Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Three-dimensional alpha shapes
ACM Transactions on Graphics (TOG)
Triangulating the surface of a molecule
Discrete Applied Mathematics - Special volume on computational molecular biology
A Generalization of Algebraic Surface Drawing
ACM Transactions on Graphics (TOG)
Multithreaded Isosurface Rendering on SMPs Using Span-Space Buckets
ICPP '02 Proceedings of the 2002 International Conference on Parallel Processing
Massively parallel isosurface extraction
VIS '92 Proceedings of the 3rd conference on Visualization '92
Accelerating marching cubes with graphics hardware
CASCON '06 Proceedings of the 2006 conference of the Center for Advanced Studies on Collaborative research
An efficient and scalable parallel algorithm for out-of-core isosurface extraction and rendering
Journal of Parallel and Distributed Computing
Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation)
Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation)
Triangulation of Molecular Surfaces Using an Isosurface Continuation Algorithm
ICCSA '09 Proceedings of the 2009 International Conference on Computational Science and Its Applications
Three-dimensional beta-shapes and beta-complexes via quasi-triangulation
Computer-Aided Design
Graphics processing unit-based triangulations of Blinn molecular surfaces
Concurrency and Computation: Practice & Experience
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Current GPU-based workstations are inadequate to triangulate and rendering large molecular datasets with thousands and hundreds of thousands, not to say millions, of atoms. The problem is not so the lack of processing power, but the memory limitations of current GPU graphics cards. For example, the NVidia GeForce GTX 590 graphics card comes with two 1.5GB GPUs. We tackle here this problem using a OpenMP-CUDA solution that runs on a loosely-coupled GPU cluster. Basically, we propose a fast, scalable, parallel triangulation algorithm for molecular surfaces that takes advantage of multicore processors of CPUs and GPUs of modern hardware architectures, where each CPU core works as the master of a single GPU, being the processing burden distributed over the CPU cores available in a single computer or a cluster. As much as we know, this is the first marching cubes algorithm that triangulates molecular surfaces on multiple GPUs using CUDA and OpenMP.