# Parallel Processing Strategies for Geospatial Data in a Cloud Computing Infrastructure
## 4 Abstracting Platform-Specific Dependencies with openEO
Speedup obtained via tiling without multi-threading is shown in gray (32 cores). an interesting study for future work. The automatic allocation of the optimal memory and computing resources for the specific workflow at hand and adding stress tests with many users competing for the same computing resources are other potential directions for further research. Conceptualization, P.K. ; methodology, P.K., T.K. and L.M. ; software, P.K., T.K. and L.M. ; writing--original draft preparation, P.K. ; review and editing, all; supervision, P.S. All authors have read and agreed to the published version of the manuscript. The openEO project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 776242. This publication only reflects the author's views; the European Commission is not responsible for any use that may be made of the information it contains. Not applicable. The openEO API is freely available online: [[https://github.com/OpenEO](https://github.com/OpenEO)]([https://github.com/OpenEO](https://github.com/OpenEO)) (accessed on 20 October 2021). Source code of pyjoe is freely available online [[https://github.com/ec-jrc/jeolib-pyjoe](https://github.com/ec-jrc/jeolib-pyjoe)]([https://github.com/ec-jrc/jeolib-pyjoe](https://github.com/ec-jrc/jeolib-pyjoe)) (accessed on 20 October 2021). Data and results are available on request. The authors declare no conflict of interest. ## References

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