This study presents an investigation into two important topics for HPC: the data-driven reduced initial condition (RIC) algorithm and an opportunistic data operations platform (ODOP). The objective is to demonstrate how these two elements can function in synergy to facilitate the realization of generic GPU-accelerated iterative stencil loops (ISLs) within exascale computing frameworks. ISLs represent a class of algorithms that are crucial for a multitude of computational tasks, particularly in high-performance computing (HPC), where structured grid computations are prevalent. Through the parallelism of ISLs, coupled with GPU technology, diverse applications ranging from image processing are capable of solving partial differential equations (PDEs).

Iterative Stencil Loops (ISLs) are regarded as fundamental components in high-performance computing (HPC), due to their efficiency in updating grid elements based on neighbourhood sampling. Traditionally implemented on structured grids, ISLs have demonstrated remarkable scalability on parallel architectures, especially on GPUs. Despite their efficacy in compute-bound scenarios, they have potential in optimising ISLs by integrating opportunistic data analysis tasks known as Opportunistic Data Operations (ODO). Structured grid computations, exemplified by ISLs, are integral to numerous HPC applications due to their regularity and parallelizability. Recent advancements in GPU technology have enhanced the performance of stencil-based algorithms, particularly in scenarios where large stencils are employed. Nevertheless, the potential for optimisation of ISLs extends beyond pure stencil operations, necessitating the incorporation of opportunistic data analysis tasks. By capitalising on the parallel processing capabilities of GPUs, ISLs can combine executing data analysis tasks opportunistically, thereby enhancing overall computational efficiency. The RIC algorithm employs data-driven techniques to expedite the initial condition computations, thereby reducing the computational overhead. Concurrently, the ODOP provides a versatile framework for influencing opportunistic data analysis tasks in conjunction with ISL executions. Through the appropriate integration of these components, a generic GPU-accelerated ISL can be realised, which is poised to operate efficiently in exascale computing environments.

By offloading data analysis tasks to opportune moments within ISL executions, it is possible to achieve significant improvements in computational efficiency, data quality, and cost-effectiveness. Furthermore, the versatility of the ODOP enables seamless integration with existing HPC architectures, thereby ensuring scalability and compatibility across diverse computational frameworks.