Multi-GPU Accelerated Admittance Method for High-Resolution Human Exposure Evaluation

作者：Xiong Zubiao; Feng Shi; Kautz Richard; Chandra Sandeep; Altunyurt Nevin; Chen Ji^*

来源：IEEE Transactions on Biomedical Engineering, 2015, 62(12): 2920-2930.

DOI：10.1109/TBME.2015.2453169

• Admittance method
• CUDA
• low-frequency electromagnetic exposure
• multi-GPU
• numerical dosimetry

Objective: A multi-graphics processing unit (GPU) accelerated admittance method solver is presented for solving the induced electric field in high-resolution anatomical models of human body when exposed to external low-frequency magnetic fields. Methods: In the solver, the anatomical model is discretized as a three-dimensional network of admittances. The conjugate orthogonal conjugate gradient (COCG) iterative algorithm is employed to take advantage of the symmetric property of the complex-valued linear system of equations. Compared against the widely used bi-conjugate gradient stabilized method, the COCG algorithm can reduce the solving time by 3.5 times and reduce the storage requirement by about 40%. The iterative algorithm is then accelerated further by using multiple NVIDIA GPUs. The computations and data transfers between GPUs are overlapped in time by using asynchronous concurrent execution design. The communication overhead is well hidden so that the acceleration is nearly linear with the number of GPU cards. Results: Numerical examples show that our GPU implementation running on four NVIDIA Tesla K20c cards can reach 90 times faster than the CPU implementation running on eight CPU cores (two Intel Xeon E5-2603 processors). Conclusion: The implemented solver is able to solve large dimensional problems efficiently. A whole adult body discretized in 1-mm resolution can be solved in just several minutes. Significance: The high efficiency achieved makes it practical to investigate human exposure involving a large number of cases with a high resolution that meets the requirements of international dosimetry guidelines.

• 出版日期2015-12