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1 June 2020 Halide and OpenMP for generating high-performance recursive filters
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Proceedings Volume 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020; 1151532 (2020)
Event: International Workshop on Advanced Imaging Technologies 2020 (IWAIT 2020), 2020, Yogyakarta, Indonesia
Halide is a domain-specific language for image processing. Halide can separate a programming code into an algorithm part and a scheduling part. We can write how to work image processing in the algorithm part, and how to compute it in the scheduling part. The scheduling part has a restriction, which does not change the calculation result. The restriction prevents efficient code generation for some kinds of image processing. In this paper, we propose high-performance recursive filters with Halide and OpenMP. The recursive filter is one of the difficult algorithms for optimizing code with Halide. In our implementation, we divided an input image into multiple tiles, and then each tile is processed with Halide code. Also, each processing for a tile in Halide is parallelized by OpenMP. The processing has an approximated computation in boundary conditions for forcefully cutting the effect from the next tiles; thus, the resulting image has slightly degraded. The closed tile, however, improves the cache efficiency in computation. In the experiment, the processing time of the box image filtering with and without tiling was compared. The box image filtering is the most simple recursive filter. The box image filtering uses an integral image technique to process in constant time, and its calculation includes recursive filters. Code with tiling, which was the proposed method, the tile size is 128×128. The experimental results showed that the proposed method had a better computational time performance than the code without tiling.
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Yuta Tsuji and Norishige Fukushima "Halide and OpenMP for generating high-performance recursive filters", Proc. SPIE 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020, 1151532 (1 June 2020);


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