偏微分方程
计算机科学
网格
计算科学
数值分析
光学(聚焦)
数值偏微分方程
应用数学
有限差分法
数学优化
并行计算
算法
数学
数学分析
几何学
物理
光学
作者
Jiajun Li,Y. Y. Zhang,Hao Zheng,Ke Wang
标识
DOI:10.1145/3579371.3589083
摘要
Partial Differential Equations (PDEs) are widely employed to describe natural phenomena in many science and engineering fields. Many PDEs do not have analytical solutions, hence, numerical methods have become prevalent for approximating PDE solutions. The most widely used numerical method is the Finite Difference Method (FDM), which requires fine grids and high-precision numerical iterations that are both compute- and memory-intensive. PDE-solving accelerators have been proposed in the literature, however, they usually focus on specific types of PDEs with rigid grid sizes which limits their broader applicability. Besides, they rarely provided insight into the optimizations of parallel computing and data accesses for solving PDEs, which hinders further improvements in performance and energy efficiency.
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