Two popular traditional join algorithms and their parallel versions are introduced. When designing join algorithms in serial computing environment, decomposing inner relation is considered as the right direction to sa...Two popular traditional join algorithms and their parallel versions are introduced. When designing join algorithms in serial computing environment, decomposing inner relation is considered as the right direction to save disk I/Os. However, two different decomposition algorithms are compared, such as inner vs. outer decomposition first algorithms for tuple-based and block-based nested loop joins, showing that the proposed approach is 20% better than general approach. Also lemmas are proved, when we have to use the outer decomposition first parallel join algorithms.展开更多
A new parallel architecture for quantified boolean formula(QBF)solving was proposed,and the prediction model based on machine learning technology was proposed for how sharing knowledge affects the solving performance ...A new parallel architecture for quantified boolean formula(QBF)solving was proposed,and the prediction model based on machine learning technology was proposed for how sharing knowledge affects the solving performance in QBF parallel solving system,and the experimental evaluation scheme was also designed.It shows that the characterization factor of clause and cube influence the solving performance markedly in our experiment.At the same time,the heuristic machine learning algorithm was applied,support vector machine was chosen to predict the performance of QBF parallel solving system based on clause sharing and cube sharing.The relative error of accuracy for prediction can be controlled in a reasonable range of 20%30%.The results show the important and complex role that knowledge sharing plays in any modern parallel solver.It shows that the parallel solver with machine learning reduces the quantity of knowledge sharing about 30%and saving computational resource but does not reduce the performance of solving system.展开更多
基金supported by the National Research Foundation (NRF) of Korea through contract N-14-NMIR06
文摘Two popular traditional join algorithms and their parallel versions are introduced. When designing join algorithms in serial computing environment, decomposing inner relation is considered as the right direction to save disk I/Os. However, two different decomposition algorithms are compared, such as inner vs. outer decomposition first algorithms for tuple-based and block-based nested loop joins, showing that the proposed approach is 20% better than general approach. Also lemmas are proved, when we have to use the outer decomposition first parallel join algorithms.
基金Project(61171141)supported by the National Natural Science Foundation of China
文摘A new parallel architecture for quantified boolean formula(QBF)solving was proposed,and the prediction model based on machine learning technology was proposed for how sharing knowledge affects the solving performance in QBF parallel solving system,and the experimental evaluation scheme was also designed.It shows that the characterization factor of clause and cube influence the solving performance markedly in our experiment.At the same time,the heuristic machine learning algorithm was applied,support vector machine was chosen to predict the performance of QBF parallel solving system based on clause sharing and cube sharing.The relative error of accuracy for prediction can be controlled in a reasonable range of 20%30%.The results show the important and complex role that knowledge sharing plays in any modern parallel solver.It shows that the parallel solver with machine learning reduces the quantity of knowledge sharing about 30%and saving computational resource but does not reduce the performance of solving system.
