Parallel Computing for Module-Based Computational Experiment
Large-scale scientific code plays an important role in scientific researches. In order to facilitate module and element evaluation in scientific applications, we introduce a unit testing framework and describe the demand for module-based experiment customization. We then develop a parallel version of the unit testing framework to handle long-term simulations with a large amount of data. Specifically, we apply message passing based parallelization and I/O behavior optimization to improve the performance of the unit testing framework and use profiling result to guide the parallel process implementation. Finally, we present a case study on litter decomposition experiment using a standalone module from a large-scale Earth System Model. This case study is also a good demonstration on the scalability, portability, and high-efficiency of the framework.
KeywordsParallel computing Scientific software Message passing based parallelization Profiling
Some part of this research is included in Yao’s Ph.D. dissertation (A Kernel Generation Framework for Scientific Legacy Code ) with the University of Tennessee, Knoxville. TN. This research was funded by the U.S. Department of Energy, Office of Science, Biological and Environmental Research program (E3SM and TES) and Advanced Scientific Computing Research program. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.
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