Abstract
Scientific applications represent a dominant sector of compute-intensive applications. Using massively parallel processing systems increases the feasibility to automate such applications because of the cooperation among multiple processors to perform the designated task. This paper proposes a parallel hidden Markov model (HMM) algorithm for 3D magnetic resonance image brain segmentation using two approaches. In the first approach, a hierarchical/multilevel parallel technique is used to achieve high performance for the running algorithm. This approach can speed up the computation process up to 7.8x compared with a serial run. The second approach is orthogonal to the first and tries to help in obtaining a minimum error for 3D magnetic resonance image brain segmentation using multiple processes with different randomization paths for cooperative fast minimum error convergence. This approach achieves minimum error level for HMM training not achievable by the serial HMM training on a single node. Then both approaches are combined to achieve both high accuracy and high performance simultaneously. For 768 processing nodes of a Blue Gene system, the combined approach, which uses both methods cooperatively, can achieve high-accuracy HMM parameters with 98% of the error level and 2.6x speedup compared with the pure accuracy-oriented approach alone. Copyright (c) 2012 John Wiley & Sons, Ltd.