Forged and tampered data frames should be identified and filtered out to ensure network security and efficiency. However， the existing schemes usually fail to work when verification devices are attacked or maliciously controlled in the Software Defined Network （SDN）. To solve the above problem， a blockchain-based data frame security verification mechanism was proposed. Firstly， a Proof of Frame Forwarding （PoFF） consensus algorithm was designed and used to build a lightweight blockchain system. Then， an efficient data frame security verifying scheme for SDN data frame was proposed on the basis of this blockchain system. Finally， a flexible semi-random verifying scheme was presented to balance the verification efficiency and the resource cost. Simulation results show that compared with the hash chain based verifying scheme， the proposed scheme decreases the missed detection rate significantly when an equal proportion of switches are maliciously controlled. Specifically， when the proportion is 40%， the decrease effect is very obvious， the missed detection rate can still be kept no more than 32% in the basic verification mode， and can be further reduced to 7% with the assistance of the semi-random verifying scheme. Both are much lower than the missed detection rate of 72% in the hash chain based verifying scheme， and the resource overhead and communication cost introduced by the proposed mechanism are within a reasonable range. Additionally， the proposed scheme can still maintain good verification performance and efficiency even when the SDN controller is completely unable to work.

The formation of carotid artery plaque is closely related to complex hemodynamic factors. The accurate simulation of complex flow conditions is of great significance for the clinical diagnosis of carotid artery plaque. In order to simulate the pulsating flow accurately， Large Eddy Simulation （LES） model was combined with Lattice Boltzmann Method （LBM） to construct a LBM-LES carotid artery simulation algorithm， and a real geometric model of carotid artery stenosis was established through medical image reconstruction software， thus the high-resolution numerical simulation of carotid artery stenosis flows was conducted. By calculating blood flow velocity and Wall Shear Stress （WSS）， some meaningful flow results were obtained， proving the effectiveness of LBM-LES in the study of blood flow in the carotid artery narrow posterior. Based on the OpenMP programming environment， the parallel computation of the grid of ten million magnitude was carried out on the fully interconnected fat node of high-performance cluster machine. The results show that the LBM-LES carotid artery simulation algorithm has good parallel performance.

Graphlet Degree Vector （GDV） is an important method for studying biological networks， and can reveal the correlation between nodes in biological networks and their local network structures. However， with the increasing number of automorphic orbits that need to be researched and the expanding biological network scale， the time complexity of the GDV method will increase exponentially. To resolve this problem， based on the existing serial GDV method， the parallelization of GDV method based on Message Passing Interface （MPI） was realized. Besides， the GDV method was improved and the parallel optimization of the optimized method was realized. The calculation process was optimized to solve the problem of double counting when searching for automorphic orbits of different nodes by the improved method， at the same time， the tasks were allocated reasonably combining with the load balancing strategy. Experimental results of simulated network data and real biological network data indicate that parallel GDV method and the improved parallel GDV method both obtain better parallel performance， they can be widely applied to different types of networks with different scales， and have good scalability. As a result， they can effectively maintain the high efficiency of searching for automorphic orbits in the network.