NDLI: Accelerating Sparse Canonical Correlation Analysis for Large Brain Imaging Genetics Data

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Accelerating Sparse Canonical Correlation Analysis for Large Brain Imaging Genetics Data

Content Provider	ACM Digital Library
Author	Wernert, Eric Saykin, Andrew J. Yan, Jingwen Du, Lei Zhang, Hui Shen, Li
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.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISBN	9781450328937
DOI	10.1145/2616498.2616515
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-07-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Brain imaging genetics Parallel computing R Sparse canonical correlation analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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