Runtime Power Monitoring in High-End Processors: Methodology and Empirical Data
Proceedings of the 36th annual IEEE/ACM International Symposium on Microarchitecture
Power Consumption of GPUs from a Software Perspective
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
On the energy efficiency of graphics processing units for scientific computing
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
An integrated GPU power and performance model
Proceedings of the 37th annual international symposium on Computer architecture
Energy-aware high performance computing with graphic processing units
HotPower'08 Proceedings of the 2008 conference on Power aware computing and systems
Statistical power modeling of GPU kernels using performance counters
GREENCOMP '10 Proceedings of the International Conference on Green Computing
Evaluating the effectiveness of model-based power characterization
USENIXATC'11 Proceedings of the 2011 USENIX conference on USENIX annual technical conference
Energy-Aware Workload Consolidation on GPU
ICPPW '11 Proceedings of the 2011 40th International Conference on Parallel Processing Workshops
Statistical GPU power analysis using tree-based methods
IGCC '11 Proceedings of the 2011 International Green Computing Conference and Workshops
Power and performance analysis of GPU-accelerated systems
HotPower'12 Proceedings of the 2012 USENIX conference on Power-Aware Computing and Systems
Measuring Energy and Power with PAPI
ICPPW '12 Proceedings of the 2012 41st International Conference on Parallel Processing Workshops
SAAHPC '12 Proceedings of the 2012 Symposium on Application Accelerators in High Performance Computing
Measuring energy consumption for short code paths using RAPL
ACM SIGMETRICS Performance Evaluation Review
Towards an Energy Model for Modular Parallel Scientific Applications
GREENCOM '12 Proceedings of the 2012 IEEE International Conference on Green Computing and Communications
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In order to be able to minimise the energy consumption of an application program, information about the specific energy consumption is required. Modern Nvidia graphics processing units (GPUs) measure their current power consumption and the driver makes the value available to the application every 20 ms. However, for evaluating the energy consumption of GPU kernel functions, such a sampling interval might not be sufficient since the kernels may have a shorter execution time. This article proposes a method for generating high-resolution power profiles, which is the power consumption of a specific function depending on the progress of its execution. The method uses low-resolution measuring instruments offered by GPUs. Power measurements obtained during several executions of the function are combined into a single power profile. The resulting power profile contains power values in intervals which are much shorter than the sampling interval of the hardware driver so that even short-term power changes can be considered, e.g. for calculating the energy consumption of a single function. The article also shows how to extend the approach to an online generation of power profiles. Furthermore, an overview on the power profiles of some important functions, such as BLAS routines, is given.