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CG-Kit is a new code generation toolkit that we propose as a solution forportability and maintainability for scientific computing applications. Thedevelopment of CG-Kit is rooted in the urgent need created by the shiftinglandscape of high-performance computing platforms and the algorithmiccomplexities of a particular large-scale multiphysics application: Flash-X.This combination leads to unique challenges including handling an existinglarge code base in Fortran and/or C/C++, subdivision of code into a greatvariety of units supporting a wide range of physics and numerical methods,different parallelization techniques for distributed- and shared-memory systemsand accelerator devices, and heterogeneity of computing platforms requiringcoexisting variants of parallel algorithms. The challenges demand thatdevelopers determine custom abstractions and granularity for code generation.CG-Kit tackles this with standalone tools that can be combined into highlyspecific and, we argue, highly effective portability and maintainability toolchains. Here we present the design of our new tools: parametrized source trees,control flow graphs, and recipes. The tools are implemented in Python. Althoughthe tools are agnostic to the programming language of the source code, we focuson C/C++ and Fortran. Code generation experiments demonstrate the generation ofvariants of parallel algorithms: first, multithreaded variants of the basicAXPY operation (scalar-vector addition and vector-vector multiplication) tointroduce the application of CG-Kit tool chains; and second, variants ofparallel algorithms within a hydrodynamics solver, called Spark, from Flash-Xthat operates on block-structured adaptive meshes. In summary, code generatedby CG-Kit achieves a reduction by over 60% of the original C/C++/Fortran sourcecode.

CG-Kit: Code Generation Toolkit for Performant and Maintainable Variants of Source Code Applied to Flash-X Hydrodynamics Simulations