Parallelization of a Dynamic Unstructured Algorithm Using Three Leading Programming Paradigms

The success of parallel computing in solving real-life computationally-intensive problems relies on their efficient mapping and execution on large-scale multiprocessor architectures. Many important applications are both unstructured and dynamic in nature, making their efficient parallel implementation a daunting task. This paper presents the parallelization of a dynamic unstructured mesh adaptation algorithm using three popular programming paradigms on three leading supercomputers. We examine an MPI message-passing implementation on the Cray T3E and the SGI Origin2000, a shared-memory implementation using cache coherent nonuniform memory access (CC-NUMA) of the Origin2000, and a multithreaded version on the newly-released Tera Multithreaded Architecture (MTA). We compare several critical factors of this parallel code development, including runtime, scalability, programmability, portability, and memory overhead. Our overall results demonstrate that multithreaded systems offer tremendous potential for quickly and efficiently solving some of the most challenging real-life problems on parallel computers.