Parallelization of Machine Learning Methods by Using CUDA

Image analysis, data mining, protein folding and gene sequencing are some examples of high-intensive bioinformatics applications that require high computing resources. In this paper we present a problem of computationally intensive methodology for microarray data analysis, whose performance needs to be improved by using high performance computing techniques. Parallelization is a key computing technique for reducing the time required for the analyses and the classification procedure. GPU provides great level of parallelization based on throughput of vast amount of data needed for machine learning problems. Therefore, we propose a model for machine learning problems parallelization based on GPU programming that will increase the speedup of several stages of the machine learning process.

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