Multistatic active system performance modeling in shallow waters

2001 ◽  
Vol 110 (5) ◽  
pp. 2744-2744
Author(s):  
David M. Fromm
Keyword(s):  
System Performance ◽  
Performance Modeling ◽  
Shallow Waters ◽  
Active System

Device/system performance modeling of stacked lateral NWFET logic

2016 ◽  
Author(s):  
Victor Huang ◽  
Chenyun Pan ◽  
Dmitry Yakimets ◽  
Praveen Raghavan ◽  
Azad Naeemi
Keyword(s):  
System Performance ◽  
Performance Modeling

VLSI And System Performance Modeling

IEEE Micro ◽  
1987 ◽  
Vol 7 (4) ◽  
pp. 59-72 ◽  
Author(s):  
Sorin lacobovici ◽  
Chakchung Ng
Keyword(s):  
System Performance ◽  
Performance Modeling

scholarly journals Fast Performance Modeling across Different Database Versions Using Partitioned Co-Kriging

2021 ◽  
Vol 11 (20) ◽  
pp. 9669
Author(s):  
Rong Cao ◽  
Liang Bao ◽  
Shouxin Wei ◽  
Jiarui Duan ◽  
Xi Wu ◽  
...  
Keyword(s):  
Performance Prediction ◽  
System Performance ◽  
Prediction Accuracy ◽  
Performance Modeling ◽  
State Of The Art ◽  
Database Systems ◽  
Target Domain ◽  
Reliable Performance ◽  
Measurement Effort ◽  
Control Functional

Database systems have a large number of configuration parameters that control functional and non-functional properties (e.g., performance and cost). Different configurations may lead to different performance values. To understand and predict the effect of configuration parameters on system performance, several learning-based strategies have been recently proposed. However, existing approaches usually assume a fixed database version such that learning has to be repeated once the database version changes. Repeating measurement and learning for each version is expensive and often practically infeasible. Instead, we propose the Partitioned Co-Kriging (PCK) approach that transfers knowledge from an older database version (source domain) to learn a reliable performance prediction model fast for a newer database version (target domain). Our method is based on the key observations that performance responses typically exhibit similarities across different database versions. We conducted extensive experiments under 5 different database systems with different versions to demonstrate the superiority of PCK. Experimental results show that PCK outperforms six state-of-the-art baseline algorithms in terms of prediction accuracy and measurement effort.

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