Alternative quadrant representations with Morton index and AVX2 vectorization for AMR algorithms within the p4est software library

Mikhail KirilinINS, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany, Carsten BursteddeINS, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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引用次数: 0

Abstract

We present a technical enhancement within the p4est software for parallel adaptive mesh refinement. In p4est primitives are stored as octants in three and quadrants in two dimensions. While, classically, they are encoded by the native approach using its spatial and refinement level, any other mathematically equivalent encoding might be used instead. Recognizing this, we add two alternative representations to the classical, explicit version, based on a long monotonic index and 128-bit AVX quad integers, respectively. The first one requires changes in logic for low-level quadrant manipulating algorithms, while the other exploits data level parallelism and requires algorithms to be adapted to SIMD instructions. The resultant algorithms and data structures lead to higher performance and lesser memory usage in comparison with the standard baseline. We benchmark selected algorithms on a cluster with two Intel(R) Xeon(R) Gold 6130 Skylake family CPUs per node, which provides support for AVX2 extensions, 192 GB RAM per node, and up to 512 computational cores in total.
p4est软件库中AMR算法的Morton索引和AVX2矢量化的可选象限表示
我们在p4est软件中提出了一种技术改进,用于并行自适应网格细化。在p4est中,原语存储为三维的八分位数和二维的象限。虽然它们通常是通过使用其空间和细化级别的替代方法进行编码的,但也可以使用其他数学上等效的编码。认识到这一点,我们在经典的显式版本上添加了两种替代表示,分别基于长单调索引和128位AVX四整数。第一种方法需要更改低级象限操作算法的逻辑,而另一种方法利用数据级并行性,并要求算法适应SIMD指令。与标准基线相比,由此产生的算法和数据结构带来了更高的性能和更少的内存使用。我们在一个集群上对选择的算法进行基准测试,每个节点有两个Intel(R) Xeon(R) Gold6130 Skylake系列cpu,它提供对AVX2扩展的支持,每个节点有192 GB RAM,总共有多达512个计算核心。
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