A new sparse matrix vector multiplication graphics processing unit algorithm designed for finite element problems. (9th January 2015)
- Record Type:
- Journal Article
- Title:
- A new sparse matrix vector multiplication graphics processing unit algorithm designed for finite element problems. (9th January 2015)
- Main Title:
- A new sparse matrix vector multiplication graphics processing unit algorithm designed for finite element problems
- Authors:
- Wong, J.
Kuhl, E.
Darve, E. - Abstract:
- <abstract abstract-type="main" id="nme4865-abs-0001"> <title>Summary</title> <p id="nme4865-para-0001">Recently, graphics processing units (GPUs) have been increasingly leveraged in a variety of scientific computing applications. However, architectural differences between CPUs and GPUs necessitate the development of algorithms that take advantage of GPU hardware. As sparse matrix vector (SPMV) multiplication operations are commonly used in finite element analysis, a new SPMV algorithm and several variations are developed for unstructured finite element meshes on GPUs. The effective bandwidth of current GPU algorithms and the newly proposed algorithms are measured and analyzed for 15 sparse matrices of varying sizes and varying sparsity structures. The effects of optimization and differences between the new GPU algorithm and its variants are then subsequently studied. Lastly, both new and current SPMV GPU algorithms are utilized in the GPU CG solver in GPU finite element simulations of the heart. These results are then compared against parallel PETSc finite element implementation results. The effective bandwidth tests indicate that the new algorithms compare very favorably with current algorithms for a wide variety of sparse matrices and can yield very notable benefits. GPU finite element simulation results demonstrate the benefit of using GPUs for finite element analysis and also show that the proposed algorithms can yield speedup factors up to 12‐fold for real finite<abstract abstract-type="main" id="nme4865-abs-0001"> <title>Summary</title> <p id="nme4865-para-0001">Recently, graphics processing units (GPUs) have been increasingly leveraged in a variety of scientific computing applications. However, architectural differences between CPUs and GPUs necessitate the development of algorithms that take advantage of GPU hardware. As sparse matrix vector (SPMV) multiplication operations are commonly used in finite element analysis, a new SPMV algorithm and several variations are developed for unstructured finite element meshes on GPUs. The effective bandwidth of current GPU algorithms and the newly proposed algorithms are measured and analyzed for 15 sparse matrices of varying sizes and varying sparsity structures. The effects of optimization and differences between the new GPU algorithm and its variants are then subsequently studied. Lastly, both new and current SPMV GPU algorithms are utilized in the GPU CG solver in GPU finite element simulations of the heart. These results are then compared against parallel PETSc finite element implementation results. The effective bandwidth tests indicate that the new algorithms compare very favorably with current algorithms for a wide variety of sparse matrices and can yield very notable benefits. GPU finite element simulation results demonstrate the benefit of using GPUs for finite element analysis and also show that the proposed algorithms can yield speedup factors up to 12‐fold for real finite element applications. Copyright © 2015 John Wiley &amp; Sons, Ltd.</p> </abstract> … (more)
- Is Part Of:
- International journal for numerical methods in engineering. Volume 102:Number 12(2015)
- Journal:
- International journal for numerical methods in engineering
- Issue:
- Volume 102:Number 12(2015)
- Issue Display:
- Volume 102, Issue 12 (2015)
- Year:
- 2015
- Volume:
- 102
- Issue:
- 12
- Issue Sort Value:
- 2015-0102-0012-0000
- Page Start:
- 1784
- Page End:
- 1814
- Publication Date:
- 2015-01-09
- Subjects:
- Numerical analysis -- Periodicals
Engineering mathematics -- Periodicals
620.001518 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
- DOI:
- 10.1002/nme.4865 ↗
- Languages:
- English
- ISSNs:
- 0029-5981
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4542.404000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 3593.xml