NVIDIA Common Unified Device Architecture (CUDA) is an example of a graphics card architecture for parallel general purpose computing. Several vendors of graphics cards offer architectures and programming tools that enable GPU-based general purpose computing using high level programming language extensions. We will be using the term "device memory" to refer to the built-in memory of the graphics card in the rest of this paper.
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developed a GPU-based software package that greatly speeds up gene-gene interaction analysis of quantitative traits.Ī typical graphics card has several processors as well as its own dedicated memory. The power of GPUs has been used to implement faster software solutions for biological problems. A GPU is a processing unit that was traditionally used for accelerating graphical operations. An emerging economic scientific computing paradigm is to use Graphics Processing Units (GPUs) that are present in graphic cards of most desktop computers or workstations for general purpose computing. The costs of building a computing cluster may run in hundreds of thousands of dollars, making it cost prohibitive. Parallel computing until recently meant using a computing cluster having multiple nodes with multi-core CPUs. Most of the current Central Processor Units (CPUs) have multiple cores. However, gene-gene interaction analysis is parallelizable in nature. The running time quickly becomes an issue due to the large number of pairs. For example, in the case of a GWAS with 500,000 SNPs the number of SNP pairs to be studied amounts to ~125 billion. The drawback of such analysis is that the number of tests will be extremely large. Gene-gene interaction is often studied using a regression framework in which a pair of SNPs and their interaction terms are included as predictors. It is becoming increasingly evident that gene-gene interactions play an important role in the etiology of complex diseases and traits, and likely explain some fraction of the "missing heritability". Although this approach has led to the discovery of disease susceptibility genes for many diseases, the identified markers often only explain a small fraction of the phenotypic variation, suggesting a large number of disease variants are yet to be discovered. Most GWAS focus on single marker-based analysis in which each marker is analyzed individually, ignoring the dependence or interactions between markers. Recent years have seen an explosion of results generated from genome-wide association studies (GWAS). The advent of high-throughput genotyping technologies has made it possible to study human genetic variation on a genome-wide scale.
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