Accelerating binary biclustering on platforms with CUDA-enabled GPUs

González-Domínguez, Jorge; Expósito, Roberto R.

doi:10.1016/j.ins.2018.05.025

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…The relationship between miRNA and the target gene is a bipartite graph structure; thus, the miRNA–target regulatory group can be found by analyzing the bipartite graph. CUBiBit ( Gonzalez-Dominguez and Exposito, 2019 ) was proposed based on Bimax( Prelic et al, 2006 ) and BiBit ( Rodriguez-Baena et al, 2011 ), which shortened the computing time and provided an optimized method for finding modules in larger data. We added the miRNA-target data based on the homology expansion predictions from A. thaliana and M. truncatula into the collected soybean miRNA-target data.…”

Section: Methodsmentioning

confidence: 99%

“…The application of biclustering algorithms and miRNA–target regulation module (MTRM) mining is feasible and important for analyzing miRNA regulation mechanisms. Compared with traditional clustering methods, such as Bimax ( Prelic et al, 2006 ) and BiBit ( Rodriguez-Baena et al, 2011 ), CUBiBit ( Gonzalez-Dominguez and Exposito, 2019 ) shortened the computing time and provided an optimized method for finding modules in larger data. However, the result obtained by CUBiBit was mostly a fully-connected bipartite graph, and the relationship between miRNA and the target gene is complex and interactive.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Level Iterative Bi-Clustering Method for Discovering miRNA Co-regulation Network of Abiotic Stress Tolerance in Soybeans

Chang

Zhang

et al. 2022

Front. Plant Sci.

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Although growing evidence shows that microRNA (miRNA) regulates plant growth and development, miRNA regulatory networks in plants are not well understood. Current experimental studies cannot characterize miRNA regulatory networks on a large scale. This information gap provides an excellent opportunity to employ computational methods for global analysis and generate valuable models and hypotheses. To address this opportunity, we collected miRNA–target interactions (MTIs) and used MTIs from Arabidopsis thaliana and Medicago truncatula to predict homologous MTIs in soybeans, resulting in 80,235 soybean MTIs in total. A multi-level iterative bi-clustering method was developed to identify 483 soybean miRNA–target regulatory modules (MTRMs). Furthermore, we collected soybean miRNA expression data and corresponding gene expression data in response to abiotic stresses. By clustering these data, 37 MTRMs related to abiotic stresses were identified, including stress-specific MTRMs and shared MTRMs. These MTRMs have gene ontology (GO) enrichment in resistance response, iron transport, positive growth regulation, etc. Our study predicts soybean MTRMs and miRNA-GO networks under different stresses, and provides miRNA targeting hypotheses for experimental analyses. The method can be applied to other biological processes and other plants to elucidate miRNA co-regulation mechanisms.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Level Iterative Bi-Clustering Method for Discovering miRNA Co-regulation Network of Abiotic Stress Tolerance in Soybeans

Chang

Zhang

et al. 2022

Front. Plant Sci.

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“…Additionally, there are other parallel tools for binary biclustering but focused on cloud systems [17] (not publicly available) or GPUs [7,16]. 3 Background: the ParBiBit approach As previously mentioned, ScalaParBiBit is an extensive redesign and rewrite of ParBiBit that provides higher performance and scalability in multicore clusters thanks to a modification of its parallel structure and implementation.…”

Section: Related Workmentioning

confidence: 99%

ScalaParBiBit: scaling the binary biclustering in distributed-memory systems

2021

Self Cite

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Biclustering is a data mining technique that allows us to find groups of rows and columns that are highly correlated in a 2D dataset. Although there exist several software applications to perform biclustering, most of them suffer from a high computational complexity which prevents their use in large datasets. In this work we present ScalaParBiBit, a parallel tool to find biclusters on binary data, quite common in many research fields such as text mining, marketing or bioinformatics. ScalaParBiBit takes advantage of the special characteristics of these binary datasets, as well as of an efficient parallel implementation and algorithm, to accelerate the biclustering procedure in distributedmemory systems. The experimental evaluation proves that our tool is significantly faster and more scalable that the state-of-the-art tool ParBiBit in a cluster with 32 nodes and 768 cores. Our tool together with its reference manual are freely available at https://github. com/fraguela/ScalaParBiBit.

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“…However, the development of GPU (Graphics Processing Units) and the emergence of CUDA (Compute Unified Device Architecture) parallel computing have greatly increased the computing speed of big data. The single instruction multi-data (SIMD) parallel computer architecture is used to command many thread-level parallel tasks [9], [10] which improves many algorithms facing the problem of insufficient real-time performance.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Clutter Simulation Based on GPU

Hao

Chen

et al. 2020

IEEE Access

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Weibull clutter is used as an example in this paper. Based on the serial parallel analysis of Zero-memory non-linear transformation's Weibull distributed clutter algorithm, fine-grained optimization is performed. The fine-grained part uses the cuBLAS library to optimize the performance of convolution calculations. Compared with CUDA shared memory convolution method and GPU parallel matrix multiplication convolution method, its computational performance can be significantly improved under a large amount of data. Simulation results show that the Zero-memory non-linear transformation's Weibull distributed clutter simulation method is optimized and accelerated. The real-time performance of clutter data is significantly improved and its acceleration effect will be better as the amount of clutter data increases. It turns out that through fine-grained optimization, the performance of convolution calculations with large amounts of data is improved.

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Accelerating binary biclustering on platforms with CUDA-enabled GPUs

Cited by 13 publications

References 21 publications

A Multi-Level Iterative Bi-Clustering Method for Discovering miRNA Co-regulation Network of Abiotic Stress Tolerance in Soybeans

A Multi-Level Iterative Bi-Clustering Method for Discovering miRNA Co-regulation Network of Abiotic Stress Tolerance in Soybeans

ScalaParBiBit: scaling the binary biclustering in distributed-memory systems

Optimization of Clutter Simulation Based on GPU

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