This thesis consists of three applications of contemporary high-performance computing to accelerate large-scale dynamic models in economics and finance. The first chapter is entitled “Scalable high-dimensional dynamic stochastic economic modeling” and presents a highly parallelizable and flexible computational method to solve high-dimensional stochastic dynamic economic models. By exploiting the generic iterative structure of this broad class of economic problems, we propose a parallelization scheme that favors hybrid massively parallel computer architectures. Numerical experiments at the Swiss National Supercomputing Centre show that high-dimensional international real business cycle models can be efficiently solved in parallel up to 2,048 compute nodes. The second chapter is called “Rethinking large-scale economic modeling for efficiency: optimizations for GPU and Xeon Phi clusters” and proposes a massively parallelized and optimized framework to solve high-dimensional dynamic stochastic economic models on modern GPU- and KNL-based clusters. Numerical experiments show that our framework scales to at least 4,096 compute nodes. The third chapter, titled “GPU-Accelerated Dynamic Human Capital Models” develops a generic computational method for dynamic discrete-choice models. We align the generic numerical properties of the models under consideration with the recent advancements in GPU computing hardware in order to solve, simulate, and calibrate models of great complexity in relatively short times. Our tests show a speedup of at least three orders of magnitude over the previous state of the art.