Benchmarking machine-learning software and hardware for quantitative economics

We investigate the performance of machine-learning software and hardware for quantitative economics. We show that the use of machine-learning software and hardware can significantly reduce computational time in compute-intensive tasks. Using a sovereign default model and the Least Squares Monte Carlo option pricing algorithm as benchmarks, we show that specialized hardware and software speed up calculations by up to four orders of magnitude when compared to programs written in popular high-level programming languages, such as MATLAB, Julia, Python/Numpy, and R, and high-performing low-level languages such as C++.