A Fast Iterative Method for Eikonal Equations

In this paper we propose a novel computational technique to solve the Eikonal equation efficiently on parallel architectures. The proposed method manages the list of active nodes and iteratively updates the solutions on those nodes until they converge. Nodes are added to or removed from the list based on a convergence measure, but the management of this list does not entail an extra burden of expensive ordered data structures or special updating sequences. The proposed method has suboptimal worst-case performance but, in practice, on real and synthetic datasets, runs faster than guaranteed-optimal alternatives. Furthermore, the proposed method uses only local, synchronous updates and therefore has better cache coherency, is simple to implement, and scales efficiently on parallel architectures. This paper describes the method, proves its consistency, gives a performance analysis that compares the proposed method against the state-of-the-art Eikonal solvers, and describes the implementation on a single instruction multiple datastream (SIMD) parallel architecture.

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