Title
Performance prediction for families of data-intensive software applications
Author
Verriet, J.
Dankers, R.
Somers, L.
Publication year
2018
Abstract
Performance is a critical system property of any system, in particular of data-intensive systems, such as image processing systems. We describe a performance engineering method for families of data-intensive systems that is both simple and accurate; the performance of new family members is predicted using models of existing family members. The predictive models are calibrated using static code analysis and regression. Code analysis is used to extract performance profiles, which are used in combination with regression to derive predictive performance models. A case study presents the application for an industrial image processing case, which revealed as benefits the easy application and identification of code performance optimization points.
Subject
ICT
ESI - Embedded Systems Innovations
TS - Technical Sciences
Informatics
Industrial Innovation
Software performance engineering
Loop analysis
Product families
Data-intensive systems
To reference this document use:
http://resolver.tudelft.nl/uuid:08d568f1-c98e-4c75-9a18-2e136b1995ad
DOI
https://doi.org/10.1145/3185768.3186405
TNO identifier
785814
Publisher
ACM
ISBN
9781450356299
Source
9th ACM/SPEC International Conference on Performance Engineering (ICPE 2018), Berlin, Germany - April 9-13, 2018
Document type
conference paper