project directories reorganization
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54
benchmarks/old_opencl/convolution/kernel.cl
Executable file
54
benchmarks/old_opencl/convolution/kernel.cl
Executable file
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__kernel
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void convolution(
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__read_only image2d_t sourceImage,
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__write_only image2d_t outputImage,
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int rows,
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int cols,
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__constant float* filter,
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int filterWidth,
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sampler_t sampler)
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{
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// Store each work-item’s unique row and column
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int column = get_global_id(0);
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int row = get_global_id(1);
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// Half the width of the filter is needed for indexing
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// memory later
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int halfWidth = (int)(filterWidth/2);
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// All accesses to images return data as four-element vector
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// (i.e., float4), although only the 'x' component will contain
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// meaningful data in this code
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float4 sum = {0.0f, 0.0f, 0.0f, 0.0f};
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// Iterator for the filter
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int filterIdx = 0;
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// Each work-item iterates around its local area based on the
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// size of the filter
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int2 coords; // Coordinates for accessing the image
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// Iterate the filter rows
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for(int i = -halfWidth; i <= halfWidth; i++) {
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coords.y = row + i;
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// Iterate over the filter columns
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for(int j = -halfWidth; j <= halfWidth; j++) {
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coords.x = column + j;
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float4 pixel;
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// Read a pixel from the image. A single channel image
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// stores the pixel in the 'x' coordinate of the returned
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// vector.
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pixel = read_imagef(sourceImage, sampler, coords);
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sum.x += pixel.x * filter[filterIdx++];
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}
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}
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// Copy the data to the output image if the
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// work-item is in bounds
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if(row < rows && column < cols) {
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coords.x = column;
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coords.y = row;
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write_imagef(outputImage, coords, sum);
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}
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}
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