Accelerating sparse canonical correlation analysis for large brain imaging genetics data

Jingwen Yan, Hui Zhang, Lei Du, Eric Wernert, Andrew J. Saykin, Li Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Recent advances in acquiring high throughput neuroimaging and genomics data provide exciting new opportunities to study the influence of genetic variation on brain structure and function. Research in this emergent field, known as imaging genetics, aims to identify the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs). Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. However, the scale and complexity of the imaging genetic data have presented critical computational bottlenecks requiring new concepts and enabling tools. In this paper, we present our initial efforts on developing a set of massively parallel strategies to accelerate a widely used SCCA implementation provided by the Penalized Multivariate Analysis (PMA) software package. In particular, we exploit parallel packages of R, optimized mathematical libraries, and the automatic offload model for Intel Many Integrated Core (MIC) architecture to accelerate SCCA. We create several simulated imaging genetics data sets of different sizes and use these synthetic data to perform comparative study. Our performance evaluation demonstrates that a 2-fold speedup can be achieved by the proposed acceleration. The preliminary results show that by combining data parallel strategy and the offload model for MIC we can significantly reduce the knowledge discovery timelines involving applying SCCA on large brain imaging genetics data.

Original languageEnglish (US)
Title of host publicationProceedings of the XSEDE 2014 Conference
Subtitle of host publicationEngaging Communities
PublisherAssociation for Computing Machinery
ISBN (Print)9781450328937
DOIs
StatePublished - Jan 1 2014
Event2014 Annual Conference on Extreme Science and Engineering Discovery Environment, XSEDE 2014 - Atlanta, GA, United States
Duration: Jul 13 2014Jul 18 2014

Publication series

NameACM International Conference Proceeding Series

Other

Other2014 Annual Conference on Extreme Science and Engineering Discovery Environment, XSEDE 2014
CountryUnited States
CityAtlanta, GA
Period7/13/147/18/14

Keywords

  • Brain imaging genetics
  • Parallel computing
  • R
  • Sparse canonical correlation analysis

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Yan, J., Zhang, H., Du, L., Wernert, E., Saykin, A. J., & Shen, L. (2014). Accelerating sparse canonical correlation analysis for large brain imaging genetics data. In Proceedings of the XSEDE 2014 Conference: Engaging Communities [4] (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/2616498.2616515