天体物理成团研究中的非规则访存优化

发布时间：2018-01-01 05:33

  本文关键词：天体物理成团研究中的非规则访存优化　出处：《计算机科学与探索》2017年01期 　论文类型：期刊论文

  更多相关文章： 天体物理成团 非规则访存优化 数据预排序 并行计算

【摘要】：HGGF(halo-based galaxy group finder)算法实现了基于暗物质晕的星系找群,在研究宇宙大尺度结构及宇宙的演化等领域中占有至关重要的地位。但由于数据规模的增长,急需对HGGF算法进行优化,以缩短运行时间。经分析,算法的热点部分耗时受到非规则访存的严重影响,因此针对算法的结构和非规则访存模型,提出了数据预排序方法,并分析了该方法如何影响访存过程。在此基础上,利用数据对齐、循环分解进一步优化访存效率,利用负载均衡和互斥变量私有化的方法提高了Open MP的并行效率,最终将HGGF应用使用12线程加速11.6倍,同时取得了更好的可扩展性。主要有三点贡献:(1)分析了HGGF算法的非规则访存问题;(2)提出并分析了数据预排序方法;(3)使用数据对齐、循环分解、负载均衡、互斥变量私有化方法提高了HGGF应用的并行性能。
[Abstract]:The HGGF(halo-based galaxy group finder algorithm implements the cluster search of galaxies based on dark matter halo. It plays an important role in the study of the large-scale structure of the universe and the evolution of the universe. However, due to the growth of the data scale, it is urgent to optimize the HGGF algorithm in order to shorten the running time. The hot spot of the algorithm is seriously affected by the irregular memory access, so a data pre-sorting method is proposed for the structure of the algorithm and the irregular memory access model. Based on the analysis of how the method affects the memory access process, the efficiency of accessing memory is further optimized by using data alignment and cyclic decomposition. The parallel efficiency of Open MP is improved by using load balancing and mutex privatization, and the HGGF application is finally accelerated by 11.6 times using 12 threads. At the same time, better scalability is achieved. There are three contributions: 1) the problem of irregular memory access in HGGF algorithm is analyzed. 2) the data pre-sorting method is proposed and analyzed. The parallel performance of HGGF applications is improved by using data alignment, cyclic decomposition, load balancing and mutex privatization.
【作者单位】： 上海交通大学高性能计算中心;NVIDIA
【基金】：国家高技术研究发展计划(863计划) 日本学术振兴会项目~~
【分类号】：P14
【正文快照】： 1引言 随着天文观测能力的不断提高,天文数据急剧增加,其中星系的观测总量已经达到109量级。面对来自大型数字巡天计划的海量数据,如何从数据中迅速准确地提取所需要的内容,直接影响着天文学的发展和研究进程。其中,如何将可观测的星系和理论中不可见的暗物质晕联系起来,是天，

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