Processor power forecasting through model sample analysis and clustering

[1]  Juan Chen,et al.  $AP^{3}$: Adaptive Power Prediction Framework based on Spatial Partition Multi-Phase Model , 2021, 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys).

[2]  Zheng Wang,et al.  More bang for your buck: Boosting performance with capped power consumption , 2021 .

[3]  Heba Khdr,et al.  Long Short-Term Memory Neural Network-based Power Forecasting of Multi-Core Processors , 2021, 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[4]  Frank Mueller,et al.  Uncore power scavenger: a runtime for uncore power conservation on HPC systems , 2019, SC.

[5]  Daniele Tafani,et al.  Towards a Predictive Energy Model for HPC Runtime Systems Using Supervised Learning , 2019, Euro-Par Workshops.

[6]  Yong Dong,et al.  A holistic energy-efficient approach for a processor-memory system , 2019, Tsinghua Science and Technology.

[7]  Aishan Wumaier,et al.  Study and Implementing K-mean Clustering Algorithm on English Text and Techniques to Find the Optimal Value of K , 2018, International Journal of Computer Applications.

[8]  B D Satoto,et al.  Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster , 2018, IOP Conference Series: Materials Science and Engineering.

[9]  Yun Zhou,et al.  Energy Wall for Exascale Supercomputing , 2017, Comput. Informatics.

[10]  David M. Eyers,et al.  Manila: Using a Densely Populated PMC-Space for Power Modelling within Large-Scale Systems , 2016, 2016 45th International Conference on Parallel Processing Workshops (ICPPW).

[11]  Jack J. Dongarra,et al.  High-performance conjugate-gradient benchmark: A new metric for ranking high-performance computing systems , 2016, Int. J. High Perform. Comput. Appl..

[12]  Yale N. Patt,et al.  Filtered runahead execution with a runahead buffer , 2015, 2015 48th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

[13]  Yoshua Bengio,et al.  On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.

[14]  Sung Woo Chung,et al.  Leveraging Process Variation for Performance and Energy: In the Perspective of Overclocking , 2014, IEEE Transactions on Computers.

[15]  Razvan Pascanu,et al.  How to Construct Deep Recurrent Neural Networks , 2013, ICLR.

[16]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[17]  Eduard Ayguadé,et al.  Decomposable and responsive power models for multicore processors using performance counters , 2010, ICS '10.

[18]  Brian W. Barrett,et al.  Introducing the Graph 500 , 2010 .

[19]  Ravi P. Ramachandran,et al.  Neural network classifiers and Principal Component Analysis for blind signal to noise ratio estimation of speech signals , 2009, 2009 IEEE International Symposium on Circuits and Systems.

[20]  Kai Li,et al.  The PARSEC benchmark suite: Characterization and architectural implications , 2008, 2008 International Conference on Parallel Architectures and Compilation Techniques (PACT).

[21]  Daisuke Takahashi,et al.  The HPC Challenge (HPCC) benchmark suite , 2006, SC.

[22]  Jiebo Luo,et al.  Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications , 1998, IEEE Trans. Image Process..

[23]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[24]  David H. Bailey,et al.  The NAS parallel benchmarks summary and preliminary results , 1991, Proceedings of the 1991 ACM/IEEE Conference on Supercomputing (Supercomputing '91).

[25]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[26]  Yifei Guo,et al.  Evaluating Performance, Power and Energy of Deep Neural Networks on CPUs and GPUs , 2021, NCTCS.

[27]  D. Tamir,et al.  Evaluating Neural Network Methods for PMC-based CPU Power Prediction , 2015 .

[28]  W. Hubei,et al.  Biomedical Applications , 2011 .

[29]  Jong Kyoung Kim,et al.  Speech recognition , 1983, 1983 IEEE International Solid-State Circuits Conference. Digest of Technical Papers.