DescriptionWe develop an adaptive multistep predictor for accelerating memory bandwidth-bound dynamic implicit finite-element simulations. We predict the solutions for future time steps adaptively using highly-efficient matrix-vector product kernels with multiple right-hand sides to reduce the number of iterations required in the solver. By applying the method to a conjugate gradient solver with 3 x 3 block Jacobi preconditioning, we were able to achieve a 42% speedup on a Skylake-SP Xeon Gold cluster for a typical earthquake ground motion problem. As the method enables the number of iterations, and thus the communication frequency, to be reduced, the developed solver was able to attain high size-up scalability: 80.6% up to 32,768 compute nodes on the K computer. The developed predictor can also be applied to other iterative solvers and is thus expected to be useful for wide range of dynamic implicit finite-element simulations.