Biological complex network motifs discovery, based on theoretical research of complex networks, is an important method for studying biological networks, which provides a new perspective on life phenomena and life mechanisms. However, it computes inefficiently when dealing with large networks or mining big motifs. On the basis of existing serial ESU (Enumerate SUbgraph) algorithm of network motifs discovery, a parallelized ESU algorithm based on Message Passing Interface (MPI) was proposed. The node values in ESU algorithm were optimized to solve the problem of node value dependency, the number of subgraphs was counted by using subgraph discovery strategy of ESU algorithm, and a dynamic programming method was used to determine optimal node allocation strategy to satisfy load balancing. The experiments on simulated and biological networks show that the parallelized ESU algorithm addresses node value dependency and realizes a load balancing strategy, which saves more than 90% running time compared to serial algorithm. Furthermore, the parallel algorithm is suitable for different types and different scales of networks, and effectively improves computation efficiency of network motifs discovery.

Markov Clustering Algorithm (MCL) is an effective method to find modules in large-scale biological networks. It can mine modules that have significant influence on network structure and function. The algorithm involves large-scale matrix calculations, so its complexity can reach cubic orders. For the problem of high complexity, a parallel algorithm of Markov clustering based on Message Passing Interface (MPI) was proposed to improve computational performance of algorithm. Firstly, a biological network was transformed into an adjacency matrix. Secondly, according to the characteristics of the algorithm, the matrix size was judged and a new matrix was regenerated to handle the calculation of non-square multiple matrix. Thirdly, the algorithm was calculated in parallel by means of block allocation, which could effectively implement the operation of matrix of any size. Finally, the loop was parallelized until the matrix was converged to obtain network clustering results. The experimental results on simulated network and real biological network datasets show that compared with Full-block Collective Communication (FCC) parallel method, the average parallel efficiency is improved by more than 10 percentage points, so the optimization algorithm can be applied in different types of large-scale biological networks.

Due to a large number of computational grids and slow convergence existed in the numerical simulation of Navier-Stokes (N-S) equation, Triangular mesh Spectral Finite Element Method based on area coordinate (TSFEM) was proposed. And further, TSFEM was paralleled with OpenMP. Spectral method was combined with finite element method, and the exponential function with infinite smoothness was selected as the basis function to replace the polynomial function in the traditional finite element method, which can efficiently reduce the amount of computational grids as well as improve the convergence and accuracy of the proposed algorithm. Because area coordinates can facilitate the calculation of triangular units, which were selected as the computing units to enhance the applicability of the algorithm. The lid-driven cavity flow was used to verify the TSFEM. The experimental results show that, compared with the traditional Finite Element Method (FEM), the TSFEM greatly improves the convergence rate and the calculation efficiency.

Nowadays, most of researches about social network use data from foreign social network platforms. However the largest social network platform Sina Weibo in China has no data interfaces for investors. A Sina Weibo data crawler combined with parallelization technology was put forward. It got fans information and Weibo data content of different weibo users in real-time. It also supported key words matching and parallelization. The serial data crawler and its parallel version were compared, and an experiment about flu was conducted on some Weibo data. The results indicate that, with parallelization, this tool has liner speedup and all the fetching data are with timeliness and accuracy.

In order to reduce the time complexity of biological networks alignment, an implementation for large scale biological networks alignment based on Scalable Protein Interaction Network Alignment (SPINAL) in Message Passing Interface (MPI) program was proposed. Based on MPI, the SPINAL algorithm combined with parallelization method was applied into this approach. Instead of serial algorithm, parallel sorting algorithm was used in multi-core environment. Load balancing strategy was chosen to assign tasks reasonably. In the processing of large scale biological networks alignment, the experiment shows that, compared with the algorithm without parallelization and load balancing strategy, this proposed algorithm can reduce the runtime and improve computation efficiency effectively.

Biomolecular networks alignment is an important field, and it is an effective way to study biomolecular phenomenon. Adaptive Hungary Greedy Algorithm (AHGA) is one of the valid biomolecular networks alignment algorithms. Commonly, biomolecular networks have large scale and biological background, so the data of biomolecular networks are special. In order to get the alignment results of biomolecular networks in acceptable time, considering the biological significance when aligning them, two methods including MPI (Message Passing Interface) and CUDA (Compute Unified Device Architecture) were used to parallelize the adaptive hybrid algorithm. The methods were analyzed and compared to find the suitable one for biomolecular networks alignment.