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

TL;DR

This paper introduces alternative quadrant encoding methods using Morton index and AVX2 vectorization in the p4est library, enhancing performance and memory efficiency for adaptive mesh refinement algorithms.

Contribution

It proposes novel encoding schemes for p4est primitives, leveraging SIMD instructions and monotonic indexing to improve computational efficiency and reduce memory usage.

Findings

Higher performance with alternative encodings

Reduced memory consumption

Effective SIMD vectorization implementation

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.