Chapter I
INTRODUCTION
For years, researches have been carried out focusing on ways to maximize
computing performance of processors as the needs and expectations for faster and higher
quality applications are of great demand. At present, high performance computing systems
exists. These systems have played an important role in the developments and innovations in
communications, hardware, network protocols, and operating systems as well as in solving
important scientific, engineering and business problems.
Conventional supercomputers are very expensive that only large corporations,
government and large educational institutions can afford them. To average researchers and
developers, these systems are beyond their budgets. But a lower cost option is available
through distributed computing.
Distributed computing is the use of multiple network-
connected computers for solving a problem or for information processing. The use of this
system has substantially lowered price and complexity for implementing high performance
computing. Given this new affordability, new experiments in installing such systems to
develop intensive processing applications are being done. One application that requires
intensive CPU processing in computer graphics is the 3D computer rendering. 3D rendering
is the process of generating from the abstract description of a 3D scene.
Despite the
developments of new techniques and algorithms, this process is computationally intensive
and requires a lot of time to be done; especially when the source scene is complex or when
photo-realistic images are required. It takes a lot of time before a rendering process is
completed. This is because rendering requires intensive CPU processing due to the large
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number of calculations done, especially in movie or video animation applications. This study
focuses on improving and speeding up the computation process in 3D rendering, through the
use of distributed computing. The capabilities of an open source 3D graphics application
will be improved so that it will allow its processes to be distributed to different processors
and achieve high performance computing at minimal cost.
As faster processors become available, so do applications and algorithms that take
full advantage of their capabilities. This is an irony in computer graphics. As a result, the
speed of animation tasks such as rendering performance is slow. With the rise of different
facts, studies, and applications about 3D rendering, intensive CPU processing, and
distributed computing, several problems arises.
Is there a way to obtain a high speed
rendering performance using available PCs? Is it possible to say that money is not an
obstacle in obtaining this? Is it possible to obtain this through distributed computing? The
researchers will be trying to solve these problems in this senior project.
This Senior Project is important to different kinds of people. To the Ateneo de Naga
University, this could provide faster rendering ability with a minimal cost. With this, the
university could save a lot of money than purchasing costly high specs computers. To the
animators: This project will help them speed up the rendering process of their animations
using the available resources in their offices or laboratories. To the students and researchers:
this project can be a reference for future researches.
This would also broaden their
understanding about distributed computing and help them be aware of the benefits that a
distributed computing as a high performance computing provides.
Hence, this will
encourage them to engage in the development of applications that require CPU intensive
2
processing, especially in solving important scientific and engineering problems that can help
in the development of our country.
This study attempts to develop a 3D graphics application that will be using
distributed systems. Specifically, this senior project aims to achieve the following: (1) obtain
high performance computing system at a low cost; (2) utilize the available computers for the
implementation of the program; (3) achieve a high speed rendering of Computer-Aided
Designs (CAD) though the use of distributed computing, and (4) prove that distributed
computing can achieve the same speed as supercomputers or as the high specs PCs.
Although this project aims to solve three broad problems, this is limited to the
following: (1)Twelve computers are to be connected in the network to implement the project;
(2) Only rendering Computer-Aided Designs(CAD) using a rendering and queuing software
programs shall be the task of the project; (3)Computers to be used in the implementation
must be the available computers in the Ateneo laboratories (CISCO); (4)The project will run
only in Linux environment, and (5)The focus of this study will only be limited to obtaining
high speed and low cost rendering through distributed computing.
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Chapter 2
REVIEW OF RELATED LITERATURE
The study aims to develop a low cost distributed system that will render 3D Computer
Aided Design. The following are the researches and literature relevant to this study:
High Performance Computing
High performance computing (HPC) is the use of parallel processing for running
advanced application programs efficiently, reliably and quickly. HPC is a high speed
computing that uses supercomputers or computer clusters to solve advanced computation
problems. HPC is usually used in research and development in many areas of science,
engineering and business. (Dowd, K. 1998)
In a computer, how quickly calculations can be set up and input to the processor and
how quickly new jobs and their data can be moved in, completed, and the results moved out
of the computer determines how much of the processor's speed can actually be. Computers
are often compared on the basis of processing speed. The convention used for measuring the
computer performance is "FLOPS" (floating point operations per second). A FLOP makes
heavy use of floating point calculations. Floating point operations include operations that
involve fractional numbers. Such operations, which take much longer time to compute than
integer operations, occur often in some applications. Thus, FLOPS measure the speed of the
arithmetic processor. Currently, the largest high performance computers have processing
speeds ranging up to petaflops. (Eadline D., 2009)
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High performance computers have played an important role in contributing to wealth
creation and improving the quality of life through enabling the development of new products
and processes with greater efficacy, efficiency or reduced harmful side effects, and in
contributing to our ability to understand and describe the world around us. Over the past
decade, High-performance 'supercomputers' are becoming tools of international competition
and they play an important role in scientific research. Many of the national and international
problems we face involve complex computations that only high-performance computers can
solve. Some examples of this includes automotive, weather forecasting, popular
entertainment, aerospace, electronic, and pharmaceutical industries that are becoming more
reliant on the use of high-performance computer in the analysis, engineering, design, and
manufacture of high-technology products. (Graham, et al., 2004) HPC was originally
pertaining only to supercomputers for scientific research, but high-performance computing
migrated to the business world. In the business world, the capabilities in developing,
manufacturing, and applying high-performance computing are also crucial in the rapid
changing global economy. To lead the increasing international competition in businesses,
some of the largest enterprises require power of a high performance computer. (Sterling, et
al., 1995)
The term HPC is sometimes used as a synonym for supercomputing, although
technically a supercomputer is a computer that performs at or near the currently highest
operational rate for computers. A supercomputer is one of the fastest kinds of computer. It is
a machine used to perform parallel processing through the use of multiple processing
elements. It uses hundreds or thousands of processing elements and are generally more
difficult to program. It is very expensive and is commonly used for special applications that
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require intense calculation tasks. These are usually used in research and development in
many areas of science and engineering, as well as national security and defence. Because
supercomputers are very costly, only big companies, large academic institutions and
government military agencies are able to afford and use these systems. (Graham, et al.,
2004)
Supercomputer-like performance also could be achieved through high performance
computing clusters. "Cluster" is an ambiguous term in computer industry. Given the
ambiguity in the usage of terminology and blurred boundaries between various technologies,
we define a cluster as set of computers which are connected to each other, and are physically
located close to each other, in order to solve problems more efficiently. It is a type of parallel
or distributed system that consists of a collection of interconnected computers and is used as
a single, unified computing resource. (Kant, 2005) A cluster of networked computers are
usually deployed to improve performance and/or availability over that of a single computer,
while typically being much more cost-effective than a single computer of comparable speed
or availability. This cluster of computers works together so that in many respects they form a
single computer. (Bookman, 2003) Clusters are used to run parallel programs for timeintensive computations. They commonly run simulations and other CPU-intensive programs
that would take an excessive amount of time to run on regular hardware.
A popular related term common in the discussions of computer clusters is the Beowulf
term. Some cluster computer is being referred to as Beowulf clusters. Although not
technically accurate for all types of HPCs, a Beowulf cluster refers to computer cluster built
using primarily commodity components and running an Open Source operating system.
(Narayan, A., 2005) The High Performance Computing Group of the Ateneo de Manila
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University has developed AGILA HPCS. It stands for the Ateneo Gigaflops-Range
Performance, Linux OS, and Athlon Processors High Performance Computing System. It is
an interdisciplinary project aimed at supporting the computational science and engineering
research at ADMU. AGILA used eight (8) compute nodes connected by a 100Mbps Fast
Ethernet and supports parallel programming using message passing software such as LAMMPI and PVM to achieve a high performance computing system. (Saldaña, et al., 2001)
With the increasing availability of cheaper and faster computers, there is a growing
interest in the technological benefits of this system. With the introduction of computer
clusters, new applications are being developed to assist researches in specific areas where
supercomputer is not practical to use because it is not affordable.
There are many applications which can benefit from using clusters of computers.
Clusters are being used in a specific area such as rendering. In a cluster for rendering
purposes, each node can be capable of running a rendering algorithm and animation can be
rendered in parallel, thus cutting down on production time. As an example, the rendering of
certain scenes in the movie Titanic were performed on a cluster of Linux-based machine. As
such, it is clear that some in the entertainment industry see the potential benefits of utilizing
clusters to create movie scenes artificially. (Hope, L. and Lam, E., 2008)
Chao-Tung Yang and Yao-Chung Chang, in their study entitled “Apply cluster and
grid computing on parallel 3D rendering” used cluster and grid computing on 3D rendering
application. In their paper, they used a PC cluster consisting of one master node and nine
diskless slave nodes built for the purpose parallel rendering. They used two heterogeneous
PC clusters and the clusters were set to different subnets then used a grid middleware to
connect the two clusters to form a grid computing environment on multiple Linux PC
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clusters. They also installed software to manage and monitor incoming or outgoing
computing job and schedule the job to achieve high performance computing and high CPU
utilization. (Yang and Chang, 2004)
In the study conducted by Chao-Tung Yang and Yao-Chung Chang, they used grid
computing on their 3D rendering computer cluster. Grid computing is a type of distributed
computing using loosely coupled systems. Grid technologies enable large scale aggregation
and sharing of computational data and other resources across the internet. A successful, wellknown project in grid computing is SETI@home, the Search for Extraterrestrial Intelligence
program, which used the idle CPU cycles of a million home PCs via screen savers to analyze
radio telescope data. Over 630,000 years of computational time has been accumulated by the
project. It is a great reflection on the power of distributed computing and the internet. These
computing technologies promise to change the way we tackle complex problems. (Narayan,
2005, Bookman, 2003)
Distributed Systems
One way to achieve a high performance computing is through Distributed Computing
Systems. A distributed computing system is a group of multiple autonomous processors
which are interconnected by a communication subnet to interact in a cooperative way to
achieve an overall goal (Ananda, 1991). Processors participating in it are often rather
different in size and power, architecture, organizational membership, and so on (Peleg,
2000). These processors do not share a common memory. Information is exchanged by
passing messages between these processors. Software supporting distributed computing must
be run on each computer to be able to execute the process.
8
The Distributed Computing Server take distributed computing requests and divide the
processes into smaller tasks that can run on the individual PCs. It sends applications and
some client management software to the client machines that request them. It monitors the
status of jobs being executed. And after the machines run the programs, the server assembles
the results sent back by the clients and combines them to solve a big rendering task.
If the server does not hear from a processing client for a certain period of time, it may
send the same application to another system. The server also manages any security policy or
other management functions as necessary, including handling the dial-up users whose
connection and IP addresses are inconsistent.
The complexity of the distributed computing system depends on the size and type of
environment. A larger system requires complex resource identification, policy management,
authentication, encryption, etc. Resource identification is necessary to define the level of
processing power, memory, and storage each system can contribute. Policy management, on
the other hand, is used to define the jobs and users are allowed to access a system, as well as
the priorities based on the importance of each project. Authentication and encryption are
necessary to prevent unauthorized access and data within the distributed system (Erlanger,
2002).
With distributed systems, we can derive a bunch of benefits. Increased performance,
increased reliability and availability, flexibility and ease of extendibility, modularity, local
control, and reduced cost are some of this benefit. (Ananda, A.L., Srinivasan, B. (1991),
Distributed Computing Systems: Concepts & Structures, IEEE Computer Society Press, Los
Alamitos, CA)
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These benefits can be easily seen by comparing it to supercomputers. Distributed
systems do not require pricey electrical power, environmental controls, and extra
infrastructure that a supercomputer requires. Also, unlike supercomputers which programs
are written in specialized languages, applications in distributed computing can be written in
basic languages like C and C++.
Distributed computing also improves the speed of
processes. In a case study that Intel did of a commercial and retail banking organization
running Data Synapse's LiveCluster platform, computation time for a series of complex
interest rate swap modeling tasks was reduced from 15 hours on a dedicated cluster of four
workstations to 30 minutes on a grid computing of around 100 desktop computers.
Processing 200 trades on a dedicated system took 44 minutes, but only 33 seconds on a grid
of 100 PCs (Erlanger,2002)
One process that can be speed up by distributed computing is the rendering process.
A study conducted in Nanyang Technological University in Singapore, entitled “Grid Based
Computer rendering” used render farms, or a cluster of interconnected computers, to boost
the rendering speed. Rendering a single frame of a 3D model in professional animation
normally takes about several hours. But in this study, by employing two different clusters
with a total of 60 remote computing nodes for rendering, the rendering time has been greatly
reduced. A 3D picture having 100 frames have been rendered for only 2mins. 50sec.
(Chong,A., Sourin, A., Levinski, K., (2003).Grid Based Computer Animation Rendering.
Graphite 2006.)
Another study that implemented distributed computing is “On utilization of the Grid
Computing Technology for Video Conversion and 3d Rendering” a study conducted at
different sites in Taiwan. The study implemented rendering, interconnecting four different
10
sites with different PC specifications, using grid computing. They used a special program,
MPI-Povray to distribute processes across networks. The study shows that there is a speedup
in task processing if there are 8 processors to render an image, which created eight task on
the grid platform. (Yang,C., Lai,C., Li, K., Hsu, C., Chu, W. (2005). On utilization of the
Grid Computing Technology for Video Conversion and 3d Rendering. Parallel and
Distributed Processing and Application, Third International Symposium, ISPA 2005, 442453)
Rendering
Rendering is the process by means of which a 2D (two-dimensional) image can be
obtained from the abstract definition of a 3D (three-dimensional) scene (Morcillo et al.). It is
a process depicting the three-dimensional scene as a picture, taken from a specified location
and perspective which could add the simulation of realistic lighting, shadows, atmosphere,
color, texture, and optical effects. Figure 1 illustrates the problem. The result of rendering
could also be unrealistic to the extent of just to appearing as a painting or an abstract image.
It is simply a computer graphics transformation process converting 3D models into 2D
images (Jeremy Birn,2002).
Figure 2.1. Rendering Process
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The process of producing this 2D (two-dimensional) image in rendering requires
several phases such as modelling, setting materials and textures, placing the virtual light
sources and the actual rendering itself (Morcillo, et al).
Rendering algorithms take a
definition of geometry, materials, textures, light sources and virtual camera as input and
produce an image or sequence images (used in animations) as output.
Considering the development of new techniques and algorithms, rendering is still
seen as an intensive process that requires a lot of time in transforming source scene, whether
complex or not, into photo-realistic images.
High-quality photorealistic rendering of complex scenes is one of the key goals of
computer graphics and has been adopted by different institutions and professionals among
different fields of expertise. Through the course of time, rendering has been serving as a
useful application despite its time constraints and naturally-intensive processing.
It has become a useful tool to different companies or professionals in different ways
but generally, in carrying out their objectives in a pleasant and detailed manner. One specific
example is the architects who use this process to present designs to their clients
(3DStormstudio, Inc.,2009). Most clients cannot understand elevations or floor plans and are
simply not trained to visualize 3D forms based from simple or a complicated 2D drawing,
therefore architects use rendering to communicate their designs easily to clients, leaving no
room for confusion. The media industry is another avenue for rendering to be used as a tool
since it demands high fidelity images for their 3D synthesis projects which can be manifested
in the advertisements, campaigns, and programs which they produce. For animators, this is
used to produce quality animation which is truly visually pleasing and aesthetic.
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Depending on the rendering method and the scene characteristics, the generation of a
single high quality image may take several hours or even days (Morcillo, et.al) In some
instances, rendering may take from seconds to days for a single image/frame. Because of the
huge amount of time required to be done, the rendering phase is often considered to be a
delaying factor or step in photorealistic projects in which one image may need some hours of
rendering in a modern workstation.
Rendering sometimes takes a long time, even on very fast and highly-advanced
computers. This is because the software is essentially "photographing" each pixel of the
image, and the calculation of the color of just one pixel can involve a great deal of
calculation, tracing rays of light as they would bounce around the 3D scene. To render all
the frames of an entire animated movie can involve hundreds of computers working
continuously for months or years.(Birn, J., 2009)
Considering the nature of rendering and knowing the computational requirements of
rendering are huge, obtaining the results in a reasonable time on a single computer is
practically impossible. For that, several approaches based on different technologies have
been developed to assure the affordability, efficiency, and quality in the rendering process.
One way to lessen the rendering time is through a supercomputer. The supercomputer
called Eka, or the sanskrit term for “the one,” is the fastest supercomputer in Asia today and
is a main component of 3D animation in Pune, India. Eka was last used in the rendering of
the film Roadside Rodeo, the first international quality of India. It accomplished the
rendering job in five months. The supercomputer has been very pivotal in reducing the
rendering times for animation frames, computer generated imagery (CGI), visual effects
13
(vfx) and compositing in the domains of high end 3D modelling, 2D & 3D animation and
game asset development.
A study in Purdue University entitled "Using 3D Computer Animation Tools to
Render Complex Simulations" created several simulations and visualizations of the terrorist
attack on the Pentagon building that happened on Sept. 11, 2001. The process took advantage
of both animation and finite element analysis (FEA) simulation techniques for visualization.
During the main production of the project, supercomputers were being used to get useful
results. However, this project was halted due to lack of computing resources. The researchers
are thinking of the possibility of distributing the solutions to many computers(distributed
computing) which could hold the key to scaling the simulations.
In spite of the great speed that supercomputer offers, only big companies can afford
its high price. Hence, other systems such as distributed computing systems are being
considered as alternatives. Today, since technologies are being replaced almost monthly by
new ones and stacks of old processors are increasing, old computers can be recycled to create
a distributed computing system. Through this, we can have the performance of a
supercomputer at an affordable price.
Different studies presented above offered a high performance computing. The making
of the film Roadside Rodeo, and the animation of the terrorist attack in Pentagon, both used
supercomputers for rendering. On one hand, the different studies on Grid computing
provided an alternative solution by implementing grid computing on rendering. These studies
14
may provide a great speed but not low cost because they used high cost processors, and even
supercomputers.
The study of Chao-Tung Yang and Yao-Chung Chang, the “Grid Based Computer
rendering” study in Singapore, and the other studies mentioned earlier, rendered their task
using grid computing. They interconnect the computers in one area and in different sites
using internet. However, this kind of setup is difficult to maintain because of the different
locations of the client.
Our study aims to use the unused PCs of the engineering department which will give
us the same performance of the previous studies at an affordable price. We also aim to
achieve high performance 3d rendering through tightly coupled or physically connected
systems. With the processors based in one location, maintenance and troubleshooting will be
easier.
15
Chapter 3
METHODOLOGY
Process Construction
Figure 3.1 Process Construction
16
The first step in this study is to find and gather as many available PCs suitable for
the research purposes. The number of CPUs that will be used impacts the rendering speed.
The more PCs attached in the distributed system and the better the specs found, the much
faster it is to render. For this study, we will be using the unused PCs from the Engineering
Laboratories of Ateneo de Naga University as well as the PCs from the CISCO Laboratory.
We will be using 12 old PCs and 12 new PCs from the CISCO Laboratory. Once the PCs
have been selected, the operating system to be used will then be installed.
The next step is to configure a server. After configuring, the server and the PCs will
be joined together in a communications network so that it is possible to exchange
information between different devices. Then, a domain will be created, and the PCs will be
joined in this domain. Through this, user management is consolidated on one central server.
User management is an essential distributed system requirement for it controls the actions of
every member of the system. It assures security by controlling the access of members to a
rendering queue by reprioritizing jobs, stopping them, restarting specific frames, changing
frames to be rendered and so on. Rendering queue is a sequence of render programs waiting
processing.
It gives permissions as well as restrictions to the users.
Logging in and
changing credentials to the computers will require permissions which can be managed in the
server rather than monitoring individual PCs in the system.
Having a server in the system also handle issues regarding an important network
consideration: network storage. Each PC in the system will need access to the same location
from which it will read its 3D instructions and, after executing them, store its results. Since
the server is already configured, centralizing server resources can easily be done by sharing
access to a common location.
17
The next step is to install the programs that would be needed by the system to
perform rendering. The major programs to be used include a rendering software and queuing
software.
This project will be using open source distributed queuing software as the
middleware system for distributed computing. This queuing software will have the function
of dividing a rendering job into multiple parts and deciding which PC from the system
executes which part and when. We will also be using open source 3D rendering software.
There are freeware Queue managers and 3d rendering software available. However,
the 3d rendering softwares are not designed for distributed systems. Also, the queue manager
does not know anything about rendering job. Our task is to make these two software work as
a whole. Since we will be using an open source queue managers and 3d rendering software,
the source code can and will be edited for the programs to work together.
The final step in the process is the testing and analysis. The system will be tested
to see whether the objectives of this study are met or not. If the objectives are met, then the
result of the study is successful. If the objectives are not met, additional PCs will be added
to the system to improve the speed of rendering. Then the additional devices will be joined
to the domain, be configured to share a common storage location with the system and again
be tested.
18
Physical Setup
In this project, we will be joining old PCs and a server in a network. This will be
done to allow the devices to communicate and exchange information with one another. The
physical set up of the devices is shown in the figure.
Figure 3.2 Physical Setup
As shown in the figure, the devices to be used are old PCs, a server, a gigabit switch
and network cables. The rendering will be done in one of the PCs. The old PCs will be
installed with an operating system and the necessary programs to support in the distribution
of rendering processes. The server will be the one responsible for user management and
resource sharing in the system. It is also responsible for the division of rendering processes
and the distribution of these processes to the PCs. The gigabit switch is the intermediary
device used to join the end devices to the network by connecting the end devices to the
switch through the network cables.
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Test and Evaluation
Testing will be done by comparing the rendering time of 3D files using distributed
systems of low specs PCs(old computers) and
distributed systems of higher specs
PCs(CISCO computers).
3D rendering files will be acquired. These files will be rendered in the two sets of
distributed systems. The results should show that rendering with higher specs of PCs is faster
than the lower ones. Also, after the trials with 12 PCs in the network, we will decrease the
number of PCs in each system. We will also add PCs with higher specs in each system.
Results should show that decreasing the number of PCs will render slow, and adding
additional high specs PCs will increase the rendering performance of each system. Results
will be recorded in a table and will be graphed after gathering all the data needed.
Using the data gathered, analysis will be done. A comparison of the rendering
speed will be made. This should prove the theory that the higher the number of PCs and the
higher the specs, the faster the rendering speed will be. A positive result will support the
idea that the distributed computing system with a certain number of PCs can achieve the
same speed as supercomputers or as high speed PCs.
However, if rendering results are not successful, troubleshooting will be done by
adding and configuring more units of computer to the system then updating the queue
manager to achieve a faster rendering speed.
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21
Cost Estimate
Item
Cost
16-port Gigabit Switch
9000 php
20RJ45
120 Php
10 meters UTP Cable
150 Php
Table 2 Cost Estimate
22
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