Some programming languages are easy to develop at the cost of slow execution, while others are lightning fast at run time but are much more difficult to write. Julia is a programming language that aims to be the best of both worlds—a development and production language at the same time. To test Julia’s utility in scientific high-performance computing (HPC), we built an unstructured-mesh shallow water model in Julia and compared it against an established Fortran-MPI ocean model, MPAS-Ocean, as well as a Python shallow water code. Three versions of the Julia shallow water code were created, for: single-core CPU; graphics processing unit (GPU); and Message Passing Interface (MPI) CPU clusters. Comparing identical simulations revealed that our first version of the single-core CPU Julia model was 13 times faster than Python. Further Julia optimizations, including static typing and removing implicit memory allocations, provided an additional 10–20x speed-up of the single-core CPU Julia model. The GPU-accelerated Julia code is extremely fast, with a speed-up of 230-380x compared to the single-core CPU Julia code if communication with the GPU occurs every 10 time steps. Parallelized Julia-MPI performance was identical to Fortran-MPI MPAS-Ocean for low processor counts, and ranges from 2x faster to 2x slower for higher processor counts. Our experience is that Julia development is fast and convenient for prototyping, but that Julia requires further investment and expertise to be competitive with compiled codes. We provide advice on Julia code optimization for HPC systems.
