A parallelization cost model for GPU

Using GPU for general computing has become an important research direction in high performance computing technology. However, this is not a lossless optimization method. Due to the impact of device initialization cost, data transmission delay, specific characteristics of programs, and other factors, the general computing on GPU may not always achieve the desired speedup, and sometimes results in program execution performance degradation. On the basis of in-depth analysis of GPU internal processing mechanisms, the main factors affecting GPU implementation performance are pointed out, and a parallel cost model for GPU based on static program analysis is proposed to provide judgement basis for using GPU in general computing.