Parallelizing Map Projection of Raster Data on Multi-core CPU and GPU Parallel Programming Frameworks

Daniel Chavez
KTH Computer Science and Communication
KTH Computer Science and Communication, 2016


   title={Parallelizing Map Projection of Raster Data on Multi-core CPU and GPU Parallel Programming Frameworks},

   author={Chavez, Daniel},



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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.
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