Hybrid CPU-GPU computation of adjoint derivatives in time domain

For the solution of large-scale inverse scattering problems - in either acoustic or electromagnetic domain - gradient based optimization approaches are a method of choice, especially when the derivatives regarding the parameter of interest can be obtained from adjoint fields [1], [2]. Gradients regarding a parameter can be effectively computed using an adjoint approach where the direct and adjoint fields are integrated in opposite temporal direction. This yielding high memory consumption, the memory reduced computation of the gradients using checkpointing and recomputation of states from the checkpoint is a method of choice. We propose the use of graphics processing units (GPU) to accelerate the computation by solving the direct problem on the GPU and the adjoint problem on the CPU of the computer. The implementation of pipelining based on CUDA streams and pinned memory masks the memory transfer between host and GPU and allows for the computation of the adjoint derivatives at only a little more than twice the time of the solution of the direct problem.