Name
Abstract | ||
|---|---|---|
NVIDIA have released a new platform (CUDA) for general purpose computing on their graphical processing units (GPU). This paper evaluates use of this platform for statistical machine learning applications. The transfer rates to and from the GPU are measured, as is the performance of matrix vector operations on the GPU. An implementation of a sparse matrix vector product on the GPU is outlined and evaluated. Performance comparisons are made with the host processor. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-69384-0_52 | ICCS (1) |
Keywords | Field | DocType |
computational sciences,vectors,sparse matrix,robot learning,machine learning | Robot learning,CUDA,Computer science,Matrix (mathematics),Computational science,Artificial intelligence,Host processor,Sparse matrix,Vector operations,General purpose computing,Parallel computing,Stream processing,Machine learning | Conference |
Volume | ISSN | Citations |
5101 | 0302-9743 | 4 |
PageRank | References | Authors |
0.61 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmed H. El Zein | 1 | 4 | 0.61 |
| Eric McCreath | 2 | 132 | 14.64 |
| Alistair P. Rendell | 3 | 209 | 34.55 |
| Alexander J. Smola | 4 | 19627 | 1967.09 |