Software based ultrasound B-mode/beamforming optimization on GPU and its performance prediction

Abstract

In the paper, we design and optimize an ultrasound B-mode imaging including a high-computationally demanding beamformer on a commercial GPU. For the performance optimization, we explore the design space spanned with the use of different memory types, instruction scheduling and thread mapping strategies, etc. Then, with the developed B-mode imaging code, we conduct performance evaluations on various GPUs having different architectural features (e.g., the number of cores and core frequency). Through the experiments on various different GPU devices, we search “performance-significant-factors” which are hardware features of affecting B-mode imaging performance. Then, the analytical relationship between these GPU architectural design factors and the B-mode imaging performance is derived for our target application. At the commercial aspect of developing a product, we can select GPU architectures which are best suitable for the ultrasound applications through the prediction model. In the future, using the predictions, it would be also possible to customize a “cost-minimal” GPU architecture which satisfies a given performance constraint. In addition, the prediction model can be used to dynamically control the activity of GPU components according to the temporal requirement of performance and power/energy consumptions in portable ultrasound diagnosis systems.