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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2209.00596 |
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| _version_ | 1866929690546012160 |
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| author | Abarenkov, Kessy Fouilloux, Anne Neukirchen, Helmut Azab, Abdulrahman |
| author_facet | Abarenkov, Kessy Fouilloux, Anne Neukirchen, Helmut Azab, Abdulrahman |
| contents | To reproduce eScience, several challenges need to be solved: scientific workflows need to be automated; the involved software versions need to be provided in an unambiguous way; input data needs to be easily accessible; High-Performance Computing (HPC) clusters are often involved and to achieve bit-to-bit reproducibility, it might be even necessary to execute the code on a particular cluster to avoid differences caused by different HPC platforms (and unless this is a scientist's local cluster, it needs to be accessed across (administrative) borders). Preferably, to allow even inexperienced users to (re-)produce results, all should be user-friendly. While some easy-to-use web-based scientific portals support already to access HPC resources, this typically only refers to computing and data resources that are local. By the example of two community-specific portals in the fields of biodiversity and climate research, we present a solution for accessing remote HPC (and cloud) compute and data resources from scientific portals across borders, involving rigorous container-based packaging of the software version and setup automation, thus enhancing reproducibility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_00596 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Reproducible Cross-border High Performance Computing for Scientific Portals Abarenkov, Kessy Fouilloux, Anne Neukirchen, Helmut Azab, Abdulrahman Distributed, Parallel, and Cluster Computing To reproduce eScience, several challenges need to be solved: scientific workflows need to be automated; the involved software versions need to be provided in an unambiguous way; input data needs to be easily accessible; High-Performance Computing (HPC) clusters are often involved and to achieve bit-to-bit reproducibility, it might be even necessary to execute the code on a particular cluster to avoid differences caused by different HPC platforms (and unless this is a scientist's local cluster, it needs to be accessed across (administrative) borders). Preferably, to allow even inexperienced users to (re-)produce results, all should be user-friendly. While some easy-to-use web-based scientific portals support already to access HPC resources, this typically only refers to computing and data resources that are local. By the example of two community-specific portals in the fields of biodiversity and climate research, we present a solution for accessing remote HPC (and cloud) compute and data resources from scientific portals across borders, involving rigorous container-based packaging of the software version and setup automation, thus enhancing reproducibility. |
| title | Reproducible Cross-border High Performance Computing for Scientific Portals |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2209.00596 |