An Abstraction for Distributed Stencil Computations Using Charm++

Python has emerged as a popular programming language for scientific computing in recent years, thanks to libraries like Numpy and SciPy. Numpy, in particular, is widely utilized for prototyping numerical solvers using methods such as finite difference, finite volume, and multigrid. However, Numpy’s performance is confined to a single node, compelling programmers to resort to a lower-level language for running large-scale simulations. In this paper, we introduce CharmStencil, a high-level abstraction featuring a Numpy-like Python frontend and a highly efficient Charm++ backend. Employing a client-server model, CharmStencil maintains productivity with tools like Jupyter notebooks on the frontend while utilizing a high-performance Charm++ library on the backend for computation. We demonstrate that CharmStencil achieves orders of magnitude better single-threaded performance compared to Numpy and can scale to thousands of CPU cores. Additionally, we showcase superior performance compared to cuNumeric and Numba, popular Python libraries for parallel array computations.