Optimizing Parallel Reduction on OpenCL FPGA Platform – A Case Study of Frequent Pattern Compression
Zheming Jin, Hal Finkel · 2018
Field-programmable gate arrays (FPGAs) are becoming a promising heterogeneous computing component in high-performance computing. To facilitate the usage of FPGAs for developers and researchers, high-level synthesis tools are pushing the FPGA-based design abstraction from the registertransfer level to high-level language design flow using OpenCL/C/C++. Currently, there are few studies on parallel reduction using atomic functions in the OpenCL-based design flow on an FPGA. Inspired by the reduction operation in frequent pattern compression, we transform the function into an OpenCL kernel, and describe the optimizations of the kernel on an Arria10-based FPGA platform as a case study. We found that automatic kernel vectorization does not improve the kernel performance. Users can manually vectorize the kernel to achieve performance speedup. Overall, our optimizations improve the kernel performance by a factor of 11.9 over the baseline kernel. The performance per watt of the kernel on an Intel Arria 10 GX1150 FPGA is 5.3X higher than an Intel Xeon 16-core CPU while 0.625X lower than an Nvidia K80 GPU.