面向非规则大数据分析应用的多核帮助线程预取方法

大数据分析应用往往采用基于大型稀疏图的遍历算法，其主要特点是非规则数据密集访存。以频繁使用的具有大型稀疏图遍历特征的介度中心算法为例，提出一种基于帮助线程的多参数预取控制模型和参数优化方法，从而达到提高非规则数据密集程序性能的目的。在商用多核平台Q6600和I7上运用该方法后，介度中心算法在不同规模输入下平均性能加速比分别为1.20和1.11。实验结果表明，帮助线程预取能够有效提升该类非规则应用程序的性能。

Multi-core helper thread prefetching for irregular data intensive applications

Big data analysis applications often use sparse graph traversal algorithm which characterized by irregular data intensive memory access. For improving performance of memory access in sparse graph traversal algorithm, helper thread prefetching could convert discontinuous locality into continuous-instant spatial-temporal locality effectively by using the shared last level cache of chip multi-processor platforms. Betweenness centrality algorithm was used as a case study, the multi-parameter prefetching model of helper thread and optimized instances were presented and evaluated on commercial CMP platforms Q6600 and I7, the average speedup of betweenness centrality algorithm at different input scale is 1.20 and 1.11 respectively. The experiment results show that helper thread prefetching can improve the performance of irregular applications effectively.