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CARAT-GxG: CUDA-Accelerated Regression Analysis Toolkit for Large-Scale Gene-Gene Interaction with GPU Computing System.

Lee S, Kwon MS, Park T - Cancer Inform (2014)

Bottom Line: Compared to other methods, CARAT-GxG achieved almost 700-fold execution speed and delivered highly reliable results through our GPU-specific optimization techniques.In addition, it was possible to achieve almost-linear speed acceleration with the application of a GPU computing system, which is implemented by the TORQUE Resource Manager.We expect that CARAT-GxG will enable large-scale regression analysis with GGI for GWAS data.

View Article: PubMed Central - PubMed

Affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea.

ABSTRACT
In genome-wide association studies (GWAS), regression analysis has been most commonly used to establish an association between a phenotype and genetic variants, such as single nucleotide polymorphism (SNP). However, most applications of regression analysis have been restricted to the investigation of single marker because of the large computational burden. Thus, there have been limited applications of regression analysis to multiple SNPs, including gene-gene interaction (GGI) in large-scale GWAS data. In order to overcome this limitation, we propose CARAT-GxG, a GPU computing system-oriented toolkit, for performing regression analysis with GGI using CUDA (compute unified device architecture). Compared to other methods, CARAT-GxG achieved almost 700-fold execution speed and delivered highly reliable results through our GPU-specific optimization techniques. In addition, it was possible to achieve almost-linear speed acceleration with the application of a GPU computing system, which is implemented by the TORQUE Resource Manager. We expect that CARAT-GxG will enable large-scale regression analysis with GGI for GWAS data.

No MeSH data available.


Related in: MedlinePlus

Results of CARAT-GxG performance assessment. (A) The dotted line indicates the theoretical acceleration folds by adding a graphics card. The solid line indicates measured acceleration folds in two against one (green) and three against one (blue) graphics cards. (B) Execution time between CARAT-GxG and CPU implementations in a single SNP test. (C) Execution time of CARAT-GxG according to the number of threads and blocks with the dataset including 1,000 samples with 500 SNPs.
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f2-cin-suppl.7-2014-027: Results of CARAT-GxG performance assessment. (A) The dotted line indicates the theoretical acceleration folds by adding a graphics card. The solid line indicates measured acceleration folds in two against one (green) and three against one (blue) graphics cards. (B) Execution time between CARAT-GxG and CPU implementations in a single SNP test. (C) Execution time of CARAT-GxG according to the number of threads and blocks with the dataset including 1,000 samples with 500 SNPs.

Mentions: In order to accomplish optimal performance using the GPU, many aspects must be considered. Since the main computational burden of regression analysis occurs during matrix calculation, an optimized access of memory, which highly varies by the model of graphics card, is essential to minimize race conditions. CARAT-GxG automatically selects the most appropriate parameter of GPU execution. In order to determine this parameter, a very naïve but fast approach is applied; it is achieved via a sequential test of equally spaced candidates of optimal parameters. As shown in Figure 2C, a concave trend of execution time along with the parameter value justifies this approach.


CARAT-GxG: CUDA-Accelerated Regression Analysis Toolkit for Large-Scale Gene-Gene Interaction with GPU Computing System.

Lee S, Kwon MS, Park T - Cancer Inform (2014)

Results of CARAT-GxG performance assessment. (A) The dotted line indicates the theoretical acceleration folds by adding a graphics card. The solid line indicates measured acceleration folds in two against one (green) and three against one (blue) graphics cards. (B) Execution time between CARAT-GxG and CPU implementations in a single SNP test. (C) Execution time of CARAT-GxG according to the number of threads and blocks with the dataset including 1,000 samples with 500 SNPs.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4263399&req=5

f2-cin-suppl.7-2014-027: Results of CARAT-GxG performance assessment. (A) The dotted line indicates the theoretical acceleration folds by adding a graphics card. The solid line indicates measured acceleration folds in two against one (green) and three against one (blue) graphics cards. (B) Execution time between CARAT-GxG and CPU implementations in a single SNP test. (C) Execution time of CARAT-GxG according to the number of threads and blocks with the dataset including 1,000 samples with 500 SNPs.
Mentions: In order to accomplish optimal performance using the GPU, many aspects must be considered. Since the main computational burden of regression analysis occurs during matrix calculation, an optimized access of memory, which highly varies by the model of graphics card, is essential to minimize race conditions. CARAT-GxG automatically selects the most appropriate parameter of GPU execution. In order to determine this parameter, a very naïve but fast approach is applied; it is achieved via a sequential test of equally spaced candidates of optimal parameters. As shown in Figure 2C, a concave trend of execution time along with the parameter value justifies this approach.

Bottom Line: Compared to other methods, CARAT-GxG achieved almost 700-fold execution speed and delivered highly reliable results through our GPU-specific optimization techniques.In addition, it was possible to achieve almost-linear speed acceleration with the application of a GPU computing system, which is implemented by the TORQUE Resource Manager.We expect that CARAT-GxG will enable large-scale regression analysis with GGI for GWAS data.

View Article: PubMed Central - PubMed

Affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea.

ABSTRACT
In genome-wide association studies (GWAS), regression analysis has been most commonly used to establish an association between a phenotype and genetic variants, such as single nucleotide polymorphism (SNP). However, most applications of regression analysis have been restricted to the investigation of single marker because of the large computational burden. Thus, there have been limited applications of regression analysis to multiple SNPs, including gene-gene interaction (GGI) in large-scale GWAS data. In order to overcome this limitation, we propose CARAT-GxG, a GPU computing system-oriented toolkit, for performing regression analysis with GGI using CUDA (compute unified device architecture). Compared to other methods, CARAT-GxG achieved almost 700-fold execution speed and delivered highly reliable results through our GPU-specific optimization techniques. In addition, it was possible to achieve almost-linear speed acceleration with the application of a GPU computing system, which is implemented by the TORQUE Resource Manager. We expect that CARAT-GxG will enable large-scale regression analysis with GGI for GWAS data.

No MeSH data available.


Related in: MedlinePlus