Playing Hare and Tortoise: The FigarOS Kernel for Fine-Grained System-Level Energy Optimizations

2015 ◽  
Author(s):  
Timo Honig ◽  
Christopher Eibel ◽  
Benedict Herzog ◽  
Heiko Janker ◽  
Peter Wagemann ◽  
...  
Keyword(s):  
System Level ◽  
Level Energy ◽  
Fine Grained ◽  
Energy Optimizations

scholarly journals A Comparative Study of Methods for Measurement of Energy of Computing

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2204 ◽  
Author(s):  
Muhammad Fahad ◽  
Arsalan Shahid ◽  
Ravi Reddy Manumachu ◽  
Alexey Lastovetsky
Keyword(s):  
Predictive Models ◽  
Ground Truth ◽  
System Level ◽  
Average Error ◽  
Level Energy ◽  
Physical Measurements ◽  
Wide Range ◽  
External Power ◽  
On Chip ◽  
Multicore Cpus

Energy of computing is a serious environmental concern and mitigating it is an important technological challenge. Accurate measurement of energy consumption during an application execution is key to application-level energy minimization techniques. There are three popular approaches to providing it: (a) System-level physical measurements using external power meters; (b) Measurements using on-chip power sensors and (c) Energy predictive models. In this work, we present a comprehensive study comparing the accuracy of state-of-the-art on-chip power sensors and energy predictive models against system-level physical measurements using external power meters, which we consider to be the ground truth. We show that the average error of the dynamic energy profiles obtained using on-chip power sensors can be as high as 73% and the maximum reaches 300% for two scientific applications, matrix-matrix multiplication and 2D fast Fourier transform for a wide range of problem sizes. The applications are executed on three modern Intel multicore CPUs, two Nvidia GPUs and an Intel Xeon Phi accelerator. The average error of the energy predictive models employing performance monitoring counters (PMCs) as predictor variables can be as high as 32% and the maximum reaches 100% for a diverse set of seventeen benchmarks executed on two Intel multicore CPUs (one Haswell and the other Skylake). We also demonstrate that using inaccurate energy measurements provided by on-chip sensors for dynamic energy optimization can result in significant energy losses up to 84%. We show that, owing to the nature of the deviations of the energy measurements provided by on-chip sensors from the ground truth, calibration can not improve the accuracy of the on-chip sensors to an extent that can allow them to be used in optimization of applications for dynamic energy. Finally, we present the lessons learned, our recommendations for the use of on-chip sensors and energy predictive models and future directions.

Safety, Energy, and Emissions Impacts of Adaptive Cruise Control and Cooperative Adaptive Cruise Control

2020 ◽  
Vol 2674 (6) ◽  
pp. 253-267 ◽  
Author(s):  
Iman Mahdinia ◽  
Ramin Arvin ◽  
Asad J. Khattak ◽  
Amir Ghiasi
Keyword(s):  
Fuel Consumption ◽  
Adaptive Cruise Control ◽  
Field Tests ◽  
System Level ◽  
Level Energy ◽  
Cruise Control ◽  
Driver Assistance Systems ◽  
Cooperative Adaptive Cruise Control ◽  
Vehicle Platoon ◽  
Fuel Consumption And Emissions

Connected and automated vehicle technologies have the potential to significantly improve transportation system performance. In particular, advanced driver-assistance systems, such as adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC), may lead to substantial improvements in performance by decreasing driver inputs and taking over control of the vehicle. However, the impacts of these technologies on the vehicle- and system-level energy consumption, emissions, and safety have not been quantified in field tests. The goal of this paper is to study the impacts of automated and cooperative systems in mixed traffic containing conventional, ACC, and CACC vehicles. To reach this goal, experimental data based on real-world conditions are collected (in tests conducted by the Federal Highway Administration and the U.S. Department of Transportation) with presence of ACC, CACC, and conventional vehicles in a vehicle platoon scenario and a cooperative merging scenario. Specifically, a platoon of five vehicles with different vehicle type combinations is analyzed to generate new knowledge about potential safety, energy efficiency, and emission improvement from vehicle automation and cooperation. Results show that adopting the CACC system in a five-vehicle platoon substantially reduces the driving volatility and reduces the risk of rear-end collision which consequently improves safety. Furthermore, it decreases fuel consumption and emissions compared with the ACC system and manually-driven vehicles. Results of the merging scenario show that while the cooperative merging system slightly reduces the driving volatility, the fuel consumption and emissions can increase because of sharper accelerations of CACC vehicles compared with manually-driven vehicles.

Common Currency for System Integration of High Intensity Energy Subsystems

2011 ◽  
Author(s):  
David M. Pratt ◽  
David J. Moorhouse
Keyword(s):  
Heat Transfer ◽  
Heat Sink ◽  
High Intensity ◽  
Waste Heat ◽  
Integrated Approach ◽  
High Energy ◽  
Heat Fluxes ◽  
System Level ◽  
Level Energy ◽  
Common Currency

Aerospace vehicle design has progressed in an evolutionary manner, with certain discrete changes such as turbine engines replacing propellers for higher speeds. The evolution has worked very well for commercial aircraft because the major components can be optimized independently. This is not true for many military configurations which require a more integrated approach. In addition, the introduction of aspects for which there is no pre-existing database requires special attention. Examples of subsystem that have no pre-existing data base include directed energy weapons (DEW) such as high power microwaves (HPM) and high energy lasers (HEL). These devices are inefficient, therefore a large portion of the energy required to operate the device is converted to waste heat and must be transferred to a suitable heat sink. For HPM, the average heat load during one ‘shot’ is on the same order as traditional subsystems and thus designing a thermal management system is possible. The challenge is transferring the heat from the HPM device to a heat sink. The power density of each shot could be hundreds of megawatts. This heat must be transferred from the HPM beam dump to a sink. The heat transfer must occur at a rate that will support shots in the 10–100Hz range. For HEL systems, in addition to the high intensity, there are substantial system level thermal loads required to provide an ‘infinite magazine.’ Present models are inadequate to analyze these problems, current systems are unable to sustain the energy dissipation required and the high intensity heat fluxes applied over a very short duration phenomenon is not well understood. These are examples of potential future vehicle integration challenges. This paper addresses these and other subsystems integration challenges using a common currency for vehicle optimization. Exergy, entropy generation minimization, and energy optimization are examples of methodologies that can enable the creation of energy optimized systems. These approaches allow the manipulation of fundamental equations governing thermodynamics, heat transfer, and fluid mechanics to produce minimized irreversibilities at the vehicle, subsystem and device levels using a common currency. Applying these techniques to design for aircraft system-level energy efficiency would identify not only which subsystems are inefficient but also those that are close to their maximum theoretical efficiency while addressing diverse system interaction and optimal subsystem integration. Such analyses would obviously guide researchers and designers to the areas having the highest payoff and enable departures from the evolutionary process and create a breakthrough design.

Assessing System-Level Energy Efficiency of mmWave-Based Wearable Networks

2016 ◽  
Vol 34 (4) ◽  
pp. 923-937 ◽  
Author(s):  
Olga Galinina ◽  
Alexander Pyattaev ◽  
Kerstin Johnsson ◽  
Andrey Turlikov ◽  
Sergey Andreev ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
System Level ◽  
Level Energy

A Holistic Approach to System Level Energy Optimization

2000 ◽  
pp. 88-107 ◽  
Author(s):  
Mary Jane Irwin ◽  
Mahmut Kandemir ◽  
N. Vijaykrishnan ◽  
Anand Sivasubramaniam
Keyword(s):  
Energy Optimization ◽  
Holistic Approach ◽  
System Level ◽  
Level Energy
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