Zhang Jilin

Webs ranking model based on pagerank algorithm

This paper analyzes the development of the PageRank algorithm both domestic and international, expounds the practical application and development of the algorithm. On this basis, the dissertation analyzes the thinking of the traditional PageRank algorithm, sums up the advantages and disadvantages, and introduces several important PageRank improved algorithms and their thinking. The paper provides a time-feedback PageRank algorithm improvement for the shortcoming of the traditional PageRank algorithm focus on the old websites. Adding a feedback factor in the traditional PageRank algorithm can accelerate the old and useless Webs sinking, the new Webs rising, and taking into account the old and important webs not sink. Implementing the PageRank value is plus or minus with the time of webs. On the basis of theoretical research, implements the rank model based on the PageRank algorithm, the model contains the traditional web ranking system model and improved web ranking system model. Experiment on this model, contrasting the experimental results. Experimental results show that the improved algorithm can improve the satisfaction of the users search results.

Parallel matrix algorithm autotuner on multi-core architecture

This paper develops a performance autotuner of the parallel sparse matrix multiplication algorithm. This autotuner can utilize the cache characters of multicore computers. It can optimize the value of performance parameters in multicore and achieve the better performance of parallel matrix multiplication algorithm. This paper proposes a parallel matrix multiplication algorithm by Using the CSR compression and cache blocking technology. The cache blocking parameters are optimized to get the better performance. From the matrix experiments, we can consult that the autotuner system can achieve the sparse matrix multiplication algorithm better performance on the multicore hardware.