A scalable framework for adaptive computational general relativity on heterogeneous clusters
A scalable framework for adaptive computational general relativity on heterogeneous clusters
复制标题
DOI：
10.1145/3330345.3330346
复制
发表时间：
2019-06
期刊：
影响因子：
--
通讯作者：
Milinda Fernando;D. Neilsen;E. Hirschmann;H. Sundar
中科院分区：
文献类型：
--
作者：
Milinda Fernando;D. Neilsen;E. Hirschmann;H. Sundar
我们提出了一个便携式且高度可观的框架，该框架针对天体物理学和数字相对社区中的问题。该框架将平行的Dendro Octree与小波自适应多解析和自动代码生成物理模块结合在一起，以求解BSSNOK配方中的一般相对性的爱因斯坦方程。这项工作的目的是对二进制黑洞和中子星合并进行先进的，大规模平行的数值模拟，包括二进制黑洞的中间质量比灵感来自质量比的中间质量比率（IMRIS），其质量比为100：1。这些研究将用于研究用于LIGO数据分析的波形，并校准产生重力波形的近似方法。这项工作的关键贡献是开发自动代码生成器，用于支持SIMD矢量化，OpenMP和CUDA的计算相对论，并结合了有效的分布式存储器自适应数据结构。这些使有效的代码的开发显示出极佳的弱可伸缩性，最高可在ORNL的泰坦上进行131K核心，用于二进制合并的质量比，最高为100。
We present a portable and highly-scalable framework that targets problems in the astrophysics and numerical relativity communities. This framework combines together the parallel Dendro octree with wavelet adaptive multiresolution and an automatic code-generation physics module to solve the Einstein equations of general relativity in the BSSNOK formulation. The goal of this work is to perform advanced, massively parallel numerical simulations of binary black hole and neutron star mergers, including Intermediate Mass Ratio Inspirals (IMRIs) of binary black holes with mass ratios on the order of 100:1. These studies will be used to study waveforms for use in LIGO data analysis and to calibrate approximate methods for generating gravitational waveforms. The key contribution of this work is the development of automatic code generators for computational relativity supporting SIMD vectorization, OpenMP, and CUDA combined with efficient distributed memory adaptive data-structures. These have enabled the development of efficient codes that demonstrate excellent weak scalability up to 131K cores on ORNL's Titan for binary mergers for mass ratios up to 100.