Abstract:

The emerging and the development of the GPGPU afford the massive computation power to the parallel applications. How to use this computation power efficiently relies on the optimization of the applications. The performance models play an important roles on the targeting the performance bottleneck, guiding the optimization strategies, load balancing with other devices, etc. The stateofart of the GPGPU performance model is described, categorized, and analyzed in details. The models are divided into statisticbased curvefitting model and analysis model. The latter one can further be divided into metric model, computation and memory parallel aware model and componentbased quantitative model. The pros and cons of all the models are analyzed, and an interpolation performance model based on statistics is implemented. Finally the unsolved problems and the future challenges are presented.

Key words: GPGPU;GPU;performance model