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Deffe: A Data-efficient Framework for Performance Characterization in Domain-Specific Computing...

by Frank Y Liu, Narasinga Rao Miniskar, Dwaipayan Chakraborty, Jeffrey S Vetter
Publication Type
Conference Paper
Book Title
Proceedings of ACM International Conference on Computing Frontiers
Publication Date
Conference Name
ACM International Conference on Computing Frontiers 2020
Conference Location
Catania, Italy
Conference Sponsor
Conference Date

Predicting workload performance is a crucial task for many architecture and system research. In this paper we present Deffe, a framework to estimate the workload performances under varying architectural configurations. The infrastructure component of Deffe is based on scalable and easy-to-use open-source software components. By casting the performance modeling as transfer learning tasks, the modeling component of Deffe can leverage the learned knowledge on one workload, and “transfer” it to a new workload. Extensive experimental results show that the method can achieve superior testing accuracy with an effective reduction of 32-80x in terms of the amount of required training data.