An efficient and scalable parallel algorithm for out-of-core isosurface extraction and rendering

Wang, Qin; JáJá, Joseph F.; Varshney, Amitabh

doi:10.1016/j.jpdc.2006.12.007

Cited by 13 publications

(3 citation statements)

References 19 publications

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“…Introduced in [WJV06], the compact interval tree is a simple indexing structure that enables the extraction of isosurfaces by accessing only active metacells. It is a variation of the interval tree but makes use of the concepts of span space and metacells, and can be used for both structured and unstructured grids.…”

Section: A Simple Optimal Out-of-core Isosurface Extraction Algorithmmentioning

confidence: 99%

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Parallel Algorithms for Volumetric Surface Construction

JáJá¹,

Shi²,

Varshney³

2007

Handbook of Parallel Computing

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SummaryLarge scale scientific data sets are appearing at an increasing rate whose sizes can range from hundreds of gigabytes to tens of terabytes. Isosurface extraction and rendering is an important visualization technique that enables the visual exploration of such data sets using surfaces.However the computational requirements of this approach are substantial which in general prevent the interactive rendering of isosurfaces for large data sets. Therefore, parallel and distributed computing techniques offer a promising direction to deal with the corresponding computational challenges. In this chapter, we give a brief historical perspective of the isosurface visualization approach, and describe the basic sequential and parallel techniques used to extract and render isosurfaces with a particular focus on out-of-core techniques. For parallel algorithms, we assume a distributed memory model in which each processor has its own local disk, and processors communicate and exchange data through an interconnection network. We present a general framework for evaluating parallel isosurface extraction algorithms and describe the related best known parallel algorithms. We also describe the main parallel strategies used to handle isosurface rendering, pointing out the limitations of these strategies.

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Section: A Simple Optimal Out-of-core Isosurface Extraction Algorithmmentioning

confidence: 99%

“…A Scalable and Efficient Parallel Algorithm for Isosurface Extraction ibed in Section 3, its or each index in the structure, we stripe the metacells stored in a brick across the p disks, that is, structure reported in [WJV06].…”

Section: Range Partitioning Based Algorithmsmentioning

confidence: 99%

Parallel Algorithms for Volumetric Surface Construction

JáJá¹,

Shi²,

Varshney³

2007

Handbook of Parallel Computing

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“…Fast computing is critical in biomolecular modeling [43, 45]. A variety of algorithms were developed in improving the modeling efficiency.…”

Section: Introductionmentioning

confidence: 99%

Multi-core CPU or GPU-accelerated Multiscale Modeling for Biomolecular Complexes

Liao¹,

Zhang²,

Kekenes‐Huskey³

et al. 2013

Computational and Mathematical Biophysics

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Multi-scale modeling plays an important role in understanding the structure and biological functionalities of large biomolecular complexes. In this paper, we present an efficient computational framework to construct multi-scale models from atomic resolution data in the Protein Data Bank (PDB), which is accelerated by multi-core CPU and programmable Graphics Processing Units (GPU). A multi-level summation of Gaus-sian kernel functions is employed to generate implicit models for biomolecules. The coefficients in the summation are designed as functions of the structure indices, which specify the structures at a certain level and enable a local resolution control on the biomolecular surface. A method called neighboring search is adopted to locate the grid points close to the expected biomolecular surface, and reduce the number of grids to be analyzed. For a specific grid point, a KD-tree or bounding volume hierarchy is applied to search for the atoms contributing to its density computation, and faraway atoms are ignored due to the decay of Gaussian kernel functions. In addition to density map construction, three modes are also employed and compared during mesh generation and quality improvement to generate high quality tetrahedral meshes: CPU sequential, multi-core CPU parallel and GPU parallel. We have applied our algorithm to several large proteins and obtained good results.

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Triangulating Gaussian-Like Surfaces of Molecules with Millions of Atoms

Dias

Gomes

2014

Computational Electrostatics for Biological Applications

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An efficient and scalable parallel algorithm for out-of-core isosurface extraction and rendering

Cited by 13 publications

References 19 publications

Parallel Algorithms for Volumetric Surface Construction

Parallel Algorithms for Volumetric Surface Construction

Multi-core CPU or GPU-accelerated Multiscale Modeling for Biomolecular Complexes

Triangulating Gaussian-Like Surfaces of Molecules with Millions of Atoms

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