Managing battery lifetime with energy-aware adaptation
ACM Transactions on Computer Systems (TOCS)
Managing battery lifetime with energy-aware adaptation
ACM Transactions on Computer Systems (TOCS)
Balancing power consumption in multiprocessor systems
Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
Scheduling for reduced CPU energy
OSDI '94 Proceedings of the 1st USENIX conference on Operating Systems Design and Implementation
Accurate on-line prediction of processor and memoryenergy usage under voltage scaling
EMSOFT '07 Proceedings of the 7th ACM & IEEE international conference on Embedded software
Slice-balancing H.264 video encoding for improved scalability of multicore decoding
EMSOFT '07 Proceedings of the 7th ACM & IEEE international conference on Embedded software
Bluetooth: vision, goals, and architecture
ACM SIGMOBILE Mobile Computing and Communications Review
An analysis of power consumption in a smartphone
USENIXATC'10 Proceedings of the 2010 USENIX conference on USENIX annual technical conference
Evaluating the effectiveness of model-based power characterization
USENIXATC'11 Proceedings of the 2011 USENIX conference on USENIX annual technical conference
Where is the energy spent inside my app?: fine grained energy accounting on smartphones with Eprof
Proceedings of the 7th ACM european conference on Computer Systems
Overview of the H.264/AVC video coding standard
IEEE Transactions on Circuits and Systems for Video Technology
Modeling energy consumption for master---slave applications
The Journal of Supercomputing
High-Resolution power profiling of GPU functions using low-resolution measurement
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
IA^3 '13 Proceedings of the 3rd Workshop on Irregular Applications: Architectures and Algorithms
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Measuring the energy consumption of software components is a major building block for generating models that allow for energy-aware scheduling, accounting and budgeting. Current measurement techniques focus on coarse-grained measurements of application or system events. However, fine grain adjustments in particular in the operating-system kernel and in application-level servers require power profiles at the level of a single software function. Until recently, this appeared to be impossible due to the lacking fine grain resolution and high costs of measurement equipment. In this paper we report on our experience in using the Running Average Power Limit (RAPL) energy sensors available in recent Intel CPUs for measuring energy consumption of short code paths. We investigate the granularity at which RAPL measurements can be performed and discuss practical obstacles that occur when performing these measurements on complex modern CPUs. Furthermore, we demonstrate how to use the RAPL infrastructure to characterize the energy costs for decoding video slices.