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Parallelizing Map Projection of Raster Data on Multi-core CPU and GPU Parallel Programming Frameworks
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Parallellisering av kartprojektion av rasterdata på flerkärniga CPU- och  GPU-programmeringsramverk (Swedish)
Abstract [en]

Map projections lie at the core of geographic information systems and numerous projections are used today. The reprojection between different map projections is recurring in a geographic information system and it can be parallelized with multi-core CPUs and GPUs. This thesis implements a parallel analytic reprojection algorithm of raster data in C/C++ with the parallel programming frameworks Pthreads, C++11 STL threads, OpenMP, Intel TBB, CUDA and OpenCL.

The thesis compares the execution times from the different implementations on small, medium and large raster data sets, where OpenMP had the best speedup of 6, 6.2 and 5.5, respectively. Meanwhile, the GPU implementations were 293 % faster than the fastest CPU implementations, where profiling shows that the CPU implementations spend most time on trigonometry functions. The results show that reprojection algorithm is well suited for the GPU, while OpenMP and Intel TBB are the fastest of the CPU frameworks.

Abstract [sv]

Kartprojektioner är en central del av geografiska informationssystem och en otalig mängd av kartprojektioner används idag. Omprojiceringen mellan olika kartprojektioner sker regelbundet i ett geografiskt informationssystem och den kan parallelliseras med flerkärniga CPU:er och GPU:er. Denna masteruppsats implementerar en parallel och analytisk omprojicering av rasterdata i C/C++ med ramverken Pthreads, C++11 STL threads, OpenMP, Intel TBB, CUDA och OpenCL.

Uppsatsen jämför de olika implementationernas exekveringstider på tre rasterdata av varierande storlek, där OpenMP hade bäst speedup på 6, 6.2 och 5.5. GPU-implementationerna var 293 % snabbare än de snabbaste CPU-implementationerna, där profileringen visar att de senare spenderade mest tid på trigonometriska funktioner. Resultaten visar att GPU:n är bäst lämpad för omprojicering av rasterdata, medan OpenMP är den snabbaste inom CPU ramverken.

Place, publisher, year, edition, pages
2016.
Keyword [en]
map projection, reprojection, raster, gpu
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-190883OAI: oai:DiVA.org:kth-190883DiVA: diva2:953533
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2016-08-18 Created: 2016-08-17 Last updated: 2016-08-18Bibliographically approved

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