Performance Comparison of Graphics Processors to Reconfigurable Logic: A Case Study

A systematic approach to the comparison of the graphics processor (GPU) and reconfigurable logic is defined in terms of three throughput drivers. The approach is applied to five case study algorithms, characterized by their arithmetic complexity, memory access requirements, and data dependence, and two target devices: the nVidia GeForce 7900 GTX GPU and a Xilinx Virtex-4 field programmable gate array (FPGA). Two orders of magnitude speedup, over a general-purpose processor, is observed for each device for arithmetic intensive algorithms. An FPGA is superior, over a GPU, for algorithms requiring large numbers of regular memory accesses, while the GPU is superior for algorithms with variable data reuse. In the presence of data dependence, the implementation of a customized data path in an FPGA exceeds GPU performance by up to eight times. The trends of the analysis to newer and future technologies are analyzed.