Accelerating Sparse Canonical Correlation Analysis for Large Brain Imaging Genetics Data
Authors: 
Jingwen Yan, Hui Zhang, Lei Du, Eric Wernert, Andrew J. Saykin, 
Li ShenAuthors Info & Claims
Jingwen Yan, Hui Zhang, Lei Du, Eric Wernert, Andrew J. Saykin, Li ShenAuthors Info & ClaimsArticle No.: 4, Pages 1 - 7
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.
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- Accelerating Sparse Canonical Correlation Analysis for Large Brain Imaging Genetics Data
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- NSF: National Science Foundation
- Drexel University
- Indiana University: Indiana University
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Published: 13 July 2014
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XSEDE '14
XSEDE '14: Annual Conference of the Extreme Science and Engineering Discovery Environment
July 13 - 18, 2014
GA, Atlanta, USA
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XSEDE '14 Paper Acceptance Rate 80 of 120 submissions, 67%;
Overall Acceptance Rate 129 of 190 submissions, 68%
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