Multi-GPU acceleration of DARTEL (early detection of Alzheimer)

Abstract

Medical image processing is becoming a significant discipline within the bioinformatic community. In particular, deformable registration methods are one of the most sophisticate and important lines of research within biomedical image processing, due to the valuable information provided. However, these methods consume considerable processing time, power consumption and require high amounts of memory. Current Graphics Processing Units (GPU) have a high number of cores and high memory bandwidth, providing an excellent platform for reducing the cost of these methods in terms of processing time and power consumption. This work proposes several Graphics Processing Units GPU-based implementations of one of the most sophisticated deformable registration algorithms, DARTEL. The main contribution consists of a new GPU approach, which considerably reduces the overhead caused by memory transfers and the computational cost required by the parallelization of DARTEL. Furthermore, the use of multiple (2 and 4) GPUs is studied, achieving favorable results. This new approach provides a high speedup with respect to the sequential counterpart. Finally, the experimental results show a processing time reduction of more than 3 hours in typical cases of study. Additionally, this new approach significantly reduces power consumption.