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GMD - Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPAS-Ocean (Version 7.1)

Abstract. Some programming languages are easy to develop at the cost of slow execution, while others are fast at runtime but 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, the Model for Prediction Across Scales–Ocean (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 Julia model was 13 times faster than Python using NumPy, where both used an unthreaded single-core CPU. Further Julia optimizations, including static typing and removing implicit mem ....

United Kingdom , United States , Markr Petersen , Los Alamos , Robertr Strauss , Sylwester Arabas , Office Of Science User Facility , Environmental Research , Us Department Of Energy , Office Of Science , National Nuclear Security Administration , Energy Exascale Earth System Model , Planetary Sciences , Los Alamos National Laboratory , Massachusetts Institute Of Technology , Software Environments , Methods Group , National Energy Research Scientific Computing Center , Institutional Computing Program , Department Of Earth , Massachusetts Institute , Nonlinear Studies , Prediction Across Scales , Message Passing Interface , Model Dev , Intel Cascade Lake ,