Issue No. 04 - April (2010 vol. 59)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TC.2009.179
Ben Cope , Imperial College London , London
Peter Y.K. Cheung , Imperial College London, London
Wayne Luk , Imperial College London, London
Lee Howes , Imperial College London
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.
Graphics processors, reconfigurable hardware, real-time and embedded systems, signal processing systems, performance measures, video.
P. Y. Cheung, W. Luk, B. Cope and L. Howes, "Performance Comparison of Graphics Processors to Reconfigurable Logic: A Case Study," in IEEE Transactions on Computers, vol. 59, no. , pp. 433-448, 2009.