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Science Gateways in HPC: Usability Meets Efficiency and Effectiveness

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Modeling and Simulation in HPC and Cloud Systems

Part of the book series: Studies in Big Data ((SBD,volume 36))

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Abstract

The amount of available data as well as computational methods for research have grown faster in the last decade than ever before. Methods that address compute-intensive and data-intensive challenges often apply sophisticated computing and data infrastructures such High-Performance Computing. Effectiveness and efficiency are the basis to achieve meaningful results in reasonable time in such infrastructures and science gateways consider additionally usability to support users in their research. The main goals of science gateways are to lower the barrier of using complex methods and infrastructures; to connect computing and data resources seamlessly behind an easy-to-use interface; and to share methods and data in a community. We go into detail on the concept of science gateways and their design requirements. Furthermore, we discuss the challenges for sharing research in science gateways and thus, aiming at reusability of scientific methods and reproducibility of science.

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Acknowledgements

This chapter is based upon work from COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet), supported by COST (European Cooperation in Science and Technology).

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Authors and Affiliations

  1. University of Notre Dame, Notre Dame, USA

    Sandra Gesing

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  1. Sandra Gesing
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Correspondence to Sandra Gesing .

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  1. Institute of Computer Science, Cracow University of Technology, Cracow, Poland

    Joanna Kołodziej

  2. Department of Computer Science, University Politehnica of Bucharest, Bucharest, Romania

    Florin Pop

  3. Department of Computer Science, University Politehnica of Bucharest, Bucharest, Romania

    Ciprian Dobre

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Gesing, S. (2018). Science Gateways in HPC: Usability Meets Efficiency and Effectiveness. In: Kołodziej, J., Pop, F., Dobre, C. (eds) Modeling and Simulation in HPC and Cloud Systems. Studies in Big Data, vol 36. Springer, Cham. https://doi.org/10.1007/978-3-319-73767-6_5

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  • DOI: https://doi.org/10.1007/978-3-319-73767-6_5

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  • Online ISBN: 978-3-319-73767-6

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