Developing a High-Resolution Tile Display Based on 

Commodity Components 

by Richard C. Angelini 

ARL-TR-3640 September 2005 

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ARL-TR-3640 September 2005 

Developing a High-Resolution Tile Display Based on 

Commodity Components 

Richard C. Angelini 

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14. ABSTRACT 

The advent of powerful, low-cost desktop computers is changing the way computational scientists visualize the results of their 

calculations. Traditionally, only the least complex data sets could be analyzed at the local desktop, while large, time-dependent 

data sets required the use of high-end graphics servers to visualize the results. Today, we can analyze the results of very large 

computations that were previous unimaginable on the desktop, and in some cases, the graphics subsystem in new desktop 

workstations can outperform dedicated visualization servers. However, in many cases, scientists are now computing more than 

they can practically represent on their traditional desktop display monitor. The challenge presented is to effectively represent a 

high-performance computing-sized computational domain in a typical 1280 × 1024 display, or develop a high resolution, costeffective display technology. 

15. SUBJECT TERMS

commodity cluster, scientific visualization, tile display 

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Richard C. Angelini 

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iii

Contents 

List of Figures iv

List of Tables iv

Acknowledgments v

1. Introduction 1

2. Background 3

3. Relative Performance of Desktop Workstations 6

4. Configuration 10

5. Performance of the Tile Display Cluster 16

6. Conclusion 17

7. References 18

Appendix A. XF86Config File 19

Appendix B. Draper Flat Panel Mounting Bracket 25

Distribution List 26 
iv

List of Figures 

Figure 1. Immersadesk display. ......................................................................................................2 

Figure 2. Rave-II display. ...............................................................................................................2 

Figure 3. Collaboratorium at the Adelphi Laboratory Center.........................................................4 

Figure 4. Aberdeen Visualization Center........................................................................................5 

Figure 5. Benchmark results. ..........................................................................................................8 

Figure 6. Best SGI performance vs. commodity workstations. ......................................................9 

Figure 7. Weighted composite score...............................................................................................9 

Figure 8. Opteron-based commodity cluster used to drive tile display. .......................................11 

Figure 9. FX3000G Dual-DVI Interface configuration. ...............................................................12 

Figure 10. Extruded aluminum frame flush mounted to wall with monitors mounted.................12 

Figure 11. Custom turnbuckle used to align monitors..................................................................13 

Figure 12. Monitor layout with exposed cabling..........................................................................14 

Figure 13. Completed tile display.................................................................................................15 

List of Tables 

Table 1. Results of each of the five tests of the benchmark. Value is time (seconds)...................8 
v

Acknowledgments 

The author thanks Dr. James Kohl and Jamison Daniel of Oak Ridge National Laboratory for 

their consultation on the design and engineering of a tile display system; Mr. Dennis Hash (ARL 

WMRD Materials Application Branch) for his assistance in the fabrication of components for the 

tile display wall; Mr. Kelly Kirk (ARL CISD High Performance Computing Division) for his 

assistance with Linux systems configuration and administration; and Mr. Chris Fahey and 

Mr. Kai Rassmussen (ARL MSRC Programming, Environment, and Training Summer Intern 

Program) for their assistance in assembly of the cluster and tile display. 
vi

INTENTIONALLY LEFT BLANK.
1

1. Introduction 

Alternate display technologies are essential to the implementation of scientific visualization in 

the laboratory setting. High-performance computing (HPC) facilities, such as the Major Shared 

Resource Center (MSRC) located at the Army Research Laboratory (ARL), Aberdeen Proving 

Ground, MD, support computational projects that generate enormous amounts of data. These 

calculations typically generate tens of gigabytes to terabytes of data, and it is difficult for a 

researcher to interpret this vast amount of information without the extensive use of visualization 

techniques and technologies. There are numerous software tools available to analyze data, 

however, very large, time-dependent data sets require specialized scientific visualization 

software and hardware not typically found at the desktop. Visualization laboratories, 

collaboratoriums and virtual reality centers have been established at ARL and other top 

government and commercial laboratories for the sole purpose of providing an environment for 

researchers to view and interact with their data. 

Until very recently, these visualization laboratories would have been very similarly equipped. 

Each would have had some sort of large, dedicated visualization server, such as a multiprocessor 

SGI Onyx2, with large memory, multiple graphics pipes, and large amounts of disk storage. The 

output from this system would then be displayed to a large screen via a stereoscopic projection 

system, such as single-screen Immersadesk (figure 1) or an immersive, multiprojection Cave or 

RAVE-II (figure 2).* At the time these displays were conceived and installed, this was a superior 

solution and state of the art. However, the cost associated with installing and maintaining such a 

system is quite substantial, and far beyond the reach of a departmental research staff. Such a 

display needed to be funded and supported at corporate levels, and required a significant amount 

of square footage to implement. 

Today, however, the amount of computer and graphics power that can be placed on the desktop 

is staggering. Commodity computers and very low-cost graphics cards have demonstrated 

enormous growth in performance over the past few years, and the cost-performance ratio cannot 

be ignored when compared to the high-end visualization servers. Computational scientists can 

now work on very large computations on their desktop system that were previous unimaginable, 

and in some case, the graphics subsystem in new desktop workstations can outperform the 

dedicated visualization server. However, despite the availability of these resources, there will 

always be certain classes of computation problems that challenge both the hardware and software 

resources that are available and problems that ultimately require the unique capabilities of 

dedicated visualization hardware and the talents of the visualization team staff. 

 *

 Immersadesk, Cave and RAVE-II are all trademarks of Fakespace, Inc. 
2

Figure 1. Immersadesk display. 

Figure 2. Rave-II display. 
3

Projects related to the use of high-resolution displays and commodity computers have been 

established at a number of government laboratories and universities. The Department of Energy 

has several initiatives (1–3) under the Advanced Simulation and Computing Initiative (ASCI) to 

develop an infrastructure of Linux-based workstations with commodity graphics cards to provide 

the means to visualize their largest data sets. California Institute of Technology, Stanford, and 

Brown are among the list of universities who are partnering with government laboratories to 

develop and extend the capabilities of commodity cluster environments. 

Scientists are most comfortable working in their own environment, and now in many instances 

have no need to go to a visualization laboratory to perform their routine data analysis and 

visualization. However, scientists are now computing problems larger than they can practically 

represent on their traditional desktop display monitor. The challenge presented is to effectively 

represent an HPC-sized computational domain in a typical 1280 × 1024 display, or develop a 

high resolution, cost-effective display technology. The risk of losing critical computed 

information exists if gigabytes of information are compressed to 1-million pixels of a traditional 

display. Therefore, a cost effective solution to display more pixels of data is required, and this 

solution needs to be easy to maintain, not take a large amount of floor space, and be cost 

effective to implement. 

To address these needs, this report will focus on the methodology used to develop a very highresolution tile display wall using cost effective commodity computers, standard flat-panel 

displays, and open source software. If successful, this display-wall technology can be 

implemented in critical work areas, allowing scientists to see more of their computation than 

they can at their desktop without going far from their desk, but at a cost which is practical. 

2. Background 

The Ballistic Research Laboratory (BRL), a predecessor organization to the current ARL, formed 

its’ first Scientific Visualization team in 1991. Shortly thereafter, the first general-purpose 

visualization laboratory was established in Building 390, in conjunction with the Interior 

Ballistics Division of BRL. 

In 1998, shortly after ARL was selected as an MSRC, a newer, larger visualization laboratory 

opened in Building 394 (replacing the Building 390 laboratory), creating a facility that was colocated with the visualization team staff. These initial facilities provided state-of-the-art 

visualization servers and graphics capabilities. At that time, most large data sets could not be 

viewed at the researchers’ desktop, and researchers would routinely work in the visualization 

laboratory, taking advantage of the capabilities of the high-end graphics systems to view the 

results of their computations. 
4

In the spring of 2002, ARL completed construction on its’ first large scale visualization theater, 

known as the Collaboratorium (figure 3). Located at the Adelphi Laboratory Center (ALC), this 

facility houses a very high-end SGI visualization server that provides a stereoscopic video 

display on a 24-ft curved screen. The facility was designed to support the requirements of 

computation scientists wishing interact with their data, but it also serves as a large, shared 

resource where an audience of 30 or 40 people can view the same stereoscopic visualization and 

feel equally immersed in the experience. On the large curved screen, up to 2-million pixels can 

be displayed in stereo mode, and approximately 3-million pixels in standard display mode. 

Figure 3. Collaboratorium at the Adelphi Laboratory Center.

In January of 2003, construction was completed on a new visualization center in Aberdeen to 

replace the facility that was developed in 1998. The Aberdeen Visualization Center (figure 4) 

houses a reconfigurable display system known as a Fakespace RAVE-II display wall. This 

display can be configured into a single wall 40 ft wide × 8 ft tall to support a presentation to an 

audience of 20 or 30 people. However, it was designed with two hinged “wing” displays, each 

of which is 10 ft wide × 8 ft tall. These wings can be closed to create a “cave”-like experience, 

displaying visualization on three side walls, plus the floor, supporting a single-person fully 

immersive experience. This reconfigurable display provides the ultimate in display technology, 

including head tracking and wand input interfaces, and a fully immersive stereoscopic “virtual 

reality” interface to scientific data. The RAVE-II can display approximately 7-million pixels in 

stereo or standard display mode. 
5

Figure 4. Aberdeen Visualization Center.

Concurrent to the construction of the Aberdeen visualization center, powerful commodity 

desktop workstations based on standard PC technology became prevalent. Low-cost commodity 

graphics cards were showing signs of being able to outperforming the previously superior 

visualization server graphics pipes. In the years leading up to this time, it was clear that 

commodity computers were becoming faster, however, the user environment was unstable at 

best. The availability of open source, public domain operating systems such as Linux provided a 

desktop environment that researchers were familiar with, but there were concerns about network 

security, operational reliability was questionable, and corporate support was not available. In 

2004, the company Red Hat announced the availability of a Linux operating system that was 

based on previously developed open-source standards, but was professionally supported. ARL 

adopted this operating system as a standard, and at this point, powerful Linux-based commodity 

desktop systems became a viable alternative to the proprietary Unix-based workstations such as 

those from SGI, SUN, and Hewlett-Packard. 

In 2004, the scientific visualization team at ARL built a cluster of commodity computers to 

evaluate this technology as a practical alternative to traditional, shared-memory servers. We 

chose to implement the cluster using Opteron*

 processors from Advanced Micro Devices, Inc 

(AMD). This commodity processor was supported by Red Hat and still placed us on the leading 

edge of the technology curve. Our initial configuration placed five of these Linux-based, 64-bit 

 *

 Opteron is a trademark of Advanced Mico Devices, Inc. 
6

commodity workstations on staff members desktops, connected to a dedicated gigabit ethernet 

network. Once it was clear that there was substantial software support for the implementation of 

a “virtual” cluster by tying these desktop systems together, we proceeded to the next step of 

building a small, dedicated cluster to serve as a transitional system as we moved toward more 

robust commodity computing technology. We chose to continue with Opteron-based processors 

for this transitional system to maintain compatibility with the desktop systems previously 

purchased. 

Once the commodity cluster technology was selected, attention was focused on the details for 

developing the high-resolution display. There are two technical approaches to building a highresolution display. The first alternative uses high-resolution projector technology (4) arranged in 

either a rear-projection or front-projection configuration. Projection displays (such as those 

already deployed in the APG and ALC visualization laboratories) can be prohibitively costly, 

and therefore, were not seriously considered as part of the commodity display solution. Tile 

displays use typical desktop monitor technology, and the delivery cost is significantly less than 

the projection-display solution. Of course, the tile display creates a visual grid due to the bezel 

edge around the flat-panel display which can be a distraction to the viewer. However, it is 

thought that once the end user has some experience with viewing data on the tile display, the 

visual grid will become less of a distraction. 

The tile display itself would be made up of standard desktop flat-panel monitors, precisely wallmounted in a 3 × 3 tile grid. Care was taken in selecting the flat-panel monitors in order to 

achieve the best possible results. Monitors with the smallest bezel (trim, also referred to as a 

mullion) would be used to minimize the distance between display frames. Open-source packages 

such as Distributed Multihead X (DMX), Blockbuster, and Chromium would also be used as a 

basis for demonstrating the viability of using a commodity cluster to visualize ARL MSRC data 

on a tile display. 

3. Relative Performance of Desktop Workstations 

It is generally recognized that modern commodity graphics cards provide exceptional 

performance, and may possibly outperform the traditional workhorse graphics systems offered 

by specialized vendors. Published benchmarks are often times measured under ideal conditions 

on complementary data sets and can therefore be misleading, so we decided to measure 

performance based on an actual application in use at ARL. The results presented in the 

following charts represent an effort to compare existing graphics workstations using a standard 

benchmark. The platforms tested included a wide variety of existing SGI desktop workstations 

and visualization servers, along with a number of different commodity-based workstations, both 

Linux and Windows. The benchmark run was a standard-sized data set using performance- 
7

monitoring tools available in Ensight.* Ensight is a commercial, multipurpose graphics package 

used on a variety of HPC-related applications, primarily in the area of computational structural 

mechanics and computational fluid dynamics. 

The purpose of performing these benchmarks was to give a relative performance value to 

existing, common workstations used by computational scientists at ARL. This is not an absolute 

performance value as there are many different types of graphics applications and each has unique 

characteristics which determine overall performance. However, we hoped to find a rend in 

performance which would indicate the relative value of commodity machines versus the longused SGI workstations. 

The benchmark consists of running five tests from an Ensight command file and is designed to 

stress the system’s graphics, memory and CPU.† The initial geometry contains 5,324 quads and 

128,500 triangles which are duplicated six times, giving a total number of polygons in the test of 

37,944 quads and 771,204 triangles. The test images are displayed in a 600 × 500 pixel window. 

These sizes are typical of data sets routinely visualized by our users. 

The individual tests consist of the following: 

1. Line-drawing test –360° rotation in 12° increments giving 30 screen refreshes. Generates a 

total of 73-million lines. 

2. Shaded test – 360° rotation in 12° increments giving 30 screen refreshes. Generates 24- 

million polygons with each part being a single color. 

3. Shaded test – same as test no. 2, but with parts colored on a per-vertex basis. 

4. Shaded test – same as test no. 3, but rendered in immediate mode (as opposed to 

display-list mode in the previous tests). 

5. Transparent test – same as test no. 4, but opacity is applied to the parts. This test stresses 

both the CPU and memory of the system in addition to the graphics. The scene is rotated 

72° and generates five frames. 

A composite score for each platform was computed by using the frame rate and weighing the 

importance of each of the tests. The composite score is calculated as follows: 

 C = 0.25*(30/T1) + 0.2*(30/T2) + 0.2*(30/T3) + 0.2*(30/T4) + 0.15*(5/T5). (1) 

 *

 Ensight and Ensight/Gold are registered trademarks of Computational Engineering, Inc. 

†

 Description of the benchmark tests is paraphrased from the README file associated with the benchmark command files 

provided by Computational Engineering, Inc. (CEI), the manufacturer of Ensight. 
8

The results of the benchmark study are presented in table 1. The SGI-based graphics technology 

was demonstrated to be significantly slower than the newer commodity-based desktop 

workstations in a real-world application that is used daily at the ARL MSRC. The graphs 

included below represent the results obtained. Figure 5 represents a comparison of all platforms 

for all five tests. Figure 6 compares the results of the best performing SGI vs. all of the tested 

commodity machines. Figure 7 graphs the results of the weighted composite score. 

Table 1. Results of each of the five tests of the benchmark. Value is time (seconds). 

Platform 1 2 3 4 5 Composite 

SGI - O2 104 105 273 262 407 0.17598 

SGI - Indigo2 High Impact 116 71 92 95 143 0.282782 

SGI - Indigo2 Max Impact 78 46 85 56 147 0.409422 

SGI - Onyx 2 - IR2 (Crab) 36 26 44 30 43 0.792908 

SGI - Onyx 2 - IR2 (Clam) 33 16 20 29 65 1.120708 

SGI - Onyx 3 - IR3 (Sponge) 18 13 16 18 43 1.60398 

SGI - Octane V8 22.45 8.45 13.62 37.09 328.37 1.648716 

SGI - Fuel V12 18.18 7.23 8.12 13.89 112.88 2.419943 

Nvidia Quadro4 700 (Windows2K, 2.2 GHz) 4.59 5.96 7.25 11.32 37.17 4.018497 

Fire-GL X1 (Windows 2K, 2.8 GHz) 3.23 5.625 7.968 8.625 29.15 4.863041 

Radeon 9600 (Mac G5, 2 GHz) 3.36 4.76 4.9 15.53 50.53 5.118329 

Nvidia FX3000 (64-bit Linux, 2.1 GHz) 3.33 4.74 6.34 6.29 21.53 5.453177 

Nvidia FX1300 PCI-Express (64bit, 2.4 GHz) 3.3 3.56 4.63 5.69 26.09 6.337245 

Fire-GL X2 (32-bit Linux, 1.4 GHz) 2.8 2.9 2.9 11.4 65.5 7.354269 

Nvidia FX3400 PCI-Express (64bit, 2.4 GHz) 2.63 2.92 2.95 6.04 16.98 7.977951 

Ensight Benchmark Results

1

10

100

1000

12345

Test Number

Time (Seconds)

SGI - O2

SGI - Indigo2 High Impact

SGI - Indigo2 Max Impact 

SGI - Onyx 2 - IR2 (Crab)

SGI - Onyx 2 - IR2 (Clam)

SGI - Onyx 3 - IR3

(Sponge)

SGI - Fuel V12

Nvidia Quadro4 700

(Windows2K, 2.2Ghz)

Fire-GL X1 (Windows 2K,

2.8Ghz)

Radeon 9600 (Mac G5,

2Ghz)

Nvidia FX-3000 (64-bit

Linux, 2.1Ghz)

Nvidia FX 1300 PCIExpress (64bit, 2.4Ghz)

Fire-GL X2 (32-bit Linux,

1.4Ghz)

Nvidia FX 3400 PCIExpress (64bit, 2.6Ghz)

Figure 5. Benchmark results. 
9

Best SGI Performance vs. Commodity Machines

0

20

40

60

80

100

120

12345

Test

Time

SGI - Fuel V12

Nvidia Quadro4 700 (Windows2K, 2.2Ghz)

Fire-GL X1 (Windows 2K, 2.8Ghz)

Radeon 9600 (Mac G5, 2Ghz)

Nvidia FX-3000 (64-bit Linux, 2.1Ghz)

Nvidia FX 1300 PCI-Express (64bit, 2.4Ghz)

Fire-GL X2 (32-bit Linux, 1.4Ghz)

Nvidia FX 3400 PCI-Express (64bit, 2.6Ghz)

Figure 6. Best SGI performance vs. commodity workstations. 

Ensight Benchmarks - Weighted Composite Score

0

1

2

3

4

5

6

7

8

9

Graphics Platform

Performance

Figure 7. Weighted composite score. 
10

4. Configuration 

We believe that these results demonstrate that commodity-based workstations perform quite 

favorably compared to the traditional scientific workstation, thus justifying our initiative to base 

the high-resolution tile display on these workstations. Therefore, we determined that the 

commodity tile display would be driven by a 9-node Linux cluster. Each node consists of dual 

64-bit Opteron processors, 4 gigabytes of system memory, a pair of 140-gigabyte disk drives, 

and an nVidia Quadro*

 FX-3000G graphics card. We had hoped to minimize the amount of rack 

space required to house the cluster by using ultra-slim “blade” systems. However, due to 

restrictions with the configuration of the graphics card with the accelerated graphics port, it was 

necessary to build each node of the cluster using traditional computer towers (figure 8) that are 

typically associated with desktop computer systems. The FX3000G graphics card offers dual 

digital video interface (DVI) output that was put to use in the configuration of the system. DVID cables are used to connect the output from the graphics card to the flat panel monitors. Also, a 

keyboard-video-mouse (KVM) switch was implemented, allowing all nine of the Linux 

workstations to share a single keyboard, mouse, and secondary monitor, providing convenient 

access by the end users to all of the systems (figure 9). The graphics driver associated with the 

FX3000G provides extensive capabilities to tune and configure performance. In this scenario, 

we configured to ports so that they were cloned, that is, the exact output was sent to each DVI 

interface, therefore displaying the same image on the flat panel monitor and through the KVM 

switch. (See appendix A for an example of this configuration file.) 

The most complex issue was the design and construction of the tile display itself. It was 

important to identify a high-resolution, flat-panel monitor that had the small mullion to reduce 

the physical space between the displays on each monitor. The team was also concerned about 

the ability to mount the displays in such a way as to allow for minor adjustments so that the 

monitors lined up properly, minimizing any gaps and alignment issues. After reviewing design 

and mounting methodologies utilized by other groups who had built tiled displays, it was 

determined that a framework of extruded aluminum would be deployed. This solution was low 

cost, easily obtained and installed, and provided a sturdy mounting mechanism. This framework 

would be flush mounted against the wall (figure 10), and the monitor mounts would be secured 

to the aluminum frame. (See appendix B for a diagram of the monitor mounts.) 

 *

 Quadro is a registered trademark of nVidia Corporation. 
11

Figure 8. Opteron-based commodity cluster used to drive tile display. 
12

Figure 9. FX3000G Dual-DVI Interface configuration.

Figure 10. Extruded aluminum frame flush mounted to wall with monitors mounted. 
13

It turned out to be very challenging to properly align the monitors. Despite the extensive efforts 

made to design a mounting mechanism that provided maximum adjustability, it was still very 

difficult to get a perfect alignment. Members of the ARL Weapons Material Branch, which 

includes a team of skilled carpenters and machinists, were brought in to brainstorm on ideas to 

resolve the alignment problem. A customized turnbuckle (figure 11) was designed and 

manufactured that would be mounted behind each monitor at the corners to provide a fine-tuning 

adjustment. The completed monitor layout can be seen in figure 12, and the final dressed 

configuration can be seen in figure 13. 

Figure 11. Custom turnbuckle used to align monitors. 

The final configuration provides a display of more than 20-million pixels, addressable using a 

number of applications. The commercial package Ensight/Gold has extensive capabilities to 

drive numerous types of advance displays, including tile displays and virtual reality 

environments, complete with head tracking and 6-degree-of-freedom input devices. Open 

sources packages, such as Paraview and Blockbuster, provide interfaces design specifically for 

support of a distributed environment required to compute and display in a tile-display 

environment. Each of these packages has been used to visualize data on the tile display. 

Paraview and Ensight provide a real-time interactive environment that allows the end users to 

manipulate the displayed geometry through rotation and translation, step through time dependent 

data, and create items of interest such as isosurfaces, particle traces, and clip planes. Blockbuster 

is more of a video playback mechanism; however, it uses parallel computing technology to allow 

for very high resolution animation playback, along with image-manipulation tools such as 

panning and zooming of the running animation. 
14

Figure 12. Monitor layout with exposed cabling. 

There are issues associated with commodity cluster visualization that require continued attention, 

such as data locality and input/output (I/O) performance. To get optimal performance, the data 

needs to be located on the same node that is processing the data; however, moving data around to 

each node is often very cumbersome. Using a network shared file partition reduces the 

requirement for moving data to the local compute node, however, provides significantly reduced 

performance as each node is competing for read access to the same disk partition. We are 

currently evaluating the use of open source software Parallel Virtual Filesystem (PVFS2) as a 

methodology that combines the best of both worlds — a global file system that provides local 

I/O performance. We continue to pursue alternatives that will provide high-bandwidth 

performance to all nodes in the cluster. 

Operationally, there are also issues that need to be addressed. The heat load associated with 

rack-mounted commodity computers is significant. If the cluster rack is located in an area that 

does not have a cooling system specifically designed for a computer facility, there may not be 

adequate cooling provided by normal building air conditioning. The commodity computers do 

have a high tolerance for heat, but there is concern about the long-term affect of heat on the 

longevity of these systems. Also, the noise generated by this cluster system may not be 

acceptable for location in an office work area. 
15

Figure 13. Completed tile display. 
16

5. Performance of the Tile Display Cluster 

We have demonstrated that the tile display and commodity workstation cluster can be assembled 

and enabled in such a way as to make a cohesive computational system. But, does the system 

provide capabilities that extend the value of scientific visualization? The evaluation of the 

overall performance of the cluster display wall is somewhat subjective. One way to measure the 

performance is to execute some simple timing tests on an application that is used on the tile 

display. For this measurement, an open-source utility from Lawrence Livermore National 

Laboratory called Blockbuster was used. Blockbuster reads image information from single 

media file and plays back the animation, and supports playback to both a single workstation 

display and to the tile display using distributed rendering applications under DMX. 

The source file was a 600-frame animation rendered at a resolution of 2400 × 1200 pixels. In 

order to display this animation on a single, large CRT display, Blockbuster reduced the size of 

the images on the fly by one-third in order to fit it on the display screen, with a resulting 

resolution of approximately 1600 × 800 pixels. Running on a single workstation to a single 

display, blockbuster averaged a frame rate of ~6 frames per second, or ~7.6-million pixels per 

second. This frame rate is obtained simply taking the total amount of pixels display divided by 

the amount of time to process all 600 frames. From the observer’s standpoint, the performance 

did not provide a visually smooth, consistent playback. 

Using nine Linux workstations displaying to a 3 × 3 tile display, the performance is significantly 

increased. The resolution of the displayed image was 5097 × 3939 pixels, requiring Blockbuster 

to increase the resolution of the image by a factor of three in order to fill the display area. 

Blockbuster, in conjunction with the companion application DMX, runs in a client/server mode, 

with a master instance of Blockbuster running on a front-end workstation, and instances of a 

Blockbuster server and DMX running on each compute node. If we look strictly at the amount 

of time to start up Blockbuster and DMX, and display 600 frames of the animation, the resulting 

performance of 6 frames per second is equal to the overall performance of the single-node, 

single-display instance. But, in the distributed case, we are pushing an average of 120-million 

pixels per second to support the higher-resolution display. 

It should be noted that there is an initially significant overhead associated with starting up both 

DMX and Blockbuster in this configuration. However, once the distributed application has been 

established on each node, there is a considerable increase in performance as viewed by the 

observer. Once running in the distributed environment, Blockbuster actually performs at about 

12 frames per second, which is twice as fast as the single workstation performance, averaging 

about 240-million pixels of information per second. From the observer’s standpoint, the 

animation is significantly larger, and runs quite smoothly when compared to the jittery results 

obtained from the single workstation playback. 
17

6. Conclusion 

This report presents a methodology for developing a high-resolution tile display based on 

commodity computing components. We successfully built a high-resolution tile display utilizing 

commodity display components, standard, off-the-shelf computer technology, and a variety of 

open sources and proprietary software packages. 

Computational scientists are generating enormous data sets on the HPC resources at the ARL 

MSRC, and it is impractical to attempt to display these data sets in a compressed manner on a 

traditional computer monitor that will only show 1-million pixels of data. The graphics 

subsystems on many of today’s desktop computers can easily outperform the visual 

supercomputers purchased only a few years ago, allowing scientists to perform complex 

visualizations at their desktop, visualizations that until now could only be performed in a stateof-the-art centralized visualization laboratory. The use of tile-display technology, along with a 

commodity computer cluster, will allow departmental groups to provide a facility to view highfidelity representations of data, perform real-time analysis on very large data sets, and display the 

results of their computations to their peers. 
18

7. References 

1. Frank, R. J. On the Use of Commodity Clusters for Large-Scale Scientific Application;

Presentation, Commodity Cluster Symposium, U.S. Department of Energy, Lawrence 

Livermore National Laboratory: Livermore, CA, July 2003. 

2. Moreland, K. Large Scale Visualization with Cluster Computing; presentation, Linux 

Cluster Institute Workshop, U.S. Department of Energy, Sandia National Laboratory: 

Albuquerque, NM, October 2004. 

3. Shalf, J.; Bethel, W. E.; Siegerist, C. Personal Display Wall. LBNL-PUB-894; Lawrence 

Berkeley National Laboratory: Berkeley, CA, 2003. 

4. Clarke, J.; Vines, J.; Mark, E. An Augmented Virtual Reality Scientific Data Center; ARLTN-229; U.S. Army Research Laboratory: Aberdeen Proving Ground, MD, September 

2004. 
19

Appendix A. XF86Config File 

 

 This appendix appears in its original form, without editorial change. 
20

# This config file works for using both the DELL and Samsung monitors. 

# The Dell is connected in the left-side DVI connector on the back of the computer. 

# The Samsung is connected to the right-side HD-15 converter/connector. 

# Created by Rick Angelini - July 26, 2004. 

# 

# XFree86 4 configuration created by redhat-config-xfree86 

Section "ServerLayout" 

 Identifier "Default Layout" 

 Screen 0 "Screen0" 0 0 

 InputDevice "Mouse0" "CorePointer" 

 InputDevice "Keyboard0" "CoreKeyboard" 

 InputDevice "DevInputMice" "AlwaysCore" 

EndSection 

Section "Files" 

# RgbPath is the location of the RGB database. Note, this is the name of the 

# file minus the extension (like ".txt" or ".db"). There is normally 

# no need to change the default. 

# Multiple FontPath entries are allowed (they are concatenated together) 

# By default, Red Hat 6.0 and later now use a font server independent of 

# the X server to render fonts. 

 RgbPath "/usr/X11R6/lib/X11/rgb" 

 FontPath "unix/:7100" 

EndSection 

Section "Module" 

 Load "dbe" 

 Load "extmod" 

 Load "fbdevhw" 

 Load "glx" 

# Load "record" 

 Load "freetype" 

 Load "type1" 

# Load "dri" 

EndSection 

Section "InputDevice" 

# Specify which keyboard LEDs can be user-controlled (eg, with xset(1)) 

# Option "Xleds" "1 2 3" 

# To disable the XKEYBOARD extension, uncomment XkbDisable. 

# Option "XkbDisable" 

# To customise the XKB settings to suit your keyboard, modify the 

# lines below (which are the defaults). For example, for a non-U.S. 
21

# keyboard, you will probably want to use: 

# Option "XkbModel" "pc102" 

# If you have a US Microsoft Natural keyboard, you can use: 

# Option "XkbModel" "microsoft" 

# 

# Then to change the language, change the Layout setting. 

# For example, a german layout can be obtained with: 

# Option "XkbLayout" "de" 

# or: 

# Option "XkbLayout" "de" 

# Option "XkbVariant" "nodeadkeys" 

# 

# If you'd like to switch the positions of your capslock and 

# control keys, use: 

# Option "XkbOptions" "ctrl:swapcaps" 

# Or if you just want both to be control, use: 

# Option "XkbOptions" "ctrl:nocaps" 

# 

 Identifier "Keyboard0" 

 Driver "keyboard" 

 Option "XkbRules" "xfree86" 

 Option "XkbModel" "pc105" 

 Option "XkbLayout" "us" 

EndSection 

Section "InputDevice" 

 Identifier "Mouse0" 

 Driver "mouse" 

 Option "Protocol" "IMPS/2" 

 Option "Device" "/dev/psaux" 

 Option "ZAxisMapping" "4 5" 

 Option "Emulate3Buttons" "yes" 

EndSection 

Section "InputDevice" 

# If the normal CorePointer mouse is not a USB mouse then 

# this input device can be used in AlwaysCore mode to let you 

# also use USB mice at the same time. 

 Identifier "DevInputMice" 

 Driver "mouse" 

 Option "Protocol" "IMPS/2" 

 Option "Device" "/dev/input/mice" 

 Option "ZAxisMapping" "4 5" 

 Option "Emulate3Buttons" "no" 

EndSection 
22

Section "Monitor" 

 Identifier "Monitor0" 

 VendorName "Monitor Vendor" 

 ModelName "Dell 2001FP" 

 HorizSync 31.0 - 80.0 

 VertRefresh 56.0 - 76.0 

 Option "dpms" 

EndSection 

Section "Monitor" 

 Identifier "Monitor1" 

 VendorName "Monitor Vendor" 

 ModelName "LCD Panel 1920x1200' 

 HorizSync 63.8 

 VertRefresh 59.8 

 Option "dpms" 

EndSection 

Section "Device" 

 Identifier "Videocard0" 

 Driver "nvidia" 

 VendorName "Videocard vendor" 

 BoardName "NVIDIA Quadro FX (generic)" 

 VideoRam 131072 

 Option "NvAGP" "3" 

 Option "TwinView" 

 Option "TwinViewOrientation" "clone" 

 Option "SecondMonitorVertRefresh" "56.0 - 85.0" 

 Option "SecondMonitorHorizSync" "30.0 - 81.0" 

# Option "HorizSync" "CRT-1: 30.0 - 96.0 , DFP-0: 31.5 - 90.0" 

# Option "VertRefresh" "CRT-1: 48.0 - 160.0 , DFP-0: 60.0 - 60.0" 

 Option "Metamodes" "CRT-1: 1600x1200, DFP-0: 1600x1200" 

EndSection 

Section "Screen" 

 Identifier "Screen0" 

 Device "Videocard0" 

 Monitor "Monitor0" 

 DefaultDepth 24 

 SubSection "Display" 

 Depth 24 

 Modes "1600x1200" "1400x1050" "1280x1024" "1280x960" "1024x768" 

"800x600" "640x480" "1920x1440" 

 EndSubSection 

EndSection 
23

Section "Screen" 

 Identifier "Screen1" 

 Device "Videocard0" 

 Monitor "Monitor1" 

 DefaultDepth 24 

 SubSection "Display" 

 Depth 24 

 Modes "1600x1200" "1280x1024" "1280x960" "1152x864" "1024x768" "800x600! 

 EndSubSection 

EndSection 

Section "DRI" 

 Group 0 

 Mode 0666 

EndSection 
24

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25

Appendix B. Draper Flat Panel Mounting Bracket 
NO. OF 

COPIES ORGANIZATION 

26

 1 DEFENSE TECHNICAL 

 (PDF INFORMATION CTR 

 ONLY) DTIC OCA 

 8725 JOHN J KINGMAN RD 

 STE 0944 

 FORT BELVOIR VA 22060-6218 

 1 US ARMY RSRCH DEV & 

 ENGRG CMD 

 SYSTEMS OF SYSTEMS 

 INTEGRATION 

 AMSRD SS T 

 6000 6TH ST STE 100 

 FORT BELVOIR VA 22060-5608 

 1 INST FOR ADVNCD TCHNLGY 

 THE UNIV OF TEXAS 

 AT AUSTIN 

 3925 W BRAKER LN 

 AUSTIN TX 78759-5316 

 1 DIRECTOR 

 US ARMY RESEARCH LAB 

 IMNE ALC IMS 

 2800 POWDER MILL RD 

 ADELPHI MD 20783-1197 

 3 DIRECTOR 

 US ARMY RESEARCH LAB 

 AMSRD ARL CI OK TL 

 2800 POWDER MILL RD 

 ADELPHI MD 20783-1197 

 3 DIRECTOR 

 US ARMY RESEARCH LAB 

 AMSRD ARL CS IS T 

 2800 POWDER MILL RD 

 ADELPHI MD 20783-1197 

 ABERDEEN PROVING GROUND

 1 DIR USARL 

 AMSRD ARL CI OK TP (BLDG 4600) 
NO. OF 

COPIES ORGANIZATION 

27

 2 RAYTHEON SYS 

 M BOLSTAD 

 J RENTERIA 

 939 I BEARDS HILL RD 

 PMB 191 

 ABERDEEN MD 21001 

 1 MISSISSIPPI STATE UNIV 

 R MOORHEAD 

 PO BOX 9627 

 MISSISSIPPI STATE MS 

 39762-9627 

 1 AERONAUTICAL SYS CTR 

 R VICKERY 

 HIGH PERFORMANCE COMPUTING BR 

 ASC HP BLDG 676 2435 FIFTH ST 

 WPAFB OH 45433-7802 

 ABERDEEN PROVING GROUND

 13 DIR USARL 

 AMSRD ARL CI 

 C NIETUBICZ 

 AMSRD ARL CI HC 

 R ANGELINI 

 J CLARKE 

 K KIRK 

 E MARK 

 R NAMBURU 

 R SCHUMEYER 

 J VINES 

 W TAUBER 

 AMSRD ARL WM TA 

 B DONEY 

 P KINGMAN 

 AMSRD ARL WM TC 

 K KIMSEY 

 S SCHRAML 
28

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