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GPGPU power estimation with core and memory frequency scaling
Qiang WANG
, Xiaowen CHU
Department of Computer Science
Research output
:
Contribution to journal
›
Conference article
›
peer-review
14
Citations (Scopus)
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Dive into the research topics of 'GPGPU power estimation with core and memory frequency scaling'. Together they form a unique fingerprint.
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Keyphrases
Graphics Processing Unit
100%
GPGPU
100%
Power Estimation
100%
Frequency Scaling
100%
Prediction Model
22%
Least Squares Support Vector Regression (LSSVR)
22%
Dynamic Voltage Frequency Scaling
22%
Dynamic Voltage Scaling
22%
Electricity Cost
11%
Environmental Concern
11%
Mean Square Error
11%
Power Consumption
11%
Memory Systems
11%
Performance Penalty
11%
Default Settings
11%
Mean Absolute Percentage Error
11%
Experimental Dataset
11%
10-fold Cross Validation
11%
Scaling Technique
11%
Economic Concern
11%
Performance Prediction Model
11%
Regression Prediction
11%
Application Performance
11%
Frequency Sets
11%
Fast Prediction
11%
Reserve Power
11%
Computer Science
Graphics Processing Unit
100%
Memory Frequency
100%
Prediction Model
33%
Support Vector Regression
22%
Dynamic Voltage and Frequency Scaling
22%
Supercomputer
11%
Power Consumption
11%
Performance Penalty
11%
Application Performance
11%
Memory System
11%
Data Center
11%
Scaling Technique
11%
Fold Cross Validation
11%
Engineering
Graphics Processing Unit
100%
Power Estimation
100%
Support Vector Machine
22%
Voltage Scaling
22%
Dynamic Frequency Scaling
22%
Electric Power Utilization
11%
Performance Penalty
11%
Application Performance
11%
Mean Square Error
11%
Electricity Cost
11%
Prior Model
11%
Experimental Data Set
11%
Performance Prediction
11%