Abstract

Abstract. Thanks to the nature of the graphics processing, the newly released products offer highly parallel processing units with high-memory bandwidth and computational power of more than teraflops per second. The modern GPUs are not only powerful graphic engines but also they are high level parallel programmable processors with very fast computing capabilities and high-memory bandwidth speed compared to central processing units (CPU). Data-parallel computations can be shortly described as mapping data elements to parallel processing threads. The rapid development of GPUs programmability and capabilities attracted the attentions of researchers dealing with complex problems which need high level calculations. This interest has revealed the concepts of “General Purpose Computation on Graphics Processing Units (GPGPU)” and “stream processing”. The graphic processors are powerful hardware which is really cheap and affordable. So the graphic processors became an alternative to computer processors. The graphic chips which were standard application hardware have been transformed into modern, powerful and programmable processors to meet the overall needs. Especially in recent years, the phenomenon of the usage of graphics processing units in general purpose computation has led the researchers and developers to this point. The biggest problem is that the graphics processing units use different programming models unlike current programming methods. Therefore, an efficient GPU programming requires re-coding of the current program algorithm by considering the limitations and the structure of the graphics hardware. Currently, multi-core processors can not be programmed by using traditional programming methods. Event procedure programming method can not be used for programming the multi-core processors. GPUs are especially effective in finding solution for repetition of the computing steps for many data elements when high accuracy is needed. Thus, it provides the computing process more quickly and accurately. Compared to the GPUs, CPUs which perform just one computing in a time according to the flow control are slower in performance. This structure can be evaluated for various applications of computer technology. In this study covers how general purpose parallel programming and computational power of the GPUs can be used in photogrammetric applications especially direct georeferencing. The direct georeferencing algorithm is coded by using GPGPU method and CUDA (Compute Unified Device Architecture) programming language. Results provided by this method were compared with the traditional CPU programming. In the other application the projective rectification is coded by using GPGPU method and CUDA programming language. Sample images of various sizes, as compared to the results of the program were evaluated. GPGPU method can be used especially in repetition of same computations on highly dense data, thus finding the solution quickly.

Highlights

  • The graphic processing units (GPU) on the graphic cards integral parts of computers are really developed today according to the last ten years

  • The modern GPUs are became powerful graphic engines and they are high level parallel programmable processors with very fast computing capabilities and high memory bandwidth speed comparing to central processing units (CPU)

  • GPU-based non-graphics computation is well suited to data-parallel tasks such as image processing kernels and matrix operations, it is possible to accelerate many other applications by adapting existing algorithms to the general purpose parallel programming (Yılmaz, 2010)

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Summary

INTRODUCTION

The graphic processing units (GPU) on the graphic cards integral parts of computers are really developed today according to the last ten years. The rapid development of GPUs programmability and capabilities attracted the researchers dealing with complex problems who need highly level calculation. This interest has revealed the concepts of “General Purpose Computation on Graphics Processing Units (GPGPU)” and “stream processing”. The announcements of immense computational speedups and fascinating developments in GPU hardware inspired to use general purpose parallel processing in image processing. GPU-based non-graphics computation is well suited to data-parallel tasks such as image processing kernels and matrix operations, it is possible to accelerate many other applications by adapting existing algorithms to the general purpose parallel programming (Yılmaz, 2010).

GPGPU AND STREAM PROCESSING
PROJECTIVE RECTIFICATION WITH CUDA
DIRECT GEOREFERENCING WITH CUDA
RESULTS
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