement of the mesh cutting, a novel friction profile is predefined in the haptical system to simulate the force feedback feel of different densities in the bone

Investigating the impact of image content on the energy efficiency of hardware-accelerated digital spatial filters

Battery-operated low-power portable computing devices are becoming an inseparable part of human daily life. One of the major goals is to achieve the longest battery life in such a device. Additionally, the need for performance in processing multimedia content is ever increasing. Processing image and video content consume more power than other applications. A widely used approach to improving energy efficiency is to implement the computationally intensive functions as digital hardware accelerators. Spatial filtering is one of the most commonly used methods of digital image processing. As per the Fourier theory, an image can be considered as a two-dimensional signal that is composed of spatially extended two-dimensional sinusoidal patterns called gratings. Spatial frequency theory states that sinusoidal gratings can be characterised by its spatial frequency, phase, amplitude, and orientation. This article presents results from our investigation into assessing the impact of these characteristics of a digital image on the energy efficiency of hardware-accelerated spatial filters employed to process the same image. Two greyscale images each of size 128 × 128 pixels comprising two-dimensional sinusoidal gratings at maximum spatial frequency of 64 cycles per image orientated at 0° and 90°, respectively, were processed in a hardware implemented Gaussian smoothing filter. The energy efficiency of the filter was compared with the baseline energy efficiency of processing a featureless plain black image. The results show that energy efficiency of the filter drops to 12.5% when the gratings are orientated at 0° whilst rises to 72.38% at 90°