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Proceedings of the 16th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 25

The impact of vectorization and parallelization of the slope algorithm on performance and energy efficiency on multi-core architecture

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DOI: http://dx.doi.org/10.15439/2021F68

Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 283290 ()

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Abstract. Spatial raster data are a valuable resource in various domains. However, processing growing volumes of such data is a challenge in terms of performance and energy efficiency trade-off. Neighborhood raster algorithms are an important part of many geospatial analyses. In our work, we concentrate on vectorization and parallelization to improve the performance of the slope calculation algorithm.  We study the impact of these optimizations on energy efficiency using RAPL. We tested our vector-parallel implementations on a multi-core computer for various data sizes. The results showed that optimization towards high performance can also be an effective strategy for improving energy efficiency.


  1. B. Bylina and J. Bylina. Studying OpenMP thread mapping for parallel linear algebra kernels on multicore system. Bulletin of the Polish Academy of Sciences, 66(6):981–990, 2018.
  2. J. Dongarra, H. Ltaief, P. Luszczek, and V. M. Weaver. Energy footprint of advanced dense numerical linear algebra using tile algorithms on multicore architectures. In 2012 Second International Conference on Cloud and Green Computing, pages 274–281, 2012.
  3. D. Hackenberg, R. Schöne, T. Ilsche, D. Molka, J. Schuchart, and R. Geyer. An energy efficiency feature survey of the Intel Haswell processor. In 2015 IEEE International Parallel and Distributed Processing Symposium Workshop, pages 896–904, 2015.
  4. T. Hengl and H.I. Reuter, editors. Geomorphometry: Concepts, Software, Applications, volume 33. Elsevier, Amsterdam, 2008.
  5. B. K. P. Horn. Hill shading and the reflectance map. Proceedings of the IEEE, 69(1):14–47, Jan 1981.
  6. T. Jakobs, B. Naumann, and G. Rünger. Performance and energy consumption of the SIMD Gram–Schmidt process for vector orthogonalization. The Journal of Supercomputing, 76:1999–2021, 2019.
  7. T. Jakobs and G. Rünger. Examining energy efficiency of vectorization techniques using a Gaussian elimination. In 2018 International Conference on High Performance Computing Simulation (HPCS), pages 268–275, 2018.
  8. T. Jakobs and G. Rünger. On the energy consumption of load/store AVX instructions. In 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), pages 319–327, 2018.
  9. K. Khan, M. Hirki, T. Niemi, J. Nurminen, and Z. Ou. RAPL in action: Experiences in using RAPL for power measurements. ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS), 3, 01 2018.
  10. S.D. Peckham. Chapter 25 Geomorphometry and Spatial Hydrologic Modelling. In Tomislav Hengl and Hannes I. Reuter, editors, Geomorphometry, volume 33 of Developments in Soil Science, pages 579 – 602. Elsevier, 2009.
  11. L. Szustak, R. Wyrzykowski, T. Olas, and V. Mele. Correlation of performance optimizations and energy consumption for stencil-based application on Intel Xeon scalable processors. IEEE Transactions on Parallel and Distributed Systems, 31(11):2582–2593, 2020.
  12. J. Tang, P. Pilesjö, and A. Persson. Estimating slope from raster data – a test of eight algorithms at different resolutions in flat and steep terrain. Geodesy and Cartography, 39(2):41–52, 2013.
  13. S. Warren, M. Hohmann, K. Auerswald, and H. Mitasova. An evaluation of methods to determine slope using digital elevation data. Catena, pages 215–233, 12 2004.
  14. M. E. Wolf and M. S. Lam. A loop transformation theory and an algorithm to maximize parallelism. IEEE Transactions on Parallel and Distributed Systems, 2(4):452–471, 1991.