In shared-memory parallel automatic differentiation, shared inputs among simultaneous thread-local preaccumulations lead to data races if Jacobians are accumulated with a single, shared vector of adjoint variables. In this work, we discuss the benefits and tradeoffs of re-enabling such preaccumulations by a transition to suitable local adjoint variables. In particular, we assess the performance of mapped local adjoints in discrete adjoint computations in the multiphysics simulation suite SU2.

翻译：在共享内存并行自动微分中，若使用单个共享的伴随变量向量进行雅可比矩阵的累积，则同时进行的线程局部预累积过程中共享输入会导致数据竞争。本研究探讨了通过过渡到合适的局部伴随变量来重新启用此类预累积的优势与权衡。具体而言，我们在多物理场仿真套件SU2中评估了映射局部伴随变量在离散伴随计算中的性能表现。