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ECE408 Objective Solution
The lab's objective is to implement a tiled image convolution using both shared and constant memory as discussed in class. Like what's discussed in class, we will have a constant 5x5 convolution mask, but will have arbitrarily sized image (We will assume the image dimensions are greater than 5x5 in this Lab).
Convolution is used in many fields, such as image processing where it is used for image filtering. A standard image convolution formula for a 5x5 convolution filter M with an Image I is:
where P_{i,j,c} is the output pixel at position i,j in channel c, I_{i,j,c} is the input pixel at i,j in channel c (the number of channels will always be 3 for this MP corresponding to the RGB values), and M_{x,y} is the mask at position x,y.
Prerequisites
Before starting this lab, make sure that:
Input Data
The input is an interleaved image of height x width x channels. By interleaved, we mean that the the element I[y][x] contains three values representing the RGB channels. This means that to index a particular element’s value, you will have to do something like:
index = (yIndex*width + xIndex)*channels + channelIndex;
For this assignment, the channel index is 0 for R, 1 for G, and 2 for B. So, to access the G value of I[y][x], you should use the linearized expression I[(yIndex*width+xIndex)*channels + 1].
For simplicity, you can assume that channels is always set to 3.
Instruction
Edit the code in the code tab to perform the following:
allocate device memory copy host memory to device initialize thread block and kernel grid dimensions
invoke CUDA kernel copy results from device to host deallocate device memory
implement the tiled 2D convolution kernel with adjustments for channels
use shared memory to reduce the number of global accesses, handle the boundary conditions in when loading input list elements into the shared memory
Instructions about where to place each part of the code is demarcated by the //@@ comment lines.
Pseudo Code
Your sequential pseudo code would look something like
maskWidth := 5
maskRadius := maskWidth/2 # this is integer division, so the result is 2 for i from 0 to height do for j from 0 to width do for k from 0 to channels accum := 0
for y from -maskRadius to maskRadius do for x from -maskRadius to maskRadius do xOffset := j + x yOffset := i + y
if xOffset >= 0 && xOffset < width && yOffset >= 0 && yOffset < height then
imagePixel := I[(yOffset * width + xOffset) * channels + k] maskValue := K[(y+maskRadius)*maskWidth+x+maskRadius] accum += imagePixel * maskValue end end end
# pixels are in the range of 0 to 1
P[(i * width + j)*channels + k] = clamp(accum, 0, 1) end end end
where clamp is defined as
def clamp(x, start, end) return min(max(x, start), end) end
Input Format
For people who are developing on their own system. The images are stored in PPM (P6) format, this means that you can (if you want) create your own input images. The easiest way to create image is via external tools. You can use tools such as bmptoppm. The masks are stored in a CSV format. Since the input is small, it is best to edit it by hand.
Convolution is used in many fields, such as image processing where it is used for image filtering. A standard image convolution formula for a 5x5 convolution filter M with an Image I is:
where P_{i,j,c} is the output pixel at position i,j in channel c, I_{i,j,c} is the input pixel at i,j in channel c (the number of channels will always be 3 for this MP corresponding to the RGB values), and M_{x,y} is the mask at position x,y.
Prerequisites
Before starting this lab, make sure that:
Input Data
The input is an interleaved image of height x width x channels. By interleaved, we mean that the the element I[y][x] contains three values representing the RGB channels. This means that to index a particular element’s value, you will have to do something like:
index = (yIndex*width + xIndex)*channels + channelIndex;
For this assignment, the channel index is 0 for R, 1 for G, and 2 for B. So, to access the G value of I[y][x], you should use the linearized expression I[(yIndex*width+xIndex)*channels + 1].
For simplicity, you can assume that channels is always set to 3.
Instruction
Edit the code in the code tab to perform the following:
allocate device memory copy host memory to device initialize thread block and kernel grid dimensions
invoke CUDA kernel copy results from device to host deallocate device memory
implement the tiled 2D convolution kernel with adjustments for channels
use shared memory to reduce the number of global accesses, handle the boundary conditions in when loading input list elements into the shared memory
Instructions about where to place each part of the code is demarcated by the //@@ comment lines.
Pseudo Code
Your sequential pseudo code would look something like
maskWidth := 5
maskRadius := maskWidth/2 # this is integer division, so the result is 2 for i from 0 to height do for j from 0 to width do for k from 0 to channels accum := 0
for y from -maskRadius to maskRadius do for x from -maskRadius to maskRadius do xOffset := j + x yOffset := i + y
if xOffset >= 0 && xOffset < width && yOffset >= 0 && yOffset < height then
imagePixel := I[(yOffset * width + xOffset) * channels + k] maskValue := K[(y+maskRadius)*maskWidth+x+maskRadius] accum += imagePixel * maskValue end end end
# pixels are in the range of 0 to 1
P[(i * width + j)*channels + k] = clamp(accum, 0, 1) end end end
where clamp is defined as
def clamp(x, start, end) return min(max(x, start), end) end
Input Format
For people who are developing on their own system. The images are stored in PPM (P6) format, this means that you can (if you want) create your own input images. The easiest way to create image is via external tools. You can use tools such as bmptoppm. The masks are stored in a CSV format. Since the input is small, it is best to edit it by hand.
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