图是一种非常重要的数据结构形式,被广泛用于社交网络、交通网络和搜索引擎等领域。随着图数据规模爆发式增长,存储容量受限,分布式图计算成为处理大规模图数据的焦点。宽度优先搜索(breadth first search,BFS)算法是图遍历和许多图分...图是一种非常重要的数据结构形式,被广泛用于社交网络、交通网络和搜索引擎等领域。随着图数据规模爆发式增长,存储容量受限,分布式图计算成为处理大规模图数据的焦点。宽度优先搜索(breadth first search,BFS)算法是图遍历和许多图分析算法的基础,而在分布式图计算过程中存在严重的通信开销。针对上述问题,本文提出了一种综合的数据压缩编码优化方案,结合位图和变长压缩数组,通过更高的压缩率来降低数据通信开销;此外,还提出了一种点对点异步环形通信策略,进一步降低分布式图计算中计算-通信的同步开销。通过这些优化手段,本文在8节点的分布式集群上对优化后BFS算法的性能进行了系统评估,结果表明,当图数据规模为28时,优化后的BFS算法平均性能为46.79亿条边每秒遍历(giga-traversed edges per second,GTEPS),性能比优化前提升了接近7.82%。展开更多
The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to elimin...The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.展开更多
文摘图是一种非常重要的数据结构形式,被广泛用于社交网络、交通网络和搜索引擎等领域。随着图数据规模爆发式增长,存储容量受限,分布式图计算成为处理大规模图数据的焦点。宽度优先搜索(breadth first search,BFS)算法是图遍历和许多图分析算法的基础,而在分布式图计算过程中存在严重的通信开销。针对上述问题,本文提出了一种综合的数据压缩编码优化方案,结合位图和变长压缩数组,通过更高的压缩率来降低数据通信开销;此外,还提出了一种点对点异步环形通信策略,进一步降低分布式图计算中计算-通信的同步开销。通过这些优化手段,本文在8节点的分布式集群上对优化后BFS算法的性能进行了系统评估,结果表明,当图数据规模为28时,优化后的BFS算法平均性能为46.79亿条边每秒遍历(giga-traversed edges per second,GTEPS),性能比优化前提升了接近7.82%。
基金supported by the National Natural Science Foundation of China(71071077)the Ministry of Education Key Project of National Educational Science Planning(DFA090215)+1 种基金China Postdoctoral Science Foundation(20100481137)Funding of Jiangsu Innovation Program for Graduate Education(CXZZ11-0226)
文摘The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.
