Science gateways - leveraging modeling and simulations in HPC infrastructures via increased usability

Modeling and simulations, which necessitate HPC infrastructures, are often based on complex scientific theories and involve interdisciplinary research teams. IT specialists support with the efficient access to HPC infrastructures. They design, implement and configure the simulations and models reflecting the sophisticated theoretical models and approaches developed and applied by domain researchers. Roles in such interdisciplinary teams may overlap dependent on the knowledge and experience with computational resources and/or the research domain. Bioinformaticians, for example, are in general trained to act as IT specialists, while having also a good knowledge about biology and chemistry to support the user community competently. Domain researchers are mainly not IT specialists and the requirement to employ HPC infrastructures via command line often forms a huge hurdle for them. Thus, there is the need to increase the usability of simulations and models on HPC infrastructures for the uptake by the user community. Science gateways form a solution, which offer a graphical user interface tailored to a specific research domain with a single point of entry for job and data management hiding the underlying infrastructure. In the last 10 years quite a few web development frameworks, science gateway frameworks and APIs with different foci and strengths have evolved to support the developers of science gateways in implementing an intuitive solution for a target research domain. The selection of a suitable technology for a specific use case is essential and helps reducing the effort in implementing the science gateway by re-using existing software or frameworks. Thus, a solution for a user community can be provided more efficiently. This paper goes into detail for science gateway concepts as well as information resources, gives examples for successful technologies and proposes criteria for choosing a technology for a use case.

[1]  Rion Dooley,et al.  Software-as-a-Service: The iPlant Foundation API , 2012 .

[2]  Edward A. Lee,et al.  Scientific workflow management and the Kepler system , 2006, Concurr. Comput. Pract. Exp..

[3]  Jim Groom,et al.  Docker - Build, Ship, and Run Any App, Anywhere , 2014 .

[4]  Carole A. Goble,et al.  The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud , 2013, Nucleic Acids Res..

[5]  Thomas Steinke,et al.  A Single Sign-On Infrastructure for Science Gateways on a Use Case for Structural Bioinformatics , 2012, Journal of Grid Computing.

[6]  Björn Hagemeier,et al.  UNICORE 6 — Recent and Future Advancements , 2010, Ann. des Télécommunications.

[7]  Richard Grunzke,et al.  Insights into the influence of dispersion correction in the theoretical treatment of guanidine‐quinoline copper(I) complexes , 2014, J. Comput. Chem..

[8]  Marlon E. Pierce,et al.  Apache Airavata: Design and Directions of a Science Gateway Framework , 2014, 2014 6th International Workshop on Science Gateways.

[9]  Miklós Kozlovszky,et al.  WS-PGRADE/gUSE Generic DCI Gateway Framework for a Large Variety of User Communities , 2012, Journal of Grid Computing.

[10]  Alexander Hoffmann,et al.  Geometrical and optical benchmarking of copper(II) guanidine–quinoline complexes: Insights from TD‐DFT and many‐body perturbation theory (part II) , 2014, J. Comput. Chem..

[11]  Srinath Perera,et al.  Apache airavata: a framework for distributed applications and computational workflows , 2011, GCE '11.

[12]  Sonja Herres-Pawlis,et al.  Geometrical and optical benchmarking of copper guanidine–quinoline complexes: Insights from TD‐DFT and many‐body perturbation theory† , 2014, J. Comput. Chem..

[13]  D. C. Englebart,et al.  Augmenting human intellect: a conceptual framework , 1962 .

[14]  Stephen Travis Pope,et al.  A cookbook for using the model-view controller user interface paradigm in Smalltalk-80 , 1988 .

[15]  A. Nekrutenko,et al.  Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences , 2010, Genome Biology.

[16]  Nancy Wilkins-Diehr,et al.  Who Cares about Science Gateways? A Large-Scale Survey of Community Use and Needs , 2014, 2014 9th Gateway Computing Environments Workshop.

[17]  Matthew R. Hanlon,et al.  Recipes 2.0: building for today and tomorrow , 2014, IWSG.

[18]  Gábor Terstyánszky,et al.  Meta-Metaworkflows for Combining Quantum Chemistry and Molecular Dynamics in the MoSGrid Science Gateway , 2014, 2014 6th International Workshop on Science Gateways.

[19]  Jim Basney,et al.  A Credential Store for Multi-tenant Science Gateways , 2014, 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[20]  Thomas Steinke,et al.  The MoSGrid Science Gateway - A Complete Solution for Molecular Simulations. , 2014, Journal of chemical theory and computation.

[21]  Thomas Steinke,et al.  Standards‐based metadata management for molecular simulations , 2014, Concurr. Comput. Pract. Exp..

[22]  Alexander Hoffmann,et al.  Corrigendum: Geometrical and optical benchmarking of copper(II) guanidine–quinoline complexes: Insights from TD‐DFT and many‐body perturbation theory (Part II) , 2015, J. Comput. Chem..