HPC Infrastructure for Solid Earth Simulation on Parallel Computers

Recently, various types of parallel computers with various types of architectures and processing elements (PE) have emerged, which include PC clusters and the Earth Simulator. Moreover, users can easily access to these computer resources through network on Grid environment. It is well-known that thorough tuning is required for programmers to achieve excellent performance on each computer. The method for tuning strongly depends on the type of PE and architecture. Optimization by tuning is a very tough work, especially for developers of applications. Moreover, parallel programming using message passing library such as MPI is another big task for application programmers. In GeoFEM project (http://gefeom.tokyo.rist.or.jp), authors have developed a parallel FEM platform for solid earth simulation on the Earth Simulator, which supports parallel I/O, parallel linear solvers and parallel visualization. This platform can efficiently hide complicated procedures for parallel programming and optimization on vector processors from application programmers. This type of infrastructure is very useful. Source codes developed on PC with single processor is easily optimized on massively parallel computer by linking the source code to the parallel platform installed on the target computer. This parallel platform, called HPC Infrastructure will provide dramatic efficiency, portability and reliability in development of scientific simulation codes. For example, line number of the source codes is expected to be less than 10,000 and porting legacy codes to parallel computer takes 2 or 3 weeks. Original GeoFEM platform supports only I/O, linear solvers and visualization. In the present work, further development for adaptive mesh refinement (AMR) and dynamic load-balancing (DLB) have been carried out. In this presentation, examples of large-scale solid earth simulation using the Earth Simulator will be demonstrated. Moreover, recent results of a parallel computational steering tool using an MxN communication model will be shown. In an MxN communication model, the large-scale computation modules run on M PE's and high performance parallel visualization modules run on N PE's, concurrently. This can allow computation and visualization to select suitable parallel hardware environments respectively. Meanwhile, real-time steering can be achieved during computation so that the users can check and adjust the computation process in real time. Furthermore, different numbers of PE's can achieve better configuration between computation and visualization under Grid environment.