Optimizing memory bandwidth efficiency with user-preferred kernel merge

Earth system modeling computations use stencils extensively while running many kernels. Optimal coding of the stencils is essential to efficiently use memory bandwidth of an underlying hardware. This is important as stencil computations are memory bound. Even when the code within one kernel is written to optimally use the memory bandwidth, there are still opportunities for further optimization at the inter-kernel level. Stencils naturally exhibit data locality, and executing a sequence of stencils within separate kernels could waste caching capabilities. Interprocedural optimizations such as merging of kernels bears the potential to improve the use of the caches. However, due to semantic restrictions, it is difficult to achieve on general purpose languages. Some tools were developed to automatically fuse loops instead of the manual optimization. However, scientists still implement fusion in different levels of loop nests manually to find optimal performance. To allow scientists to still apply loop fusions equal to manual loop fusion, we develop a technique to automatically analyze the code and allow scientists to select their preferred fusions by providing automatic dependency analysis and code transformation; this also bears the potential for automatic tools that make smart choices on behalf of the user. Our work is done using GGDML language extensions which enables performance portability over different architectures using a single source code