  Recent years have witnessed a rapid advancement in GPU technology,
establishing it as a formidable high-performance parallel computing technology
with superior floating-point computational capabilities compared to traditional
CPUs. This paper explores the application of this technology in the field of
photoacoustic imaging, an emerging non-destructive testing technique in
biomedical engineering characterized by its high contrast, resolution, and
penetration depth. We conduct a data parallelism analysis targeting the
computationally intensive image reconstruction segment of photoacoustic
imaging. By parallelizing the serial code for iterative reconstruction and
optimizing memory access, we achieve significant improvements in processing
speed. Our experiments compare the imaging speeds of vascular images
reconstructed using CPUs and GPUs, with the results visualized using Matlab.
The findings demonstrate that, while maintaining data accuracy, GPU parallel
computing methods can markedly accelerate photoacoustic image reconstruction.
This acceleration has the potential to facilitate the broader adoption of
photoacoustic imaging in applications such as hemodynamic monitoring, clinical
disease diagnosis, and drug development.
