Optimized parallel implementations on GPU or CPU have dramatically enhanced the fidelity, resolution and accuracy of physical simulations and mesh-based algorithms. However, attaining optimal performance requires expert knowledge and might demand complex code and memory layout optimizations. This adds to the fact that physical simulation algorithms require the implementation of derivatives, which can be a tedious and error-prone process. In recent years, researchers and practitioners have investigated the concept of designing systems that allow for a more expressive definition of mesh-based simulation code. These systems leverage domain-specific languages (DSL), automatic differentiation or symbolic computing to enhance readability of implementations without compromising performance. We follow this line of work and propose a symbolic code generation approach tailored to mesh-based computations on parallel devices. Our system extends related work by incorporating collision handling and a data access synchronization approach, enabling rapid sparse matrix assembly.

中文翻译：

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使用自动代码生成的基于网格的仿真框架


GPU 或 CPU 上优化的并行实现显著提高了物理仿真和基于网格的算法的保真度、分辨率和准确性。但是，获得最佳性能需要专业知识，并且可能需要复杂的代码和内存布局优化。这增加了一个事实，即物理仿真算法需要实现导数，这可能是一个乏味且容易出错的过程。近年来，研究人员和从业者研究了设计系统的概念，这些系统允许对基于网格的仿真代码进行更具表现力的定义。这些系统利用域特定语言 （DSL）、自动微分或符号计算来增强实现的可读性，而不会影响性能。我们遵循这一工作路线，并提出了一种为并行设备上基于网格的计算量身定制的符号代码生成方法。我们的系统通过整合碰撞处理和数据访问同步方法扩展了相关工作，从而实现了快速的稀疏矩阵组装。