  A large part of modern research, especially in the broad field of complex
systems, relies on the numerical integration of PDEs, with and without
stochastic noise. This is usually done with eiher in- house made codes or
external packages like MATLAB, Mathematica, Fenicsx, OpenFOAM, Dedalus, and
others. These packages rarely offer a good combination of speed, generality,
and the option to easily add stochasticity to the system, while in-house codes
depend on certain expertise to obtain good performance, and are usually written
for each specific use case, sacrificing modularity and reusability. This paper
introduces a package written in CUDA C++, thus enabling by default gpu
acceleration, that performs pseudo-spectral integration of generic stochastic
PDEs in flat lattices in one, two and three dimensions. This manuscript
describes how the basic functionality of cuPSS, with an example and
benchmarking, showing that cuPSS offers a considerable improvement in speed
over other popular finite-difference and spectral solvers.
