Mapping Multi-Layer Baysian LDA to Massively Parallel Supercomputers
2011-10-10
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Mapping Multi-Layer Baysian LDA to Massively Parallel Supercomputers
Published Date
2011-10-10
Publisher
Type
Report
Abstract
LDA, short for Latent Dirichlet Allocation, is a hierarchical Bayesian model for content analysis. LDA has seen a wide variety of applications, but it also presents computational challenges because the iterative computation of approximate inference is required. Recently an approach based on Gibbs Sampling and MPI is proposed to address these challenges, while this report presents the work that maps it to a massively parallel supercomputer, Blue Gene. The work enhances the runtime performance by utilizing special hardware architecture of Blue Gene such as dual floating-point unit and by using general programming/compiling techniques such as loop unfolding. Results from the empirical evaluation using a real-world large-scale data set indicate the following findings: First, the use of dual floating-point unit contributes to a significant performance gain, and thus it should be considered in the design of processors for computationally intensive machine learning applications. Second, although it is a simple technique and most compilers support it, loop unfolding improves the performance gain even further. Since loop unfolding is general enough to be applied to other platforms, this report suggests that compilers should perform loop unfolding in a more intelligent manner.
Keywords
Description
Related to
Replaces
License
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Suggested citation
Hsu, Kuo-Wei; Lin, Ching-Yung; Srivastava, Jaideep. (2011). Mapping Multi-Layer Baysian LDA to Massively Parallel Supercomputers. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215870.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.