The Experts below are selected from a list of 35118 Experts worldwide ranked by ideXlab platform

Wei Zhou - One of the best experts on this subject based on the ideXlab platform.

  • synergistic calibration of noisy thermal sensors using smoothing filter based kalman predictor
    International Symposium on Circuits and Systems, 2018
    Co-Authors: Henglu Wei, Wei Zhou
    Abstract:

    Embedded thermal sensors are very susceptible to a variety of noise sources, 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 dynamic thermal management (DTM). In this paper, a smoothing filter-based Kalman prediction technique is proposed to estimate the accurate temperatures of noisy sensors. On this basis, a multi-sensor synergistic calibration algorithm 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 AMD Quad-Core Processor in real-time. The acquired real temperature data are used to evaluate our prediction performance. Simulation shows that the synergistic calibration scheme can achieve an average reduction of the root-mean-square error (RMSE) by 75.9% 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 21.6%. These results clearly demonstrate that if our approach is used to perform the temperature estimation, the response mechanisms of DTM can be triggered to adjust the voltages, frequencies, and cooling fan speeds at more appropriate times.

  • On-Line Temperature Estimation for Noisy Thermal Sensors Using a Smoothing Filter-Based Kalman Predictor.
    Sensors (Basel Switzerland), 2018
    Co-Authors: Henglu Wei, Wei Zhou, Zhemin Duan
    Abstract:

    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.

Henglu Wei - One of the best experts on this subject based on the ideXlab platform.

  • synergistic calibration of noisy thermal sensors using smoothing filter based kalman predictor
    International Symposium on Circuits and Systems, 2018
    Co-Authors: Henglu Wei, Wei Zhou
    Abstract:

    Embedded thermal sensors are very susceptible to a variety of noise sources, 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 dynamic thermal management (DTM). In this paper, a smoothing filter-based Kalman prediction technique is proposed to estimate the accurate temperatures of noisy sensors. On this basis, a multi-sensor synergistic calibration algorithm 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 AMD Quad-Core Processor in real-time. The acquired real temperature data are used to evaluate our prediction performance. Simulation shows that the synergistic calibration scheme can achieve an average reduction of the root-mean-square error (RMSE) by 75.9% 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 21.6%. These results clearly demonstrate that if our approach is used to perform the temperature estimation, the response mechanisms of DTM can be triggered to adjust the voltages, frequencies, and cooling fan speeds at more appropriate times.

  • On-Line Temperature Estimation for Noisy Thermal Sensors Using a Smoothing Filter-Based Kalman Predictor.
    Sensors (Basel Switzerland), 2018
    Co-Authors: Henglu Wei, Wei Zhou, Zhemin Duan
    Abstract:

    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.

Zhemin Duan - One of the best experts on this subject based on the ideXlab platform.

  • On-Line Temperature Estimation for Noisy Thermal Sensors Using a Smoothing Filter-Based Kalman Predictor.
    Sensors (Basel Switzerland), 2018
    Co-Authors: Henglu Wei, Wei Zhou, Zhemin Duan
    Abstract:

    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.

Jose Carlos Sancho - One of the best experts on this subject based on the ideXlab platform.

  • a performance evaluation of the nehalem quad core Processor for scientific computing
    Parallel Processing Letters, 2008
    Co-Authors: Kevin J Barker, Kei Davis, Adolfy Hoisie, Darren J Kerbyson, Michael Lang, Scott Pakin, Jose Carlos Sancho
    Abstract:

    In this work we present an initial performance evaluation of Intel's latest, second- generation Quad-Core Processor, Nehalem, and provide a comparison to first-generat ion AMD and Intel Quad-Core Processors Barcelona and Tigerton. Nehalem is the first In­ tel Processor to implement a NUMA architecture incorporating QuickPath Interconnect for interconnecting Processors within a node, and the first to incorporate an integrated memory controller. We evaluate the suitability of these Processors in quad-socket com­ pute nodes as building blocks for large-scale scientific computing clusters. Our analysis of intra-Processor and intra-node scalability of microbenchmarks, and a range of large-scale scientific applications, indicates that Quad-Core Processors can deliver an improvement in performance of up to 4x over a single core depending on the workload being processed. However, scalability can be less when considering a full node. We show that Nehalem outperforms Barcelona on memory-intens ive codes by a factor of two for a Nehalem node with 8 cores and a Barcelona node containing 16 cores. Further optimizations are pos­ sible with Nehalem, including the use of Simultaneous Multithreading, which improves the performance of some applications by up to 50%.

Mohd Azizi Sanwani - One of the best experts on this subject based on the ideXlab platform.

  • MPI communication benchmarking on intel Xeon dual Quad-Core Processor cluster
    2011 IEEE Conference on Open Systems, 2011
    Co-Authors: Roswan Ismail, Nor Asila Wati Abdul Hamid, Mohamed Othman, Rohaya Latip, Mohd Azizi Sanwani
    Abstract:

    This paper reports the measurements of MPI communication benchmarking on Khaldun cluster which ran on Linux-based IBM Blade HS21 Servers with Intel Xeon dual Quad-Core Processor and Gigabit Ethernet interconnect. The measurements were done by using SKaMPI and IMB benchmark programs. Significantly, these were the first results produced by using SKaMPI and IMB to analyze the performance of Open MPI implementation on Khaldun cluster. The comparison and analysis of the results of point to point and collective communication from these two benchmark programs were then provided. It showed that different MPI benchmark programs rendered different results since they used different measurement techniques. The results were then compared to the experiment's results that were done on cluster with Opteron dual Quad-Core Processor and Gigabit Ethernet interconnect. The analysis indicated that the architecture of machines used also affected the results.