Performance-Detective: Automatic Deduction of Cheap and Accurate Performance Models
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. Show more
Publication status
publishedExternal links
Book title
ICS '22: Proceedings of the 36th ACM International Conference on SupercomputingPages / Article No.
Publisher
Association for Computing MachineryEvent
Subject
automatic performance modeling; empirical performance modeling; experiment design; configurable systemsOrganisational unit
03950 - Hoefler, Torsten / Hoefler, Torsten
Funding
101002047 - Productive Spatial Accelerator Programming (EC)
170415 - Automatic Performance Modeling of HPC Applications with Multiple Model Parameters (SNF)
More
Show all metadata