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The substantial parallel processing resources in modern GPUs makes them a natural choice for implementing vision-processing functions.

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This chapter describes how to re-use the code from this sample, the limitations of the test method, and a method of analyzing the results.

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This chapter describes the requirements needed to run this sample and the example hardware that produces the results in this guide.

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This chapter describes the conclusions from the optimization process.

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This chapter describes the performance of some common 3 x 3 convolution matrices using the fully optimized code.

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This chapter describes variations to the algorithm for 3 x 3 convolution matrices to create an algorithm for 5 x 5 convolution matrices.

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This chapter describes describes some general concepts to consider when optimizing kernels.

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This chapter describes describes some useful optimization methods, the logic for them, and the results they provide on the test platform.

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This chapter describes some initial (as well as the simplest and most intuitive) implementations of convolution algorithms.

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Convolution operations are important in image processing, particularly in filtering. GPU compute can improve performance significantly.

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