Thread Count Prediction Model: Dynamically Adjusting Threads for Heterogeneous Many-Core Systems

2015 ◽  
Author(s):  
Tao Ju ◽  
Weiguo Wu ◽  
Heng Chen ◽  
Zhengdong Zhu ◽  
Xiaoshe Dong
Keyword(s):  
Prediction Model ◽  
Many Core ◽  
Thread Count

scholarly journals Energy-Efficient Thread Mapping for Heterogeneous Many-Core Systems via Dynamically Adjusting the Thread Count

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1346 ◽  
Author(s):  
Tao Ju ◽  
Yan Zhang ◽  
Xuejun Zhang ◽  
Xiaogang Du ◽  
Xiaoshe Dong
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Prediction Model ◽  
Phase Changes ◽  
Core System ◽  
Computing Performance ◽  
Many Core ◽  
Thread Mapping ◽  
Intel Mic ◽  
Thread Count

Improving computing performance and reducing energy consumption are a major concern in heterogeneous many-core systems. The thread count directly influences the computing performance and energy consumption for a multithread application running on a heterogeneous many-core system. For this work, we studied the interrelation between the thread count and the performance of applications to improve total energy efficiency. A prediction model of the optimum thread count, hereafter the thread count prediction model (TCPM), was designed by using regression analysis based on the program running behaviors and heterogeneous many-core architecture feature. Subsequently, a dynamic predictive thread mapping (DPTM) framework was proposed. DPTM uses the prediction model to estimate the optimum thread count and dynamically adjusts the number of active hardware threads according to the phase changes of the running program in order to achieve the optimal energy efficiency. Experimental results show that DPTM obtains a nearly 49% improvement in performance and a 59% reduction in energy consumption on average. Moreover, DPTM introduces about 2% additional overhead compared with traditional thread mapping for PARSEC(The Princeton Application Repository for Shared-Memory Computers) benchmark programs running on an Intel MIC (Many integrated core)heterogeneous many-core system.

1575: External Validation of Outcome Prediction Model for Ureteral/Renal Calculi

2005 ◽  
Vol 173 (4S) ◽  
pp. 427-427
Author(s):  
Sijo J. Parekattil ◽  
Udaya Kumar ◽  
Nicholas J. Hegarty ◽  
Clay Williams ◽  
Tara Allen ◽  
...  
Keyword(s):  
Prediction Model ◽  
Outcome Prediction ◽  
External Validation ◽  
Renal Calculi

Development of a headlamp dimming request prediction model

1975 ◽  
Author(s):  
Vivek D. Bhise ◽  
Thomas F. Swigart ◽  
Eugene I. Farber
Keyword(s):  
Prediction Model

Short-Version YLS-CMI: An Alternative Prediction Model for Juvenile Offenders at Intake

2009 ◽  
Author(s):  
Christina Campbell ◽  
Eyitayo Onifade ◽  
William Davidson ◽  
Jodie Petersen
Keyword(s):  
Juvenile Offenders ◽  
Prediction Model ◽  
Short Version

Career Choice Prediction--Model

2019 ◽  
Author(s):  
Zool Hilmi Mohamed Ashari ◽  
Norzaini Azman ◽  
Mohamad Sattar Rasul
Keyword(s):  
Prediction Model ◽  
Career Choice

Speed Prediction Model of Trucks on Gradient Sections Considering Vertical Curve

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Qianqian Liang ◽  
Xiaodong Zhang ◽  
Jinliang Xu ◽  
Yang Zhang
Keyword(s):  
Prediction Model ◽  
Speed Prediction

Stock Market Prediction using Data Mining Techniques

2020 ◽  
pp. 310-313
Author(s):  
Karunesh Makker ◽  
Prince Patel ◽  
Hrishikesh Roy ◽  
Sonali Borse
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Prediction Model ◽  
Efficient Market Hypothesis ◽  
Deterministic System ◽  
Vast Number ◽  
Factors Influencing ◽  
Number Of Factors ◽  
Using Data ◽  
Robust Prediction

Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding Technical indicators will guide the investor to minimize the risk and reap better returns.

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