Larissa Schmid, Marcin Copik, Alexandru Calotoiu, Dominik Werle, Andreas Reiter, Michael Selzer, Anne Koziolek, Torsten Hoefler:
Performance-Detective: Automatic Deduction of Cheap and Accurate Performance Models
(In Proceedings of the 2022 International Conference on Supercomputing (ICS'22), Jul. 2022)
Abstract
The many configuration options of modern applications make it difficult for users to select a performance-optimal configuration. Performance models help users in understanding system performance and choosing a fast configuration. Existing performance modeling approaches for applications and configurable systems either require a full-factorial experiment design or a sampling design based on heuristics. This results in high costs for achieving accurate models. Furthermore, they require repeated execution of experiments to account for measurement noise. We propose Performance-Detective, a novel code analysis tool that deduces insights on the interactions of program parameters. We use the insights to derive the smallest necessary experiment design and avoiding repetitions of measurements when possible, significantly lowering the cost of performance modeling. We evaluate Performance-Detective using two case studies where we reduce the number of measurements from up to 3125 to only 25, decreasing cost to only 2.9% of the previously needed core hours, while maintaining accuracy of the resulting model with 91.5% compared to 93.8% using all 3125 measurements.
Documents
download article: download slides:
Recorded talk (best effort)
BibTeX
@inproceedings{, author={Larissa Schmid and Marcin Copik and Alexandru Calotoiu and Dominik Werle and Andreas Reiter and Michael Selzer and Anne Koziolek and Torsten Hoefler}, title={{Performance-Detective: Automatic Deduction of Cheap and Accurate Performance Models}}, year={2022}, month={Jul.}, booktitle={Proceedings of the 2022 International Conference on Supercomputing (ICS'22)}, source={http://www.unixer.de/~htor/publications/}, }
