Hardware dependencies of GPU-accelerated beamformer performances for microwave breast cancer detection

Abstract

UWB microwave imaging has proven to be a promising technique for early-stage breast cancer detection. The extensive image reconstruction time can be accelerated by parallelizing the execution of the underlying beamforming algorithms. However, the efficiency of the parallelization will most likely depend on the grade of parallelism of the imaging algorithm and of the utilized hardware. This paper investigates the dependencies of two different beamforming algorithms on multiple hardware specification of several graphics boards. The parallel implementation is realized by using NVIDIA’s CUDA. Three conclusions are drawn about the behavior of the parallel implementation and how to efficiently use the accessible hardware.

Keywords

• beamforming
• breast cancer detection
• cuda
• gpgpu
• gpu
• microwave imaging
• nvidia
• uwb