Assignment 2 Accelerated Image Denoising

COMP 429/529: Assignment 2


Note that this assignment will require a considerable amount of coding. So start early.
Accelerated Image Denoising
In this assignment, you will work on a noise reduction algorithm that removes noise from an
image. The image denoising algorithm we are using is very simple compared to the block
matching or Bilateral filters. The algorithm is a diffusion method typically used for ultrasonic and radar imaging applications based on partial differential equations (PDEs). It is
used to remove locally correlated noise, known as speckles, without destroying important
image features. The program consists of three stages in continuous iterations over the image (reduction/statistics, computation 1 and computation 2). The sequential dependency
between all of these stages requires synchronization after each stage (because each stage
operates on the entire image).
Serial Code
You are provided with the serial version of image denoising algorithm. The code has various
options as follows:
1 ./noise_remover
2
3 With the arguments
4 -i Input image to be denoised
5 -iter Number of iterations
6 -l lambda which is the step size
7 -o Output denoised image
8 Example command line
9 ./noise_remover -i coffee.pmg -iter 100 -o denoised_coffee.png
You will parallelise the provided serial algorithm using CUDA. Starting with the serial implementation, the assignment requires 3 versions of the same code:
• Version 1: Parallelize the algorithm using a single GPU. First implement a naive version
that makes all the references through global memory. Make sure that your naive version
1
COMP 429/529 ( Spring 2020): Assignment 2 ENVIRONMENT
works correctly before you implement the other options. Check if all the data allocations
on the GPU, data transfers and synchronisations are implemented correctly.
• Version 2: Use temporary variables (in registers) to eliminate global memory references
for arrays in the calculation. Take advantage of the data reuse.
• Version 3: Then develop a CUDA implementation by using shared memory (on-chip
memory) on the GPU by bringing a 1D or 2D block into shared memory and sharing
it with multiple threads in the same thread block. Perform this optimisation only to
Compute 2 loop for grading (You can apply it to the others if you would like).
• You should implement these versions on top of each other (e.g. Version 3 should be
implemented on top of Version 2).
• For details on how to implement these optimizations, refer to Lectures on GPU memory
optimizations.
• Note that these optimisations do not guarantee performance improvement. Implement
them and observe how they affect the performance.
• Optimize the code as much as you can. You can use techniques not mentioned here or
not covered in the class. Document each of the optimizations in your report.
For full credit, you have to implement all these versions and submit the relevant codes. You
can refer to the Nvidia Best Practices which discuss other types of optimisations:
http://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#abstract
Validation
Your parallel implementations should give the same result as the serial implementation. It
might be difficult to inspect the images visually to check if the results are the same. The
current program takes the average sum of the pixels in the final images. Use this number
reported in the standard output as a way to check the program correctness. If the average
does not match with the serial version’s, then something in your program is probably wrong.
Environment
We will be using the KUACC cluster for this assignment.
• You need to add gres parameter to be able to use GPUs. You can
refer to the example jobscript at /kuacc/jobscripts/mumax3/mumax3 submit.sh
• or on the web https://docs.computecanada.ca/wiki/Using GPUs with Slurm
• For interactive GPU usage, you can use the following commands:
Student Name: Page 2 of ??
COMP 429/529 ( Spring 2020): Assignment 2 SUBMISSION
1 srun -A users -p short -n1 --gres=gpu:1 --qos=users --pty $SHELL
2
3 srun -A users -p short -n1 --gres=gpu:gtx_1080ti:1 --qos=users --pty $SHELL
• For batch jobs, you need to add the following parameter into your scripts
1 #SBATCH --gres=gpu:tesla_k20m:1
2 or
3 #SBATCH --gres=gpu:1
4 #SBATCH constraint=tesla_k20m
• If you have problems compiling or running jobs on KUACC, first check the website
provided by the KU IT. If you cannot find the solution there, you can always post a
question on Blackboard.
• Don’t leave the experiments on KUACC to the last minutes of the deadline as the
machine gets busy time to time. Note that there are many other people on campus
using the cluster.
Reporting Performance
• Use the image files provided with the assignment for testing. Report the performance
for the coffee.pgm image for iter=100.
• We have provided you the code to measure the Gflop/s rates. Note that at each iteration,
for each data point, we perform about 45 floating point operations (divisions, multiplies
and adds). Then you can compute the flop rates as follows:
1 int num_flops_per_point = 45 ; // see the exact formula in the code, it is not exactly 45
2
3 double flop_rate = num_iterations *
4 (1E-9 * grid_rows * grid_cols * num_flops_per_point) / total_time;
• Tune the block size for your implementation and draw a performance chart for various
block sizes for one of the images.
• Compare the performance of your best implementation with the CPU version (serial).
• Compare the performance of different CUDA versions.
• Document these in your report.
Submission
• Submit all three versions of the code, label them properly. If you have another version
that implements different optimizations, submit that as well.
Student Name: Page 3 of ??
Grading COMP 429/529 ( Spring 2020): Assignment 2 SUBMISSION
• The first paragraph of your report should clearly state which versions work properly
and which version is the best performant one.
• In the first paragraph explicitly state the Gflops rate you achieve for coffee.pgm and
iter=100.
• Document your work in a well-written report which discusses your findings. Offer insight
into your results and plots.
• Submit both the report and source codes electronically through blackboard.
• Please create a parent folder named after your username(s). Your parent folder should
include a report in pdf and a subdirectory for the sources. Include all the necessary files
to compile your code. Be sure to delete all object and executable files before creating a
zip file.
Grading
Your grade will depend on 3 factors: performance, correctness and the depth and your explanations of observed performance in your report.
Implementation (80 points): Version 1 (30 pts), Version 2 (20 pts each), Version 3 (30 points)
Report (20 points): implementation description and any tuning/optimisation you performed
(10 pts), performance study (10 pts).