Optimizing OpenCL Code for Performance on FPGA: k-Means Case Study With Integer Data Sets

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Nuno Paulino (Author) FEUP; João Canas Ferreira (Author) FEUP; João M. P. Cardoso (Author) FEUP

Journal

Title: IEEE Access

Vol. 8

Pages: 152286 -152304

ISSN: 2169-3536

Publisher: IEEE

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 5 Citations

Other information

Authenticus ID: P-00S-P0Z

DOI: 10.1109/access.2020.3017552

Abstract (EN): High Level Synthesis (HLS) tools targeting Field Programmable Gate Arrays (FPGAs) aim to provide a method for programming these devices via high-level abstractions. Initially, HLS support for FPGAs focused on compiling C/C CC to hardware circuits. This raised the issue of determining the programming practices which resulted in the best performing circuits. Recently, to further increase the applicability of HLS approaches, renewed effort was placed on support for HLS of OpenCL code for FPGA, raising the same issues of coding practices and performance portability. This paper explores the performance of OpenCL code compiled for FPGAs for different coding techniques. We evaluate the use of task-kernels versus NDRange kernels, data vectorization, the use of on-chip local memories, and data transfer optimizations by exploiting burst access inference. We present this exploration via a case study of the k-means algorithm, and produce a total of 10 OpenCL implementations of the kernel. To determine the effects of different data set characteristics, and to determine the gains from specialization based on number of attributes, we generated a total of 12 integer data sets. The data sets vary regarding the number of instances, number of attributes (i.e., features), and number of clusters. We also vary the number of processing cores, and present the resulting required resources and operating frequencies. Finally, we execute the same OpenCL code on a 4 GHz Intel i7-6700K CPU, showing that the FPGA achieves speedups up to 1.54 x for four cases, and energy savings up to 80% in all cases.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 19