Abstract
We present Trixi.jl, a Julia package for adaptive high-order numerical simulations of hyperbolic partial differential equations. Utilizing Julia's strengths, Trixi.jl is extensible, easy to use, and fast. We describe the main design choices that enable these features and compare Trixi.jl with a mature open source Fortran code that uses the same numerical methods. We conclude with an assessment of Julia for simulation-focused scientific computing, an area that is still dominated by traditional high-performance computing languages such as C, C++, and Fortran.
Highlights
We are broadly interested in simulation-focused scientific computing, in particular numerical approximations for hyperbolic partial differential equations (PDEs), computational fluid dynamics (CFD), and related problems
While our Julia code is faster than the mature high-performance computing (HPC) Fortran code FLUXO for this non-trivial example, we do not claim that Julia is generally faster than Fortran, C, or C++
We would like to emphasize that well-written Julia code can be at least as fast as code written in these traditional scientific computing languages, as demonstrated by several microbenchmarks [5]
Summary
We are broadly interested in simulation-focused scientific computing, in particular numerical approximations for hyperbolic partial differential equations (PDEs), computational fluid dynamics (CFD), and related problems. We would like to have a code that is (1) extensible for research and development, (2) easy to understand and use for students and collaborators, (3) fast enough for applied 3D problems. We present an overview of the main features and design decisions of Trixi.jl, laying the ground for an extensible and easy-to-use framework of high-order methods for hyperbolic PDEs. we compare the serial performance with a mature high-performance computing (HPC) Fortran code, demonstrating that Julia is not generically slower than traditional HPC languages (and can even be faster in this particular case).
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