GPU-accelerated exhaustive search for third-order epistatic interactions in case–control studies

González-Domínguez, Jorge; Schmidt, Bertil

doi:10.1016/j.jocs.2015.04.001

Cited by 30 publications

(19 citation statements)

References 29 publications

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“…There is a large variety of methods to identify the interesting SNPs, from following a statistical approach (Fang et al, 2012; Tang et al, 2009; Zhang and Liu, 2007), to applying clustering strategies (Leem et al, 2014; Xie et al, 2012), or using different machine learning techniques (Wan et al, 2009, 2010). Nevertheless, initially discarding some SNPs can lead to worse accuracy, as proven by González-Domínguez and Schmidt (2015) for third-order epistatic interactions.…”

Section: Related Workmentioning

confidence: 99%

“…In a previous work, we proposed GPU3SNP (González-Domínguez and Schmidt, 2015), a tool that is able to exploit several GPUs within the same node to exhaustively search third-order epistatic interactions. Although the results show that GPU3SNP achieves high performance and significantly reduces the execution times, the analysis of large GWAS data sets would still require a significant amount of time.…”

Section: Introductionmentioning

confidence: 99%

“…The main contributions of this work are the following: A hybrid implementation that combines Message Passing Interface (MPI) processes and threads to efficiently exploit the cores available on each node of a multicore CPU cluster. A hybrid MPI/CUDA approach to exploit the resources of a multi-GPU cluster. In this version, MPI is used for inter-node communications, and the CUDA kernels developed in the work of González-Domínguez and Schmidt (2015) are used to take benefit from the massive parallelism inside the GPUs of each node. A workload distribution that avoids communications and synchronizations and balances the computation among the available nodes of the cluster. …”

Section: Introductionmentioning

confidence: 99%

“…A hybrid MPI/CUDA approach to exploit the resources of a multi-GPU cluster. In this version, MPI is used for inter-node communications, and the CUDA kernels developed in the work of González-Domínguez and Schmidt (2015) are used to take benefit from the massive parallelism inside the GPUs of each node.…”

Section: Introductionmentioning

confidence: 99%

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Fast search of third-order epistatic interactions on CPU and GPU clusters

Ponte-Fernández

González-Domínguez

Martín

2019

The International Journal of High Performance Computing Applica

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Genome-Wide Association Studies (GWASs), analyses that try to find a link between a given phenotype (such as a disease) and genetic markers, have been growing in popularity in the recent years. Relations between phenotypes and genotypes are not easy to identify, as most of the phenotypes are a product of the interaction between multiple genes, a phenomenon known as epistasis. Many authors have resorted to different approaches and hardware architectures in order to mitigate the exponential time complexity of the problem. However, these studies make some compromises in order to keep a reasonable execution time, such as limiting the number of genetic markers involved in the interaction, or discarding some of these markers in an initial filtering stage. This work presents MPI3SNP, a tool that implements a three-way exhaustive search for cluster architectures with the aim of mitigating the exponential growth of the run-time. Modern cluster solutions usually incorporate GPUs. Thus, MPI3SNP includes implementations for both multi-CPU and multi-GPU clusters. To contextualize the performance achieved, MPI3SNP is able to analyze an input of 6300 genetic markers and 3200 samples in less than 6 min using 768 CPU cores or 4 min using 8 NVIDIA K80 GPUs. The source code is available at https://github.com/chponte/mpi3snp .

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast search of third-order epistatic interactions on CPU and GPU clusters

Ponte-Fernández

González-Domínguez

Martín

2019

The International Journal of High Performance Computing Applica

Self Cite

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“…One noteworthy implementation using a measure similar to the Mutual Information used in this work, is GPU3SNP [GS15]. The authors use a configuration of four NVIDIA GTX Titan graphics accelerators to exhaustively evaluate all SNP 3-tuples using Mutual Information in 22 hours for 50 000 SNPs and 1 000 individuals.…”

Section: Third Order Interactionmentioning

confidence: 99%

A Hybrid-parallel Architecture for Applications in Bioinformatics

Kässens¹

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Since the advent of Next Generation Sequencing (NGS) technology, the amount of data from whole genome sequencing has been rising fast. In turn, the availability of these resources led to the tapping of whole new research fields in molecular and cellular biology, producing even more data. On the other hand, the available computational power is only increasing linearly. In recent years though, special-purpose high-performance devices started to become prevalent in today’s scientific data centers, namely graphics processing units (GPUs) and, to a lesser extent, field-programmable gate arrays (FPGAs). Driven by the need for performance, developers started porting regular applications to GPU frameworks and FPGA configurations to exploit the special operations only these devices may perform in a timely manner. However, applications using both accelerator technologies are still rare. Major challenges in joint GPU/FPGA application development include the required deep knowledge of associated programming paradigms and the efficient communication both types of devices. In this work, two algorithms from bioinformatics are implemented on a custom hybrid-parallel hardware architecture and a highly concurrent software platform. It is shown that such a solution is not only possible to develop but also its ability to outperform implementations on similar- sized GPU or FPGA clusters in terms of both performance and energy consumption. Both algorithms analyze case/control data from genome- wide association studies to find interactions between two or three genes with different methods. Especially in the latter case, the newly available calculation power and method enables analyses of large data sets for the first time without occupying whole data centers for weeks. The success of the hybrid-parallel architecture proposal led to the development of a high- end array of FPGA/GPU accelerator pairs to provide even better runtimes and more possibilities.

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HPC Tools to Deal with Microarray Data

González-Domínguez

Expósito

2019

Methods in Molecular Biology

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GPU-accelerated exhaustive search for third-order epistatic interactions in case–control studies

Cited by 30 publications

References 29 publications

Fast search of third-order epistatic interactions on CPU and GPU clusters

Fast search of third-order epistatic interactions on CPU and GPU clusters

A Hybrid-parallel Architecture for Applications in Bioinformatics

HPC Tools to Deal with Microarray Data

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