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020 | _ | _ | |a 978-3-319-53861-7 (print) |
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024 | 7 | _ | |a 10.1007/978-3-319-53862-4_21 |2 doi |
024 | 7 | _ | |a 0302-9743 |2 ISSN |
024 | 7 | _ | |a 1611-3349 |2 ISSN |
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100 | 1 | _ | |a Senk, Johanna |0 P:(DE-Juel1)162130 |b 0 |e Corresponding author |u fzj |
111 | 2 | _ | |a Jülich Aachen Research Alliance (JARA) High-Performance Computing Symposium |g JHPCS '16 |c Aachen |d 2016-10-04 - 2016-10-05 |w Germany |
245 | _ | _ | |a A Collaborative Simulation-Analysis Workflow for Computational Neuroscience Using HPC |
260 | _ | _ | |a Cham |c 2017 |b Springer International Publishing |
295 | 1 | 0 | |a High-Performance Scientific Computing / Di Napoli, Edoardo (Editor) ; Cham : Springer International Publishing, 2017, Chapter 21 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-319-53861-7=978-3-319-53862-4 ; doi:10.1007/978-3-319-53862-4 |
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490 | 0 | _ | |a Lecture Notes in Computer Science |v 10164 |
520 | _ | _ | |a Workflows for the acquisition and analysis of data in the natural sciences exhibit a growing degree of complexity and heterogeneity, are increasingly performed in large collaborative efforts, and often require the use of high-performance computing (HPC). Here, we explore the reasons for these new challenges and demands and discuss their impact with a focus on the scientific domain of computational neuroscience. We argue for the need of software platforms integrating HPC systems that allow scientists to construct, comprehend and execute workflows composed of diverse data generation and processing steps using different tools. As a use case we present a concrete implementation of such a complex workflow, covering diverse topics such as HPC-based simulation using the NEST software, access to the SpiNNaker neuromorphic hardware platform, complex data analysis using the Elephant library, and interactive visualization methods for facilitating further analysis. Tools are embedded into a web-based software platform under development by the Human Brain Project, called the Collaboratory. On the basis of this implementation, we discuss the state of the art and future challenges in constructing large, collaborative workflows with access to HPC resources. |
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