Temporal characterization of SPEC CPU2006 workloads: Analysis and synthesis

Zou, Qiang; Yue, Jianhui; Segee, Bruce; Zhu, Yan

doi:10.1109/pccc.2012.6407716

Cited by 9 publications

(5 citation statements)

References 30 publications

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“…To confirm the validity of our methods, we simulate the SPEC2006 benchmark suite [17] which has 29 applications, using SimpleScalar [18] toolset for a 65 nm processor based on Alpha 21264 architecture [19]. Thermal simulation is performed using HotSpot [20] version 5.0 in the grid level (discretised with resolution L = W = 32).…”

Section: Resultsmentioning

confidence: 99%

Optimising thermal sensor placement and thermal maps reconstruction for microprocessors using simulated annealing algorithm based on PCA

Jiang

Zhou

2016

IET Circuits, Devices & Systems

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Using embedded thermal sensors, high-performance microprocessors employ dynamic thermal management techniques to measure runtime thermal behaviour so as to prevent thermal runaway situations. However, on-chip thermal sensors are highly susceptible to noise, which results in a higher probability of false alarms and unnecessary responses. In this study, the authors propose a set of methods based on principal component analysis (PCA) to address the problem of recovering precisely the full thermal map from the on-chip thermal sensors when the sensor readings have been corrupted by noise. The authors utilise simulated annealing algorithm to devise method that determines the optimal thermal sensor locations, which can obtain superior results compared with the available literature. On this basis, the authors also propose a practical method for full thermal reconstruction to estimate the accurate temperatures of full chip, which would not need to know a-priori temperature information at each spatial distribution of thermal map. The experimental results confirm that the authors' proposed methods are stable in the case of noisy thermal sensor observations, which can achieve a high fidelity thermal monitoring.

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Section: Resultsmentioning

confidence: 99%

Optimising thermal sensor placement and thermal maps reconstruction for microprocessors using simulated annealing algorithm based on PCA

Jiang

Zhou

2016

IET Circuits, Devices & Systems

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“…The oil temperature is monitored using a thermostat. The detailed thermal traces of the SPEC CPU 2006 (Standard Performance Evaluation Corporation (SPEC), Gainesville, Virginia, United States of America) benchmark workloads [ 46 ] are captured using a mid-wave infrared camera (InfraTec ImageIR ® 8300 (InfraTec GmbH Infrarotsensorik und Messtechnik, Dresden, Germany)). Because the lightly doped and undoped silicon are partially transparent at the mid-wave infrared range, the chip temperature can be measured through our tailored infrared transparent heat sink.…”

Section: Infrared Imaging-based Temperature Measurement Techniquementioning

confidence: 99%

On-Line Temperature Estimation for Noisy Thermal Sensors Using a Smoothing Filter-Based Kalman Predictor

et al. 2018

Sensors

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Dynamic thermal management (DTM) mechanisms utilize embedded thermal sensors to collect fine-grained temperature information for monitoring the real-time thermal behavior of multi-core processors. However, embedded thermal sensors are very susceptible to a variety of sources of noise, including environmental uncertainty and process variation. This causes the discrepancies between actual temperatures and those observed by on-chip thermal sensors, which seriously affect the efficiency of DTM. In this paper, a smoothing filter-based Kalman prediction technique is proposed to accurately estimate the temperatures from noisy sensor readings. For the multi-sensor estimation scenario, the spatial correlations among different sensor locations are exploited. On this basis, a multi-sensor synergistic calibration algorithm (known as MSSCA) is proposed to improve the simultaneous prediction accuracy of multiple sensors. Moreover, an infrared imaging-based temperature measurement technique is also proposed to capture the thermal traces of an advanced micro devices (AMD) quad-core processor in real time. The acquired real temperature data are used to evaluate our prediction performance. Simulation shows that the proposed synergistic calibration scheme can reduce the root-mean-square error (RMSE) by 1.2 • C and increase the signal-to-noise ratio (SNR) by 15.8 dB (with a very small average runtime overhead) compared with assuming the thermal sensor readings to be ideal. Additionally, the average false alarm rate (FAR) of the corrected sensor temperature readings can be reduced by 28.6%. These results clearly demonstrate that if our approach is used to perform temperature estimation, the response mechanisms of DTM can be triggered to adjust the voltages, frequencies, and cooling fan speeds at more appropriate times.

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“…The chip being tested in our experiments is a 45 nm dual‐core AMD Athlon X2 5000 processor operating at 2.2 GHz. To collect the thermal maps used in the following experiments, we utilise the SPEC CPU 2006 benchmark suite [24] which includes 29 different applications.…”

Section: Infrared Temperature Measurement Setupmentioning

confidence: 99%

Heuristic thermal sensor allocation methods for overheating detection of real microprocessors

Wei

Zhou

2017

IET Circuits, Devices & Syst

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Modern microprocessors utilise embedded thermal sensors to continuously monitor the chip's temperature during runtime. However, the overheating locations change temporally and spatially depending on the various workloads running on the chip. Furthermore, on-chip thermal sensor readings are highly affected by noise due to fabrication fluctuations and randomness, which makes the task of thermal monitoring particularly challenging. In this study, the authors first establish overheating detection models to address the thermal sensor allocation problem under two different conditions when the on-chip thermal sensor observations are corrupted by noise. On this basis, a heuristic method based on genetic algorithm is proposed to find a near-optimal thermal sensor allocation solution, which can make overheating detection probability significantly improved with a greatly reduced execution time. They also propose a hybrid algorithm to identify the optimal thermal sensor placement for each individual chip block or component. Moreover, they develop an oil-based cooling system and utilise infrared thermal imaging techniques to capture the thermal traces of a real dual-core microprocessor when running various workloads. The experiments demonstrate that the authors' proposed thermal sensor allocation methods obviously outperform several common allocation approaches in terms of overheating detection, which can provide an accurate and reliable thermal monitoring.

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Temporal characterization of SPEC CPU2006 workloads: Analysis and synthesis

Cited by 9 publications

References 30 publications

Optimising thermal sensor placement and thermal maps reconstruction for microprocessors using simulated annealing algorithm based on PCA

Optimising thermal sensor placement and thermal maps reconstruction for microprocessors using simulated annealing algorithm based on PCA

On-Line Temperature Estimation for Noisy Thermal Sensors Using a Smoothing Filter-Based Kalman Predictor

Heuristic thermal sensor allocation methods for overheating detection of real microprocessors

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