Abstract
HPC systems are widely used for accelerating calculation-intensive irregular applications, e.g., molecular dynamics (MD) simulations, astrophysics applications, and irregular grid applications. As the scalability and complexity of current HPC systems keeps growing, it is difficult to parallelize these applications in an efficient fashion due to irregular communication patterns, load imbalance issues, dynamic characteristics, and many more. This paper presents a fine granular programming scheme, on which programmers are able to implement parallel scientific applications in a fine granular and SPMD (single program multiple data) fashion. Different from current programming models starting from the global data structure, this programming scheme provides a high-level and object-oriented programming interface that supports writing applications by focusing on the finest granular elements and their interactions. Its implementation framework takes care of the implementation details e.g., the data partition, automatic EP aggregation, memory management, and data communication. The experimental results on SuperMUC show that the OOP implementations of multi-body and irregular applications have little overhead compared to the manual implementations using C++ with OpenMP or MPI. However, it improves the programming productivity in terms of the source code size, the coding method, and the implementation difficulty.
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Huang, H., Jiang, L., Dong, W., Chang, R., Hou, Y., Gerndt, M. (2016). A Fine-Granular Programming Scheme for Irregular Scientific Applications. In: Wu, J., Li, L. (eds) Advanced Computer Architecture. ACA 2016. Communications in Computer and Information Science, vol 626. Springer, Singapore. https://doi.org/10.1007/978-981-10-2209-8_12
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DOI: https://doi.org/10.1007/978-981-10-2209-8_12
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