PPROG-Assignment VI: OpenCL Programming Solved

The purpose of this assignment is to familiarize yourself with OpenCL programming.

Get the source code:

$ cd <your_workplace>
$ wget https://nycu-sslab.github.io/PP-f21/HW6/HW6.zip
$ unzip HW6.zip -d HW6

1. Image convolution using OpenCL
Convolution is a common operation in image processing. It is used for blurring, sharpening, embossing, edge detection, and more. The image convolution process is accomplished by doing a convolution between a small matrix (which is called a filter kernel in image processing) and an image. You may learn more about the convolution process at Wikipedia: Convolution.

Figure 1 shows an illustration of the concept of applying a convolution filter to a specific pixel, value of which is 3. After the convolution process, the value of the pixel becomes 7—how the resulting value is computed is illustrated on the right of the figure.

 

Figure 1. Applying a convolution filter to the dark gray pixel of the source image (value of which is 3).

In this assignment, you will need to implement a GPU kernel function for convolution in OpenCL by using the zero-padding method. A serial implementation of convolution can be found in serialConv() in serialConv.c. You can refer to the implementation to port it to OpenCL. You may refer to this article to learn about the zero-padding method. Figure 2 shows an example of applying the zero-padding method to the source image (on the left) and thereby resulting a same-size, filtered output image (on the right).

 

Figure 2. Applying the zero-padding method to a source image.

Your job is to parallelize the computation of the convolution using OpenCL. A starter code that spawns OpenCL threads is provided in function hostFE(), which is located in hostFE.c. hostFE() is the host front-end function that allocates memories and launches a GPU kernel, called convolution(), which is located in kernel.cl.

Currently hostFE() and convolution() do not do any computation and return immediately. You should complete these two functions to accomplish this assignment.

You can build the program by typing make, and run the program via ./conv. Your program should read an input image from input.bmp, perform image convolution, and output the result image into output.bmp.

You can use the following command to test your own image:

ffmpeg -i output.bmp(or your own image file) -pix_fmt gray -vf scale=600:400 destination.bmp

You can use a different filter kernel by adding option -f N when running the program (i.e., ./conv -f N), where N is either 1 (by default), 2, or 3, and indicates which filter kernel is used. Each filter kernel is defined in a CSV file (filter1.csv, filter2.csv, or filter3.csv). The first line of the CSV file defines the width (or height) of the filter kernel, and the remaining lines define the values of the filter kernel.

2. Requirements
You will modify only hostFE.c and kernel.cl.

Q1 (5 points): Explain your implementation. How do you optimize the performance of convolution?

[Bonus] Q2 (10 points): Rewrite the program using CUDA. (1) Explain your CUDA implementation, (2) plot a chart to show the performance difference between using OpenCL and CUDA, and (3) explain the result.

Answer the questions marked with Q1 (and Q2) in a REPORT using HackMD. Notice that in this assignment a higher standard will be applied when grading the quality of your report.

Note: You cannot print any message in your program.

Evaluation Platform
Your program should be able to run on UNIX-like OS platforms. We will evaluate your programs on the workstations dedicated for this course. You can access these workstations by ssh with the following information.

The workstations are based on Ubuntu 18.04 with Intel(R) Core(TM) i5-7500 CPU @ 3.40GHz processors and GeForce GTX 1060 6GB. g++-10, clang++-11, cuda10.1, and OpenCL2.1 have been installed.

IP
Port
User Name
Password
140.113.215.195
37072 ~ 37080
{student_id}
{Provided by TA}
Login example:

$ ssh <student_id>@140.113.215.195 -p <port>

You can use the testing script test_hw6 to check your answer for reference only. Run test_hw6 in a directory that contains your HW6_XXXXXXX.zip file on the workstation.

References
NVIDIA OpenCL SDK Code Samples
Getting started with OpenCL and GPU Computing