Research on Parallel Architecture of OpenCL-Based FPGA

Abstract

Moore’s law encounters a bottleneck today. Computing power of the general purpose processor is restricted. At the same time, new types of enterprise computing such as big data management and analysis bring more challenges to the computational performance and scalability of the data center. Research efforts have been devoted to accelerating algorithm on Field Programmable Gate Arrays (FPGAs), due to their high performance and reprogramming. In this paper, we first study the heterogeneous platform of OpenCL-based FPGA, and propose a novel multi-computing unit combined with internal hardware flow parallel acceleration framework. Then, we evaluate the influences of different number of computing units on performance and resource utilization with the high performance computing applications (AES algorithm) that implemented through the proposed framework. Meanwhile, we compare the performance with CPU implementation. The result shows that our proposed framework has advantages of high performance and scalability for the implementation of a class of algorithms suitable for parallelization, and suits for the demands of data center and high performance computing (HPC) applications.