VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures

Kenneth Moreland; Christopher Sewell; William Usher; Li Ta Lo; Jeremy Meredith; David Pugmire; James Kress; Hendrik Schroots; Kwan Liu Ma; Hank Childs; Matthew Larsen; Chun Ming Chen; Robert Maynard; Berk Geveci

doi:10.1109/MCG.2016.48

VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures

Kenneth Moreland, Christopher Sewell, William Usher, Li Ta Lo, Jeremy Meredith, David Pugmire, James Kress, Hendrik Schroots, Kwan Liu Ma, Hank Childs, Matthew Larsen, Chun Ming Chen, Robert Maynard, Berk Geveci

Visualization

Research output: Contribution to journal › Article › peer-review

113 Scopus citations

Abstract

One of the most critical challenges for high-performance computing (HPC) scientific visualization is execution on massively threaded processors. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Our current production scientific visualization software is not designed for these new types of architectures. To address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.

Original language	English
Article number	7466740
Pages (from-to)	48-58
Number of pages	11
Journal	IEEE Computer Graphics and Applications
Volume	36
Issue number	3
DOIs	https://doi.org/10.1109/MCG.2016.48
State	Published - May 1 2016

Keywords

VTK-m framework
algorithmic structures
computer graphics
high-performance computing
massively threaded processors
parallel algorithms
visualization software

Access to Document

10.1109/MCG.2016.48

Cite this

Moreland, K., Sewell, C., Usher, W., Lo, L. T., Meredith, J., Pugmire, D., Kress, J., Schroots, H., Ma, K. L., Childs, H., Larsen, M., Chen, C. M., Maynard, R., & Geveci, B. (2016). VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures. IEEE Computer Graphics and Applications, 36(3), 48-58. Article 7466740. https://doi.org/10.1109/MCG.2016.48

@article{bab9ae1466524aa9a124337eb95b0e95,

title = "VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures",

abstract = "One of the most critical challenges for high-performance computing (HPC) scientific visualization is execution on massively threaded processors. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Our current production scientific visualization software is not designed for these new types of architectures. To address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.",

keywords = "VTK-m framework, algorithmic structures, computer graphics, high-performance computing, massively threaded processors, parallel algorithms, visualization software",

author = "Kenneth Moreland and Christopher Sewell and William Usher and Lo, {Li Ta} and Jeremy Meredith and David Pugmire and James Kress and Hendrik Schroots and Ma, {Kwan Liu} and Hank Childs and Matthew Larsen and Chen, {Chun Ming} and Robert Maynard and Berk Geveci",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.",

year = "2016",

month = may,

day = "1",

doi = "10.1109/MCG.2016.48",

language = "English",

volume = "36",

pages = "48--58",

journal = "IEEE Computer Graphics and Applications",

issn = "0272-1716",

publisher = "Institute of Electrical and Electronics Engineers",

number = "3",

}

TY - JOUR

T1 - VTK-m

T2 - Accelerating the Visualization Toolkit for Massively Threaded Architectures

AU - Moreland, Kenneth

AU - Sewell, Christopher

AU - Usher, William

AU - Lo, Li Ta

AU - Meredith, Jeremy

AU - Pugmire, David

AU - Kress, James

AU - Schroots, Hendrik

AU - Ma, Kwan Liu

AU - Childs, Hank

AU - Larsen, Matthew

AU - Chen, Chun Ming

AU - Maynard, Robert

AU - Geveci, Berk

PY - 2016/5/1

Y1 - 2016/5/1

N2 - One of the most critical challenges for high-performance computing (HPC) scientific visualization is execution on massively threaded processors. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Our current production scientific visualization software is not designed for these new types of architectures. To address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.

AB - One of the most critical challenges for high-performance computing (HPC) scientific visualization is execution on massively threaded processors. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Our current production scientific visualization software is not designed for these new types of architectures. To address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.

KW - VTK-m framework

KW - algorithmic structures

KW - computer graphics

KW - high-performance computing

KW - massively threaded processors

KW - parallel algorithms

KW - visualization software

UR - http://www.scopus.com/inward/record.url?scp=84969645967&partnerID=8YFLogxK

U2 - 10.1109/MCG.2016.48

DO - 10.1109/MCG.2016.48

M3 - Article

AN - SCOPUS:84969645967

SN - 0272-1716

VL - 36

SP - 48

EP - 58

JO - IEEE Computer Graphics and Applications

JF - IEEE Computer Graphics and Applications

IS - 3

M1 - 7466740

ER -

VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this