Parallel Rank Coherence in Networks for Inferring Disease Phenotype and Gene Set Associations

The RCNet (Rank Coherence in Networks) algorithm has been used to find out the associations between the gene sets and disease phenotypes. However, it suffers from high computational cost when the size of dataset is very large. In this paper, we design three mechanisms to solve the RCNet algorithm on heterogeneous CPU-GPU system based on CUDA and OpenMP programming model. The pipeline mechanism is much suitable for the collaborative computing on CPU and dual-GPUs, which can achieve more than 33 times performance gains. The work plays an important role in reconstructing the disease phoneme-genome association efficiently.

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