Title
Evaluating and Optimizing OpenCL Base64 Data Unpacking Kernel with FPGA
Abstract
Development of applications using OpenCL targeting FPGAs is an emerging approach on heterogeneous computing systems. This paper uses the data unpacking algorithm in Base64 encoding as a case study to present programming and optimization techniques, and experimental results of the OpenCL-based implementations on an FPGA. We explain the algorithm and evaluate the performance of the kernel implementations with Intel's FPGA OpenCL SDK. The experimental results show kernel vectorization and duplication are two optimization techniques that can improve the kernel performance. The performance of kernel duplication is also closely related to the local work size. Our experiment shows 16-lane vectorization increases the bandwidth by a factor of 2 to 10 for large input data sizes. Moreover, the performance of kernel duplication using 16 compute units is 40% to 1.5% less than that of kernel vectorization depending on the input size. Tuning the local work size can improve the kernel performance by a factor of 3 to 23. For this kernel, using local memory is not an effective technique to improve the kernel performance because input data is not reused. A combination of vectorization and duplication achieves the highest performance of 12.3 GiB/s. Compared to an Intel Xeon E5 CPU and an Nvidia Tesla K80 GPU, the performance of the kernel on the Arria 10 FPGA is 6.7X faster than the CPU and 3X slower than the GPU. The performance per watt on the FPGA is 20.5X higher than the CPU and 1.19X lower than the GPU.
Year
DOI
Venue
2018
10.1109/PDP2018.2018.00046
2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)
Keywords
Field
DocType
FPGA,OpenCL,Base64 Encoding
Kernel (linear algebra),Central processing unit,Computer science,Parallel computing,Symmetric multiprocessor system,Vectorization (mathematics),Field-programmable gate array,Performance per watt,Xeon,Unpacking
Conference
ISSN
ISBN
Citations 
1066-6192
978-1-5386-4976-3
1
PageRank 
References 
Authors
0.36
4
3
Name
Order
Citations
PageRank
Zheming Jin11711.95
Iris Johnson210.36
Hal Finkel363.21