Solutions For Optimizing The Stream Compaction Algorithmic Function Using The Compute Unified Device Architecture
In this paper, I have researched and developed solutions for optimizing the stream compaction algorithmic function using the Compute Unified Device Architecture (CUDA). The stream compaction is a common parallel primitive, an essential building block for many data processing algorithms, whose optimization improves the performance of a wide class of parallel algorithms useful in data processing. A particular interest in this research was to develop solutions for optimizing the stream compaction algorithmic function that offers optimal solutions over an entire range of CUDA enabled GPUs: Tesla GT200, Fermi GF100 and the latest Kepler GK104 architecture, released on 22 March 2012. In order to confirm the utility of the developed optimization solutions, I have extensively benchmarked and evaluated the performance of the stream compaction algorithmic function in CUDA.
Volume (Year): 6 (2012)
Issue (Month): 1 (May)
|Contact details of provider:|| Postal: |
Web page: http://www.rau.ro/
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Alexandru PIRJAN, 2010. "Improving Software Performance in the Compute Unified Device Architecture," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 14(4), pages 30-47.
When requesting a correction, please mention this item's handle: RePEc:rau:journl:v:6:y:2012:i:1:p:216-231. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Alex Tabusca)
If references are entirely missing, you can add them using this form.