Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Programming with POSIX threads
Programming with POSIX threads
Nonlinear filtering of magnetic resonance tomograms by geometry-driven diffusion
Machine Vision and Applications
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coherence-Enhancing Diffusion Filtering
International Journal of Computer Vision
Message passing and shared address space parallelism on an SMP cluster
Parallel Computing
Journal of Parallel and Distributed Computing
Anisotropic Nonlinear Filtering of Cellular Structures in Cryoelectron Tomography
Computing in Science and Engineering
Performance comparison of MPI and OpenMP on shared memory multiprocessors: Research Articles
Concurrency and Computation: Practice & Experience
Parallel Programming in C with MPI and OpenMP
Parallel Programming in C with MPI and OpenMP
Development of mixed mode MPI / OpenMP applications
Scientific Programming
Three-dimensional feature-preserving noise reduction for real-time electron tomography
Digital Signal Processing
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Anisotropic nonlinear diffusion (AND) is one of the most powerful noise reduction techniques in the field of image processing. This method is based on a partial differential equation (PDE) tightly coupled with a massive set of eigensystems. Denoising large 3D images in biomedicine and structural cellular biology by AND is extremely expensive from a computational point of view, with huge memory needs. In this work, high performance computing techniques have been applied to AND. An strategy for optimal memory usage has been designed, which has allowed a remarkable reduction of the memory requirements. Parallel implementations of AND have been developed with special focus on clusters of symmetric multiprocessors (SMPs), currently a dominant platform in high performance computing. Different programming models have been used for the parallelization of AND: (1) Message-passing paradigm using MPI and (2) a hybrid paradigm that uses message passing among nodes plus the shared address space paradigm between the processors within the nodes. The parallel approaches have been evaluated on a cluster of dual-Xeon nodes, a representative example of clusters of SMPs. The conclusion drawn is that the hybrid approach is the most suitable for the parallelization of AND for this kind of computing platforms.