This paper presents a performance study of a nonrigid registration algorithm for investigating lung disease on clusters. Our algorithm combines two conventional acceleration techniques in order to achieve fast registration: a data-parallel processing technique for accelerating the registration procedure; and a precomputation technique for reducing the computational complexity. We perform some experiments on three clusters with different CPU and network performance in order to make clear what kinds of acceleration techniques and computing environments provide higher performance. The results show that a cluster with Gigabit Ethernet (GbE) network is the most cost effective solution that reduces registration time from ten hours to ten minutes with a linear speedup.