Gaussian Blur through Parallel Computing
Nahla M. Ibrahim, Ahmed Abou ElFarag and Rania Kadry
Department of Computer Engineering, Arab Academy for Science and Technology and Maritime Transport,
Alexandria, Egypt
Keywords: CUDA, Parallel Computing, Image Convolution, Gaussian Blur, Google Colaboratory.
Abstract: Two dimensional 2D convolution is one of the most complex calculations and memory intensive algorithms
used in image processing. In our paper, we present the 2D convolution algorithm used in the Gaussian blur
which is a filter widely used for noise reduction and has high computational requirements. Since, single
threaded solutions cannot keep up with the performance and speed needed for image processing techniques.
Therefore, parallelizing the image convolution on parallel systems enhances the performance and reduces the
processing time. This paper aims to give an overview on the performance enhancement of the parallel systems
on image convolution using Gaussian blur algorithm. We compare the speed up of the algorithm on two
parallel systems: multi-core central processing unit CPU and graphics processing unit GPU using Google
Colaboratory or “colab”.
1 INTRODUCTION
Parallel computer systems popularity is highly
increasing for their ability to solve complex problems
and to deal with the significant increase in the data
sizes and huge data sets (Bozkurt et al, 2015). Image
processing has moved from the sequential approach
to the parallel programming approach as the image
processing applications such as image filtering and
convolution consume time, resources and the
complexity increases with the image size (Reddy et
al, 2017). Convolution is known to be a complex
mathematical operation that is highly used in image
processing (Novák et al, 2012). The parallel
programming approach for the image processing is
done through a multi-core central processing unit
CPU and graphics processing unit GPU. GPU with
the presence of the multithreaded parallel
programming capabilities provided by CUDA gained
more popularity with image processing applications
as it increased the speedup factor by hundreds to
thousands compared to the CPU as the CPU speedup
factor is limited to the number of available cores
(Reddy et al, 2017).
This paper studies the performance of Image
convolution using Gaussian filter technique on
parallel systems using Google Colaboratory platform
and studies the effect of using Google Colaboratory
platform. The paper uses two different parallel
systems: multi-core central processing unit CPU and
graphics processing unit GPU. The paper is organized
as follows: Section 1 presents the introduction on the
parallel systems: multi-core central processing unit
CPU and graphics processing unit GPU, Gaussian
Blur filter and related work. Section 2 presents the
experiment, the architecture of the platform used, the
results and the final section is the conclusion.
2 PRELIMINARY
2.1 CPU and GPU
A Multicore CPU is a single computing component
with more than one independent core. OpenMP
(Open Multi-Processing) and TBB (Threading
Building Blocks) are widely used application
programming interfaces (APIs) to make use of
multicore CPU efficiently (Polesel et al, 2000). In this
study, a general purpose platform using Python pymp
tool is used to parallelize the algorithm. On the other
hand, GPU is a single instruction and multiple data
(SIMD) stream architecture which is suitable for
applications where the same instruction is running in
parallel on different data elements. In image
convolution, image pixels are treated as separate data
elements which makes GPU architecture more
suitable for parallelizing the application (Polesel et al,
Ibrahim, N., ElFarag, A. and Kadry, R.
Gaussian Blur through Parallel Computing.
DOI: 10.5220/0010513301750179
In Proceedings of the International Conference on Image Processing and Vision Engineering (IMPROVE 2021), pages 175-179
ISBN: 978-989-758-511-1
Copyright
c
2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
175
2000). In this study, we are using CUDA platform
with GPU since CUDA is the most popular platform
used to increase the GPU utilization. The main
difference between CPU and GPU, as shown in
Figure 1 (Reddy et al, 2017), is the number of
processing units. In CPU it has less processing units
with cache and control units while in GPU it has more
processing units with its own cache and control units.
GPUs contain hundreds of cores which causes higher
parallelism compared to CPUs.
2.2 NVIDIA, CUDA Architecture and
Threads
The GPU follows the SIMD programming model.
The GPU contains hundreds of processing cores,
called the Scalar Processors (SPs). Streaming
multiprocessor (SM) is a group of eight SPs forming
the graphic card. Group of SPs in the same SM
execute the same instruction at the same time hence
they execute in Single Instruction Multiple Thread
(SIMT) fashion (Lad et al, 2012). Compute Unified
Device Architecture (CUDA), developed by
NVIDIA, is a parallel processing architecture, which
with the help of the GPU produced a significant
performance improvement. CUDA enabled GPU is
widely used in many applications as image and video
processing in chemistry and biology, fluid dynamic
simulations, computerized tomography (CT), etc
(Bozkurt et al, 2015). CUDA is an extension of C
language for executing on the GPU, that
automatically creates parallelism with no need to
change program architecture for making them
multithreaded. It also supports memory scatter
bringing more flexibilities to GPU (Reddy et al,
2017). The CUDA API allows the execution of the
code using a large number of threads, where threads
are grouped into blocks and blocks make up a grid.
Blocks are serially assigned for execution on each SM
(Lad et al, 2012).
2.3 Gaussian Blur Filter
The Gaussian blur, is a convolution technique used as
a pre-processing stage of many computer vision
algorithms used for smoothing, blurring and
eliminating noise in an image (Chauhan, 2018).
Gaussian blur is a linear low-pass filter, where the
pixel value is calculated using the Gaussian function
(Novák et al, 2012). The 2 Dimensional (2D)
Gaussian function is the product of two 1
Dimensional (1D) Gaussian functions, defined as
shown in equation (1) (Novák et al, 2012):
G(x,y) =
𝑒
(1)
where (x,y) are coordinates and ‘σ’ is the standard
deviation of the Gaussian distribution.
The linear spatial filter mechanism is in the
movement of the center of a filter mask from one
point to another and the value of each pixel (x, y) is
the result of the filter at that point is the sum of the
multiplication of the filter coefficients and the
corresponding neighbor pixels in the filter mask range
(Putra et al, 2017). The outcome of the Gaussian blur
function is a bell shaped curve as shown in Figure 1
as the pixel weight depends on the distance metric of
the neighboring pixels (Chauhan, 2018).
The filter kernel size is a factor that affects the
performance and processing time of the convolution
process. In our study, we used odd numbers for the
kernel width: 7x7, 13x13, 15x15 and 17x17.
Figure 1: The 2D Gaussian Function.
2.4 Related Work
Optimizing image convolution is one of the important
topics in image processing that is being widely
explored and developed. The effect of optimizing
Gaussian blur by running the filter on CPU multicore
systems and its improvement from single CPU had
been explored by Novák et al. (2012). Also, exploring
the effect of running Gaussian blur filter using CUDA
has been explored by Chauhan (2018). In the previous
studies, the focus was getting the best performance
from multicore CPUs or from GPU compared to the
sequential code. Samet et al. (2015) presented a
comparison between the speed up of real time
applications on CPU and GPU using C++ language
and Open Multi-Processing OpenMP. Also, Reddy et
al. (2017) presented a comparison between the
performance of CPU and GPU was studied for image
edge detection algorithm using C language and
CUDA on NVIDIA GeForce 970 GTX. In our study
we are exploring the performance improvement
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between two different parallel systems, CPU
multicore and GPU using python parallel libraries
pymp and CUDA respectively. We are using Intel
Xeon CPU and NVIDIA Tesla P100 GPU.
3 EXPERIMENTAL SETUP
In our experiment, we are using Google Colaboratory
or “colab” to run our code. Google Colaboratory is a
free online cloud-based Jupyter notebook
environment that allows us to train PyCUDA and
which gives you easy, pythonic access NVIDIA’s
CUDA parallel computation API. To be able to run
our code on Google Colabs, we used the Python
programming language. Python pymp library is used
for the multiprocessor code. This package brings
OpenMP-like functionality to Python. It takes the
good qualities of OpenMP such as minimal code
changes and high efficiency and combines them with
the Python Zen of code clarity and ease-of-use.
Python PyCUDA library is used to be able to call
CUDA function in our python code and PyCUDA
gives you easy, pythonic access to NVIDIA’s CUDA
parallel computation API.
3.1 Architecture of the Used GPU
NVIDIA Tesla P100 GPU accelerators are one of the
advanced data center accelerators, powered by the
breakthrough NVIDIA Pascal™ architecture and
designed to boost throughput and save money for
HPC and hyperscale data centers. The newest
addition to this family, Tesla P100 for PCIe enables a
single node to replace half a rack of commodity CPU
nodes by delivering lightning-fast performance in a
broad range of HPC applications (Nvidia
Corporation, 2016). CUDA toolkit 10.1 is used.
3.2 Architecture of the Used CPU
Intel Xeon is a high-performance version of Intel
desktop processors intended for use in servers and
high-end workstations. Xeon family spans multiple
generations of microprocessor cores. Two CPUs
available with two threads per core and one core per
socket (http://www.cpu-world.com/CPUs/Xeon/).
3.3 Experiment Results
Three images are used in our experiment, their
particular sizes are of 256 x 256, 1920 x 1200 and
3840 × 2160. We ran our Gaussian filter algorithm on
the images using different kernel sizes and calculated
the average processing time of 10 runs. First, the color
channels of the images are being extracted to red
channel, green channel and blue channel. In the
second step the convolution of each channel
respectively with the Gaussian filter. Using the
pycuda library in python code, a CUDA C function
had been used to be able to run the code on the GPU.
The data is transferred from host to device and after
the convolution operation is being brought back to the
host. In the end we merge all the channels together to
get the output of the blurred image. The processing
time calculated is the time taken by the convolution
function of the filter on the CPU using two threads
and on the GPU as shown in Table 1 and Table 2
respectively. The processing time and speedup are
shown in Figure 2 and 3 respectively. Figures 4 and 5
respectively show the original image and blurred
image of resolution 256 x 256 using 7 x 7 filter
kernel.
From the results of Bozkurt et al. (2015), it is
observed that our performance speedup on GPU was
higher than the one proposed by Bozkurt et al. (2015)
by more than 3 times.
Table 1: CPU time.
Image resolution
(pixels)
Kernel size
Processing time
(s)
256 x 256
1920 x 1200
3840 × 2160
7 x 7
13 x 13
15 x 15
17 x 17
7 x7
13 x 13
15 x 15
17 x 17
7 x 7
13 x 13
15 x 15
17 x 17
8.2
34.9
47.8
62.3
429.3
1358.2
1882.9
2294.15
1296.4
5322.2
7347.02
9225.1
Table 2: GPU time.
Image resolution
(pixels)
Kernel size
Processing time
(s)
256 x 256
1920 x 1200
3840 × 2160
7 x 7
13 x 13
15 x 15
17 x 17
7 x 7
13 x 13
15 x 15
17 x 17
7 x 7
13 x 13
15 x 15
17 x 17
0.00200
0.00298
0.00312
0.00363
0.02726
0.03582
0.04410
0.054377
0.06950
0.11282
0.14214
0.17886
Gaussian Blur through Parallel Computing
177
Figure 2: Processing Time.
Figure 3: Speedup.
Figure 4: Original Image.
Figure 5: Blurred image using 7 x 7 filter kernel.
4 CONCLUSIONS
Google Colaboratory platform showed great results
for using their free access to run our code on the
GPUs. Better performance and speedup were gained
using CUDA GPU architecture when compared to
multicore CPU. The graphs in Figures 2 and 3 show
that the processing time for the GPU is almost zero
compared to the CPU processing time. Increasing the
number of threads for the CPU could give better
results but still it will not be able to fill the gap clearly
shown between CPU and GPU. GPU is a better
candidate for applications having complex operations
where the same operation is applied to a big set of
data as convolution. Image convolution with
Gaussian blur filter is better handled with GPU and
gives much better performance.
As future work, to run the same algorithm on
different multicore CPU and GPU. Also to investigate
the image transfer time from the CPU to GPU and
optimize the transfer time of the image for better
performance and speedup.
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