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
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While waiting for AMD 4th Gen EPYC 'Genoa' instances to become available via the major public cloud providers, I was curious to see how existing AMD EPYC Milan instances compare to Intel's new Sapphire Rapids instances in public preview on Google Cloud.
Back in October Google announced their Compute Engine C3 instances in private preview that featured 4th Gen Xeon Scalable 'Sapphire Rapids' processors as well as making use of Google's custom Intel Infrastructure Processing Unit (IPU).
In our latest @HPCpodcast episode, Shahin and Doug discuss the latest HPC industry news, including several quantum computing developments, Google’s [.]