Evaluating PaRSEC Through Matrix Computations in Scientific Applications

Task-based runtime systems, characterized by their dynamic execution models and optimized resource management, contribute significantly to the computational revolution. They enable the development of more intricate and adaptable algorithms, essential in the field of computational science. This paper provides an in-depth exploration of the PaRSEC task-based runtime system, particularly focusing on its versatility in managing a variety of matrix computations. More specifically, we examine PaRSEC’s role in enhancing efficiency when solving linear systems and processing dense, low-rank, mixed-precision, and sparse matrix operations, which are crucial in scientific applications, e.g., climate/weather prediction and 3D unstructured mesh deformation-the primary focus of this study. Through experimentation and analysis, we showcase PaRSEC’s ability to significantly boost computational efficiency and scalability across a range of computationally intensive and less intensive tasks on various hardware architectures. Our findings not only underscore the potential of PaRSEC in advancing sustainable, efficient, and accurate domain modeling and simulation but also emphasize the growing necessity of task-based runtime systems in supporting the next generation of matrix computations.