CAD2122 Project 1 Solved

mage filters, like the blur filter used in lab 3, are examples of matrix processing that can be taken to the GPU. Consider two classes of filters: area filters, that considers an area around each pixel to define the final value of that pixel; and the point filters, that applies some function on the pixel value. For this project you need to implement a sequence of two filters with some parameters, that could be applied to any given image.  

For the area filter, considering just the direct neighbors, we get a 3x3 grid of pixels with the original pixel at its center.  

  

The final value for that pixel will be the weighted average defined by a 3x3 matrix of coefficients. The following examples of coefficients allows the implementation of several types of filters:

  

For the point filter, we pretend to gray the image using the following expression:

(r,g,b) = alpha*(c,c,c) + (1-alpha)(r,g,b),  where c = 0.3 r + 0.59 g + 0.11 b Alfa is a value in [0 .. 1] that defines the color shift to grayscale.  

 

A sequential code example is provided for reference. The main objective is to achieve the best performance, particularly for big images. For convenience, this work is presented in several stages (number 5 is mandatory):

 

1.       (30%) Implement a CUDA or OpenCL solution that  

a.       Parallelizes both filter operations

Suggestion: experiment with different parallel strategies. Examples: just one kernel with both filters vs. one kernel per filter.

b.       Experiment with different grid layouts and block sizes.

Study if using a local shared area can improve your kernel(s) performance (nvprof and section 8 of CUDA Best Practices Guide[1] may help you evaluate any improvement).  

 

2.       (20%) Evaluate these solutions against

a.       The sequential version

b.       Using different grid arrangements and thread block sizes

c.       Using/not using shared memory  

Note: ignore file I/O times but include in your timings memcopy to/from device.

                 

 

  

3.       (15%) Complement your solution with the ability to overlap computation with communication by partitioning the image space to process into a given number of partitions (NP). Assigning each of such partitions to a dedicated stream. Consider the following example with NP=3.

 

 

 

While kernel(s) is(are) being applied over Partition 2, Partition 3 can be simultaneously uploaded to the GPU and, possibly, the result of processing Partition 1 can be simultaneously downloaded from the GPU.

 

4.       (15%) Evaluate this solution against the others.  

 

5.       (20%) Write a report (max of 5 pages A4 11pt font) that presents

a.       Tested approaches and final solution

b.       Relevant implementation details

c.       Your evaluation results (include times and/or graphs to compare and justify your solution)

d.       An analysis and interpretation of these results


 
[1] https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#performance-metrics