Gathering requirements for advancing simulations in HPC infrastructures via science gateways. Gesing, S., Dooley, R., Pierce, M., Krüger, J., Grunzke, R., Herres-Pawlis, S., & Hoffmann, A. Future Generation Computer Systems, Elsevier B.V., 2016.
Gathering requirements for advancing simulations in HPC infrastructures via science gateways [link]Website  doi  abstract   bibtex   
Compute-intensive simulations are often based on complex scientific theories and necessitate high-performance computing (HPC) infrastructures to deliver results in reasonable time. While domain researchers are experts in their field and apply sophisticated theoretical models in computational simulations, they are not necessarily also HPC experts or IT specialists in general. Thus, they appreciate easy-to-use solutions tailored to their research, which hide the complex underlying computing and data infrastructures. Science gateways form such end-to-end solutions and their development for compute-intensive simulations necessitates expertise to connect HPC research infrastructures including grid and cloud infrastructures to support with the efficient access to such resources. HPC experts and IT specialists fulfilling this task may have only rudimentary knowledge about the research domain of a simulation. Thus, it is crucial that they gather the requirements of a research use case, which they aim to support efficiently via a science gateway.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 introduces the general architecture of science gateways, goes into detail for criteria to design science gateways efficiently and gives examples of mature science gateways and science gateway frameworks. © 2017 Elsevier B.V.
@article{
 title = {Gathering requirements for advancing simulations in HPC infrastructures via science gateways},
 type = {article},
 year = {2016},
 keywords = {Computation theory; Large scale systems; Web crawl,Computational science; Computational simulation;,Distributed computer systems},
 websites = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015395737&doi=10.1016%2Fj.future.2017.02.042&partnerID=40&md5=083c74b7a31e9af48390ff775de4f7cf},
 publisher = {Elsevier B.V.},
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 abstract = {Compute-intensive simulations are often based on complex scientific theories and necessitate high-performance computing (HPC) infrastructures to deliver results in reasonable time. While domain researchers are experts in their field and apply sophisticated theoretical models in computational simulations, they are not necessarily also HPC experts or IT specialists in general. Thus, they appreciate easy-to-use solutions tailored to their research, which hide the complex underlying computing and data infrastructures. Science gateways form such end-to-end solutions and their development for compute-intensive simulations necessitates expertise to connect HPC research infrastructures including grid and cloud infrastructures to support with the efficient access to such resources. HPC experts and IT specialists fulfilling this task may have only rudimentary knowledge about the research domain of a simulation. Thus, it is crucial that they gather the requirements of a research use case, which they aim to support efficiently via a science gateway.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 introduces the general architecture of science gateways, goes into detail for criteria to design science gateways efficiently and gives examples of mature science gateways and science gateway frameworks. © 2017 Elsevier B.V.},
 bibtype = {article},
 author = {Gesing, S and Dooley, R and Pierce, M and Krüger, J and Grunzke, R and Herres-Pawlis, S and Hoffmann, A},
 doi = {10.1016/j.future.2017.02.042},
 journal = {Future Generation Computer Systems}
}
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