Sparse matrix-vector multiplication on GPGPUs

Simple item page
dc.contributor.authorFilippone, Salvatore
dc.date.accessioned2017-03-22T11:25:26Z
dc.date.available2017-03-22T11:25:26Z
dc.date.issued2017-03-01
dc.description.abstractThe multiplication of a sparse matrix by a dense vector (SpMV) is a centerpiece of scientific computing applications: it is the essential kernel for the solution of sparse linear systems and sparse eigenvalue problems by iterative methods. The efficient implementation of the sparse matrix-vector multiplication is therefore crucial and has been the subject of an immense amount of research, with interest renewed with every major new trend in high performance computing architectures. The introduction of General Purpose Graphics Processing Units (GPGPUs) is no exception, and many articles have been devoted to this problem. With this paper we provide a review of the techniques for implementing the SpMV kernel on GPGPUs that have appeared in the literature of the last few years. We discuss the issues and trade-offs that have been encountered by the various researchers, and a list of solutions, organized in categories according to common features. We also provide a performance comparison across different GPGPU models and on a set of test matrices coming from various application domains.en_UK
dc.identifier.citationFilippone S, Cardellini V, Barbieri D, Fanfarillo A. (2017) Sparse matrix-vector multiplication on GPGPUs. ACM Transactions on Mathematical Software, Volume 43, Issue 4, March 2017, Article 30en_UK
dc.identifier.issn0098-3500
dc.identifier.urihttp://dx.doi.org/10.1145/3017994
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/11636
dc.language.isoenen_UK
dc.publisherAssociation for Computing Machinery (ACM)en_UK
dc.rightsAttribution-Non-Commercial 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/
dc.titleSparse matrix-vector multiplication on GPGPUsen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Sparse_matrix_vector_multiplication_on_GPGPUs-2017.pdf
Size:
992.28 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.79 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)