Comparing performance and energy efficiency of FPGAs and GPUs for high productivity computing

Abstract

This paper provides the first comparison of performance and energy efficiency of high productivity computing systems based on FPGA (Field-Programmable Gate Array) and GPU (Graphics Processing Unit) technologies. The search for higher performance compute solutions has recently led to great interest in heterogeneous systems containing FPGA and GPU accelerators. While these accelerators can provide significant performance improvements, they can also require much more design effort than a pure software solution, reducing programmer productivity. The CUDA system has provided a high productivity approach for programming GPUs. This paper evaluates the High-Productivity Reconfigurable Computer (HPRC) approach to FPGA programming, where a commodity CPU instruction set architecture is augmented with instructions which execute on a specialised FPGA co-processor, allowing the CPU and FPGA to co-operate closely while providing a programming model similar to that of traditional software. To compare the GPU and FPGA approaches, we select a set of established benchmarks with different memory access characteristics, and compare their performance and energy efficiency on an FPGA-based Hybrid-Core system with a GPU-based system. Our results show that while GPUs excel at streaming applications, high-productivity reconfigurable computing systems outperform GPUs in applications with poor locality characteristics and low memory bandwidth requirements.