A parallel isosurface extraction component for visualization pipelines executing on GPU clusters

D’Agostino, Daniele; Seinstra, F.J.

doi:10.1016/j.cam.2014.05.019

Cited by 5 publications

(7 citation statements)

References 11 publications

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“…Resource discovery requirements for such future systems are beyond the techniques used in today's Grids, Clouds and HPC clusters. We were inspired by the concept of "Jungle Computing", introduced in the recent years [1][2][3][4][5][6][7][8], and accordingly, we can envision that most of requirements for Jungle can be applied for future computing systems. In fact, two important features of such future environments are: first, heterogeneity of resources; and, second, very large number of resources.…”

Section: Motivationmentioning

confidence: 99%

“…Listing 2 presents a description example, using Grammar [5] PT "Processor Type" : bitstring(2,value) {"CPU"=0, "GPU"=1, "FPGA"=2, "Others"=3} , [6] ISA "Instruction Set Architecture" : bitstring(3,value) {" X86"=0, "SPARC"=1, "ARM"=2, "XCORE"=3, "RISC"=4, "CISC"=5, "Legacy"=6, "Others"=7} end layer Layer3 [7] MS "Memory Size by MB" : number, [8] DC "Die Count" : number end attributes [9] INB "Inter-node Bandwidth Mb/S" : number, [10] INL "Inter-node Latency by ns" : number, [11] IGB "Inter-group Bandwidth Mb/S" : number, [12] IGL "Inter-group Latency by ns" : number end end Listing 2: An example of definitions for layers and attributes in .def files…”

Section: Defining Layers and Attributesmentioning

confidence: 99%

“…Moreover, data distribution, hardware availability, software heterogeneity, and also the sheer size of scientific problems, commonly force scientists to resort to Jungle Computing instead of traditional supercomputers and clusters. Jungle Computing (i.e., leveraging multiple computing platforms simultaneously) is a recent distributed computing paradigm based on concurrent combination of various hierarchical and distributed computing environments with large number of heterogeneous resources [1][2][3][4][5][6][7][8]) .…”

Section: Introductionmentioning

confidence: 99%

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HARD: Hybrid Adaptive Resource Discovery for Jungle Computing

Zarrin

Aguiar

Barraca

2017

Journal of Network and Computer Applications

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractIn recent years, Jungle Computing has emerged as a distributed computing paradigm based on simultaneous combination of various hierarchical and distributed computing environments which are composed by large number of heterogeneous resources. In such a computing environment, the resources and the underlying computation and communication infrastructures are highly-hierarchical and heterogeneous. This creates a lot of difficulty and complexity for finding the proper resources in a precise way in order to run a particular job on the system efficiently. This paper proposes Hybrid Adaptive Resource Discovery (HARD), a novel efficient and highly scalable resource-discovery approach which is built upon a virtual hierarchical overlay based on self-organization and self-adaptation of processing resources in the system, where the computing resources are organized into distributed hierarchies according to a proposed hierarchical multi-layered resource description model. The proposed approach supports distributed query processing within and across hierarchical layers by deploying various distributed resource discovery services and functionalities in the system which are implemented using different adapted algorithms and mechanisms in each level of hierarchy. The proposed approach addresses the requirements for resource discovery in Jungle Computing environments such as high-hierarchy, high-heterogeneity, high-scalability and dynamicity. Simulation results show significant scalability and efficiency of the proposed approach over highly heterogeneous, hierarchical and dynamic computing environments.Keywords: distributed operation systems, many-core systems, P2P, resource management, grid computing, DHT

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Section: Motivationmentioning

confidence: 99%

Section: Defining Layers and Attributesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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HARD: Hybrid Adaptive Resource Discovery for Jungle Computing

Zarrin

Aguiar

Barraca

2017

Journal of Network and Computer Applications

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“…Obviously, this application is only an example, and it has many limitations, such as for instance, the small size of the volume that is able to process. Other proposals were published, with the main aim to speedup the processing of extracting and visualizing very large isosurfaces (refer to for a survey). The main issue with these algorithms is the fact that they are designed for a direct visualization of the produced isosurfaces and not for storing them.…”

Section: Cuda Accelerated Molecular Surface Generationmentioning

confidence: 99%

“…In , we proposed a novel algorithm that is able to produce an isosurface equivalent to that produced by the sequential algorithm in an efficient way using several improvements, such as the techniques presented in . The coordinates of a vertex are computed only the first time the corresponding edge intersected by the isosurface is considered.…”

Section: Cuda Accelerated Molecular Surface Generationmentioning

confidence: 99%

CUDA accelerated molecular surface generation

D’Agostino

Clematis

Decherchi

et al. 2013

Concurrency and Computation

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SUMMARYA proper and efficient representation of possibly complex and large molecular surfaces is an important task in bioinformatics and biophysics. Molecular surfaces indeed are used for different aims, in particular, for computation, as visual support tools for biologists, in electrostatics problems involving implicit solvents (e.g. while solving the Poisson-Boltzmann equation) or for molecular dynamics simulations. This is the reason why, in literature, a multitude of algorithms that differ on the basis of the adopted representation and the approach/technology used were proposed. In this paper we present a CUDA-based software component able to produce high-resolution molecular surfaces based on the Van der Waals, solvent accessible, RichardsConnolly and blobby definitions. The component was designed to be used in heterogeneous visualization pipelines; therefore, the representation of the molecular surfaces suits both the direct visualization and the efficient storing for following processing steps. Experimental results show speedup figures between 39.3 and 88.4 considering molecules of different sizes and surface resolutions, resulting in meshes of up to 258.6 million triangles.

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A parallel dual marching cubes approach to quad only surface reconstruction

Grosso

Zint

2021

Vis Comput

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We present a novel method that reconstructs surfaces from volume data using a dual marching cubes approach without lookup tables. The method generates quad only meshes which are consistent across cell borders, i.e., they are manifold and watertight. Vertices are positioned exactly on the reconstructed surface almost everywhere, leading to higher accuracy than other reconstruction methods. A halfedge data structure is used for storing the meshes which is convenient for further processing. The method processes elements in parallel and therefore runs efficiently on GPU. Due to the transition between layers in volume data, meshes have numerous vertices with valence three. We use simplification patterns for eliminating quads containing these vertices wherever possible which reduces the number of elements and increases quality. We briefly describe a CUDA implementation of our method, which allows processing huge amounts of data on GPU at almost interactive time rates. Finally, we present runtime and quality results of our method on medical and synthetic data sets.

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A parallel isosurface extraction component for visualization pipelines executing on GPU clusters

Cited by 5 publications

References 11 publications

HARD: Hybrid Adaptive Resource Discovery for Jungle Computing

HARD: Hybrid Adaptive Resource Discovery for Jungle Computing

CUDA accelerated molecular surface generation

A parallel dual marching cubes approach to quad only surface reconstruction

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