A fast linear complementarity problem solver for fluid animation using high level algebra interfaces for GPU libraries. (December 2017)
- Record Type:
- Journal Article
- Title:
- A fast linear complementarity problem solver for fluid animation using high level algebra interfaces for GPU libraries. (December 2017)
- Main Title:
- A fast linear complementarity problem solver for fluid animation using high level algebra interfaces for GPU libraries
- Authors:
- Andersen, Michael
Niebe, Sarah
Erleben, Kenny - Abstract:
- Highlights: A parallel, easy to implement, fluid linear complementarity problem solver is presented. Implementation requires no expert GPU programming experience. Speedup factors of up to 500 are reported. Graphical abstract: Abstract: We address the task of computing solutions for a separating solid wall boundary condition model. We present a parallel, easy to implement, fluid linear complementarity problem solver. All that is needed is the implementation of linear operators, using an existing high-level sparse algebra GPU library. No low-level GPU programming is necessary. This means we can rely on the efficiency of a tried-and-tested library, requiring minimal debugging compared to writing more low level GPU kernels. The solver exploits matrix-vector products as computational building blocks. We block the matrix-vector products in a way that allows us to evaluate the products, without having to assemble the full systems. Our work shows speedup factors ranging up to two orders of magnitudes for larger grid resolutions.
- Is Part Of:
- Computers & graphics. Volume 69(2017)
- Journal:
- Computers & graphics
- Issue:
- Volume 69(2017)
- Issue Display:
- Volume 69, Issue 2017 (2017)
- Year:
- 2017
- Volume:
- 69
- Issue:
- 2017
- Issue Sort Value:
- 2017-0069-2017-0000
- Page Start:
- 36
- Page End:
- 48
- Publication Date:
- 2017-12
- Subjects:
- Fluid animation -- Separating solid wall boundary conditions -- Newton method -- Easy GPU implementation
Computer graphics -- Periodicals
006.6 - Journal URLs:
- http://www.elsevier.com/journals ↗
- DOI:
- 10.1016/j.cag.2017.09.006 ↗
- Languages:
- English
- ISSNs:
- 0097-8493
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.700000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 5595.xml