Univariate Interpolation Library

Update: This tool is no longer maintained and supported.

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. (Link to License)

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 = 12, 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

Update: This tool is no longer maintained and supported.

Univariate Interpolation Library

Mailing List

Publications

Data privacy protection

Data privacy protection

Picture credits

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