의료영상을 위한 위치 기반 역학의 GPU 병렬화 연구
GPU Parallelization Study of Position Based Dynamics for Medical Imaging
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초록
의료영상 데이터를 이용한 의료 시뮬레이션 시스템은 피부조직과 같은 장기의 실제 거동을 모방하기 위해 물리 연산이 필요하다. 의료 시뮬레이션 프로그램은 크기가 큰 데이터를 사용하므로 연산 속도에 대한 제약을 받는다. 본 논문에서는 정밀성과 속도를 적절히 고려하여 위치 기반 역학(Position Based Dynamics, PBD) 기반의 시뮬레이션을 실행한다. 그리고 GPU 병렬 처리를 통해 PBD를 고속으로 움직임을 계산할 수 있도록, Nvidia의 CUDA C/C++를 기반으로 프로그램을 구현하였다. 병렬화 과정에서 발생하는 반복되는 커널 실행의 비효율성 문제를 스레드 독립성을 이용하여 해결하였고, 변형체 해상도에 따른 성능 변화를 CPU와 GPU별로 비교하였다. 또한, CPU 및 GPU를 사용한 병렬 처리에서 병렬성의 한계를 검토하였다. GPU 병렬 처리를 통해 CPU 병렬 처리에서는 불가능한 큰 변형체에 대한 시뮬레이션의 대화적 시간의 확보에 성공하였다.
Abstract
A simulation system using medical image data requires calculation to imitate the actual behavior of an organ such as skin tissue. Medical simulation programs have a problem in that they take a long calculation time because they use large amounts of data. In this paper, we conducted a simulation based on Position Based Dynamics (PBD) with consideration of precision and speed. In addition, we programmed a simulation system based on Nvidia's CUDA C/C++ so that PBD can be calculated at high speed through GPU parallel processing. Using thread independence property, we addressed efficiency of iterative kernel launch in the parallelization process and we compared the performance of CPU and GPU according to the resolution of the deformable object. In addition we examined the efficiency limitations of parallel processing in CPU and GPU. With GPU parallel processing, we succeeded in securing interactive time for simulations on medical deformable object that are not possible in CPU parallel processing.
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