Improving the Accuracy of Energy Predictive Models for Multicore CPUs Using Additivity of Performance Monitoring Counters

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.

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A Comparative Study of Techniques for Energy Predictive Modeling Using Performance Monitoring Counters on Modern Multicore CPUs

IEEE Access ◽

10.1109/access.2020.3013812 ◽

2020 ◽

Vol 8 ◽

pp. 143306-143332

Author(s):

Arsalan Shahid ◽

Muhammad Fahad ◽

Ravi Reddy Manumachu ◽

Alexey Lastovetsky

Keyword(s):

Comparative Study ◽

Predictive Modeling ◽

Performance Monitoring ◽

Multicore Cpus

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Intelligent and Data-Driven Fault Detection of Photovoltaic Plants

Processes ◽

10.3390/pr9101711 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1711

Author(s):

Siya Yao ◽

Qi Kang ◽

Mengchu Zhou ◽

Abdullah Abusorrah ◽

Yusuf Al-Turki

Keyword(s):

Fault Detection ◽

Predictive Models ◽

Performance Monitoring ◽

Prediction Models ◽

Transition Period ◽

Data Driven ◽

Pv System ◽

Operation And Maintenance ◽

Photovoltaic Plants ◽

Pv Power Generation

Most photovoltaic (PV) plants conduct operation and maintenance (O&M) by periodical inspection and cleaning. Such O&M is costly and inefficient. It fails to detect system faults in time, thus causing heavy loss. To ensure their operations are at an ideal state, this work proposes an unsupervised method for intelligent performance evaluation and data-driven fault detection, which enables engineers to check PV panels in time and implement timely maintenance. It classifies monitoring data into three subsets: ideal period A, transition period S, and downturn period B. Based on A and B datasets, we build two non-continuous regression prediction models, which are based on a tree ensemble algorithm and then modified to fit the non-continuous characteristic of PV data. We compare real-time measured power with both upper and lower reference baselines derived from two predictive models. By calculating their threshold ranges, the proposed method achieves the instantaneous performance monitoring of PV power generation and provides failure identification and O&M suggestions to engineers. It has been assessed on a 6.95 MW PV plant. Its evaluation results indicate that it is able to accurately determine different functioning states and detect both direct and indirect faults in a PV system, thereby achieving intelligent data-driven maintenance.

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