Abstract
The increasing complexity and heterogeneity of extreme scale systems makes the optimization of large scale scientific applications particularly challenging. Efficiently leveraging these complex systems requires a great deal of technical expertise and a considerable amount of man-hours. The computational neuroscience community relies on an handful of those frameworks to model the electrical activity of brain tissue at different scales. As the members of the Blue Brain Project actively contribute to a large part of those frameworks, it becomes mandatory to implement a strategy to reduce the overall development cost. Therefore, we present Neuromapp, a computational neuroscience mini-application framework. Neuromapp consists of a number of mini-apps (small standalone applications) that represent a single functionality in one of the large scientific frameworks. The collection of several mini-apps forms a skeleton which is able to reproduce the original workflow of the scientific application. Thus, it becomes easy to investigate both single component and workflow optimizations, new software and hardware systems or future system design. New solutions can then be integrated into the large scientific applications if proved to be successful, reducing the overall development and optimization effort.
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Notes
- 1.
In the vocabulary of NEURON simulator, the synonym mechanisms is used for channels/synapses key word.
- 2.
One could argue that the sleeping phase is not needed, or it is even disturbing the I/O results, as it allows time for the I/O library to flush internal buffers in between two I/O calls. However, if we remove the sleep call, the mini-app would no longer mimic the behavior of the real framework and, therefore, any extrapolation to the real framework would be incorrect.
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Acknowledgments
We would like to thank the HPC team of BBP, Till Schumann and Michael Hines for their discussions and contributions; DDN developers for the help and feedback provided and CSCS for the hardware resources and support provided. This work has been funded by the EPFL Blue Brain Project (funded by the Swiss ETH board) and the Supercomputing and Modeling for the Human Brain (SMHB) project supported by the German Helmholtz Association.
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Ewart, T., Planas, J., Cremonesi, F., Langen, K., Schürmann, F., Delalondre, F. (2017). Neuromapp: A Mini-application Framework to Improve Neural Simulators. In: Kunkel, J.M., Yokota, R., Balaji, P., Keyes, D. (eds) High Performance Computing. ISC High Performance 2017. Lecture Notes in Computer Science(), vol 10266. Springer, Cham. https://doi.org/10.1007/978-3-319-58667-0_10
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