2016 Ieee International Conference on Big Data (big Data) Three-dimensional Spatial Join Count Exploiting Cpu Optimized Str R-tree
2016 Ieee International Conference on Big Data (big Data) Three-dimensional Spatial Join Count Exploiting Cpu Optimized Str R-tree
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2016 IEEE International Conference on Big Data（大数据）三维空间连接计数利用CPU优化的Str R树
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通讯作者：
O. Tatebe
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作者：
Ryuya Mitsuhashi;H. Kawashima;T. Nishimichi;O. Tatebe
- 在这项研究中，我们试图解决有关空间连接计数的问题，其中对于给定的模拟结果，光环周围的粒子数量仅计数一次。一个有效的空间索引是必要的加速计数，因此，我们提出了一个CPU优化的排序瓦片递归R-树，采用并行基数排序和节点包装线程池和单指令多数据指令。在天文数据进行的实验中，所提出的方法表现出的性能提高了26.8倍相比，使用传统的CPU优化的R树。我们还提出了一个部分物化的方法来处理大量的数据，超过了主存的容量。为了加速这种方法，我们提出了一个构造-搜索-销毁管道，利用线程池来隐藏索引的构造和销毁的延迟。与传统的CPU优化R树相比，流水线方法实现了27.5倍的性能改善。我们所有的代码都可以在GitHub上找到。
—In this study, we attempt to address the issue regarding the spatial join count, where in the number of particles around a halo is counted only once for a given simulation result. An efficient spatial index is necessary for accelerated counting; therefore, we propose a CPU optimized sort-tile-recursive R-tree that employs a parallel radix sort and node packing with thread pool and single instruction multiple data instructions. In an experiment conducted with astronomical data, the proposed method demonstrates an improvement in performance by 26.8 times compared with that using a conventional CPU optimized R-tree. We also propose a partial materialization approach to handle large amount of data that exceeds the capacity of main memory. To accelerate the approach, we propose a construct-search-destruct pipeline that exploits a thread pool to conceal the latency of the construction and destruction of the index. The pipelining method achieves an improvement in performance by 27.5 times compared with that of a conventional CPU optimized R-tree. All our codes are available on GitHub.
影响因子：
56.9
作者：
N. Yoshida;K. Omukai;L. Hernquist
通讯作者：
N. Yoshida;K. Omukai;L. Hernquist