Evaluation of a Floating-Point Intensive Kernel on FPGA - A Case Study of Geodesic Distance Kernel

Heterogeneous platforms provide a promising solution for high-performance and energy-efficient computing applications. This paper presents our research on usage of heterogeneous platform for a floating-point intensive kernel. We first introduce the floating-point intensive kernel from the geographical information system. Then we analyze the FPGA designs generated by the Intel FPGA SDK for OpenCL, and evaluate the kernel performance and the floating-point error rate of the FPGA designs. Finally, we compare the performance and energy efficiency of the kernel implementations on the Arria 10 FPGA, Intel’s Xeon Phi Knights Landing CPU, and NVIDIA’s Kepler GPU. Our evaluation shows the energy efficiency of the single-precision kernel on the FPGA is 1.35X better than on the CPU and the GPU, while the energy efficiency of the double-precision kernel on the FPGA is 1.36X and 1.72X less than the CPU and GPU, respectively.