Fast Parallel Markov Clustering in Bioinformatics Using Massively Parallel Computing on GPU with CUDA and ELLPACK-R Sparse Format

Abstract

Markov clustering (MCL) is becoming a key algorithm within bioinformatics for determining clusters in networks. However,with increasing vast amount of data on biological networks, performance and scalability issues are becoming a critical limiting factor in applications. Meanwhile, GPU computing, which uses CUDA tool for implementing a massively parallel computing environment in the GPU card, is becoming a very powerful, efficient, and low-cost option to achieve substantial performance gains over CPU approaches. The use of on-chip memory on the GPU is efficiently lowering the latency time, thus, circumventing a major issue in other parallel computing environments, such as MPI. We introduce a very fast Markov clustering algorithm using CUDA (CUDA-MCL) to perform parallel sparse matrix-matrix computations and parallel sparse Markov matrix normalizations, which are at the heart of MCL. We utilized ELLPACK-R sparse format to allow the effective and fine-grain massively parallel processing to cope with the sparse nature of interaction networks data sets in bioinformatics applications. As the results show, CUDA-MCL is significantly faster than the original MCL running on CPU. Thus, large-scale parallel computation on off-the-shelf desktop-machines, that were previously only possible on supercomputing architectures, can significantly change the way bioinformaticians and biologists deal with their data.

Parallelization strategies for continuum-generalized method of moments on the multi-thread systems

“…In most real life problems, this regularization parameter is unknown and needs to be estimated from a sample of size T. The regularization parameter can be obtained by minimizing the trace of MSE matrix of 5 2 E , as defined as follow [2]:…”

“…Accordingly, Marine Carrasco and Jean-Pierre Florens [1] developed a method which combines the attractive features of GMM with the efficiency of MLE in one framework that was called Continuum-Generalized Method of Moments (C-GMM) to rely on a continuum of moment conditions in a GMM procedure. To improve the objectivity of the C-GMM method, it is required optimization stages to the new parameter, known as regularization parameter [2]. It is optimized through two-stage estimation in which the optimal regularization parameter is obtained by determining the regularization parameter that makes the Mean-Squared of Error (MSE) of the C-GMM estimator based on sampled simulation become minimum [3].…”

A fast implementation of MLR-MCL algorithm on multi-core processors

“…As the number and the size of new graph datasets increase dramatically, there are increasing demands for a high-performance, parallel implementation of MCL and its variants. Bustamam et al [25], [26] proposed a MPI and a CUDA implementation of MCL. A distributed R-MCL algorithm was developed using MPI and MapReduce for large-scale datasets [27].…”

Systems Biology, Bioinformatics, and Biomarkers in Neuropsychiatry

“…Heuristics can be used to reduce the overhead. For example, the Markov clustering algorithm (MCL; Bustamam et al, 2012) is used in bioinformatics to cluster protein–protein interaction networks (PPI) and protein similarity networks (Satuluri et al, 2010). It is a graph clustering algorithm that relies on probabilistic studies to analyze the network components and the flow within network clusters.…”