论文标题
分配优化算法分化重用指数管理策略
Assign optimization for algorithmic differentiation reuse index management strategies
论文作者
论文摘要
通常，识别原始变量和伴随变量通常是通过操作员超载算法分化工具的索引来完成的。一种方法是线性管理方案，该方案易于实现并支持复制语句的内存优化。另一种方法执行了索引的重复使用，这需要更多的实施工作，但导致伴随矢量较小。因此，算法分化的向量模式通过重复使用管理方案更好地尺度。在本文中，我们提出了一种新颖的方法，可以重用索引并允许复制优化，从而结合了上述两个方案的优势。将新方法与使用计算流体动力学求解器SU2的简单合成测试用例和现实世界中的已知方法进行了比较。
The identification of primal variables and adjoint variables is usually done via indices in operator overloading algorithmic differentiation tools. One approach is a linear management scheme, which is easy to implement and supports memory optimization for copy statements. An alternative approach performs a reuse of indices, which requires more implementation effort but results in much smaller adjoint vectors. Therefore, the vector mode of algorithmic differentiation scales better with the reuse management scheme. In this paper, we present a novel approach that reuses the indices and allows the copy optimization, thus combining the advantages of the two aforementioned schemes. The new approach is compared to the known approaches on a simple synthetic test case and a real-world example using the computational fluid dynamics solver SU2.
