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- Edlund, Åke, et al.
(författare)
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Practical Cloud Evaluation from a Nordic eScience User perspective
- 2011
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Ingår i: Proceedings of the 5th international workshop on Virtualization technologies in distributed computing. - New York, NY, USA : ACM.
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Konferensbidrag (refereegranskat)abstract
- In this paper, we describe the findings of the NEON project - a cross-Nordic - Sweden, Norway, Denmark, Finland and Iceland - project evaluating the usefulness of private versus public cloud services for HPC users. Our findings are briefly that private cloud technology is not mature enough yet to provide a transparent user experience. It is expected that this will be the case mid 2012. The cost efficiency of both public and private cloud should be continuously monitored as there is a strong downward trend. This conclusion is supported by NEON experimenting as well as larger initiatives e.g. StratusLab reports. Public cloud technology is mature enough but lacks certain features that will be necessary to include cloud resources in a transparent manner in a national infrastructure like the Norwegian NOTUR (www.notur.no) case, e.g. with respect to quota management. These features are expected to emerge in 2011 via third party management software and in the best of breed public cloud services. Public clouds are competitive in the low end for non-HPC jobs (low memory, low number of cores) on price. A significant fraction (ca. 20%) of the jobs running on the current Nordic supercomputer infrastructure are potentially suitable for cloud-like technology. This holds in particular for singlethreaded or single-node jobs with small/medium memory requirements and non-intensive I/O. There is a backlog of real supercomputer jobs that suffers from the non-HPC jobs on the supercomputer infrastructure. Off-loading these non-HPC jobs to a public cloud would effectively add supercomputing capacity. Another finding is that available storage capacity is not accessible in a user-friendly way; most storage clouds are only accessible via programmable interfaces. A number of experiments and piloting are presented to support these claims.
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| 2. |
- Memon, Shahbaz, et al.
(författare)
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Scientific workflows applied to the coupling of a continuum (Elmer v8.3) and a discrete element (HiDEM v1.0) ice dynamic model
- 2019
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Ingår i: Geoscientific Model Development. - : COPERNICUS GESELLSCHAFT MBH. - 1991-959X .- 1991-9603. ; 12:7, s. 3001-3015
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Tidskriftsartikel (refereegranskat)abstract
- Scientific computing applications involving complex simulations and data-intensive processing are often composed of multiple tasks forming a workflow of computing jobs. Scientific communities running such applications on computing resources often find it cumbersome to manage and monitor the execution of these tasks and their associated data. These workflow implementations usually add overhead by introducing unnecessary input/output (I/O) for coupling the models and can lead to sub-optimal CPU utilization. Furthermore, running these workflow implementations in different environments requires significant adaptation efforts, which can hinder the reproducibility of the underlying science. High-level scientific workflow management systems (WMS) can be used to automate and simplify complex task structures by providing tooling for the composition and execution of workflows - even across distributed and heterogeneous computing environments. The WMS approach allows users to focus on the underlying high-level workflow and avoid low-level pitfalls that would lead to non-optimal resource usage while still allowing the workflow to remain portable between different computing environments. As a case study, we apply the UNICORE workflow management system to enable the coupling of a glacier flow model and calving model which contain many tasks and dependencies, ranging from pre-processing and data management to repetitive executions in heterogeneous high-performance computing (HPC) resource environments. Using the UNICORE workflow management system, the composition, management, and execution of the glacier modelling workflow becomes easier with respect to usage, monitoring, maintenance, reusability, portability, and reproducibility in different environments and by different user groups. Last but not least, the workflow helps to speed the runs up by reducing model coupling I/O overhead and it optimizes CPU utilization by avoiding idle CPU cores and running the models in a distributed way on the HPC cluster that best fits the characteristics of each model.
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