Fuzzy classification of OpenCL programs targeting heterogeneous systems

Article type: Research Article

Authors: Al-Zoubi, Ahmad^{; *} | Tatas, Konstantinos | Kyriacou, Costas

Affiliations: Department of Electrical and Computer Engineering and Informatics, Frederick University, Nicosia, Cyprus

Correspondence: [*] Corresponding author. Ahmad Al-Zoubi, Department of Electrical and Computer Engineering and Informatics, Frederick University, Nicosia, Cyprus. E-mail: st012422@stud.frederick.ac.cy.

Abstract: Heterogeneous systems featuring multiple kinds of processors are becoming increasingly attractive due to their high performance and energy savings over their homogeneous counterparts. With the OpenCL as a unified programming language providing program portability across different types of accelerators, finding the best task-to-device mapping will be the key to achieve such a high performance. We introduce in this work the design of a fuzzy logic classifier and the evaluation of its performance in classifying OpenCL workloads in a CPU-GPU-FPGA heterogeneous environment based on carefully analyzed kernel features. The classifier is designed as part of a scheduling scheme. Results demonstrate substantial improvement in accuracy when compared to other classifiers such as the K-Nearest- Neighbor (KNN), Support-Vector-Machine (SVM), Random-Forest (RF), Naïve-Bayes (NB) and the Bayes-Network (BN) with low computational complexity, facilitating run-time operation.

Keywords: Fuzzy Logic, Heterogeneous, Classification, OpenCL

DOI: 10.3233/JIFS-200616

Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7189-7202, 2020