Interpolation

Univariate Interpolation Library

The Univariate Interpolation Library is a Java 8 library for interpolating data sets of 2D-vectors using univariate functions. The library implements a great range of different interpolation methods and contains functionality to automatically select a good one for any given data set. The automated selection can either be achieved using an independent reference dataset or no additional data at all.

Links:

Download
License
Source Code @ SVN (User: anonymous, Password: anonymous)

If you have any questions, please contact Jóakim v. Kistowski.

Mailing List

Publications

2015[ to top ]

Univariate Interpolation-based Modeling of Power and Performance J. von Kistowski; S. Kounev; in Proceedings of the 9th EAI International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS 2015) (2015).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Performance and power scale non-linearly with device utilization, making characterization and prediction of energy efficiency at a given load level a challenging issue. A common approach to address this problem is the creation of power or performance state tables for a pre-measured subset of all possible system states. Approaches to determine performance and power for a state not included in the measured subset use simple interpolation, such as nearest neighbor interpolation, or define state switching rules. This leads to a loss in accuracy, as unmeasured system states are not considered. In this paper, we compare different interpolation functions and automatically configure and select functions for a given domain or measurement set. We evaluate our approach by comparing interpolation of measurement data subsets against power and performance measurements on a commodity server. We show that for non-extrapolating models interpolation is significantly more accurate than regression, with our automatically configured interpolation function improving modeling accuracy up to 43.6%.}

@inproceedings{KiKo2015-VALUETOOLS, abstract = {{Performance and power scale non-linearly with device utilization, making characterization and prediction of energy efficiency at a given load level a challenging issue. A common approach to address this problem is the creation of power or performance state tables for a pre-measured subset of all possible system states. Approaches to determine performance and power for a state not included in the measured subset use simple interpolation, such as nearest neighbor interpolation, or define state switching rules. This leads to a loss in accuracy, as unmeasured system states are not considered. In this paper, we compare different interpolation functions and automatically configure and select functions for a given domain or measurement set. We evaluate our approach by comparing interpolation of measurement data subsets against power and performance measurements on a commodity server. We show that for non-extrapolating models interpolation is significantly more accurate than regression, with our automatically configured interpolation function improving modeling accuracy up to 43.6\%.}}, author = {von Kistowski, J{\'o}akim and Kounev, Samuel}, booktitle = {Proceedings of the 9th EAI International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS 2015)}, keywords = {Power}, month = {December}, title = {{Univariate Interpolation-based Modeling of Power and Performance}}, year = 2015 }

%0 Conference Paper %1 KiKo2015-VALUETOOLS %A von Kistowski, Jóakim %A Kounev, Samuel %B Proceedings of the 9th EAI International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS 2015) %D 2015 %T Univariate Interpolation-based Modeling of Power and Performance %X Performance and power scale non-linearly with device utilization, making characterization and prediction of energy efficiency at a given load level a challenging issue. A common approach to address this problem is the creation of power or performance state tables for a pre-measured subset of all possible system states. Approaches to determine performance and power for a state not included in the measured subset use simple interpolation, such as nearest neighbor interpolation, or define state switching rules. This leads to a loss in accuracy, as unmeasured system states are not considered. In this paper, we compare different interpolation functions and automatically configure and select functions for a given domain or measurement set. We evaluate our approach by comparing interpolation of measurement data subsets against power and performance measurements on a commodity server. We show that for non-extrapolating models interpolation is significantly more accurate than regression, with our automatically configured interpolation function improving modeling accuracy up to 43.6%.}

Univariate Interpolation Library

Mailing List

Publications

Data privacy protection

Data privacy protection

Picture credits

Your E-mail address:
Your Name (optional):