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Evolution of Graphic Card ---Development of Graphic Card since
2006
Jia Ming Simon Ma
CMPS 161
Final Project
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Table of Contents
Abstract ---------------------- -------------------------------------------------------- 3 ~ 4
Introduction -------------------------------------------------------------------------- 4~6
How to Use------------------------------------------------------------------------------ 6
Benchmark Software --------------------------------------------------------------- 7~9
Discussion ----------------------------------------------------------------------------- 9~10
Initial Assumption --------------------------------------------------------------------- 10
Result --------------------------------------------------------------------------------- 11~14
Conclusion --------------------------------------------------------------------------- 15~16
Reference ---------------------------------------------------------------------------- 16~ 17
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Abstract
The graphic card, or precisely the GPU, was not popularized until 1999. Before
that, CPU was responsible to process most of the data. However as the digital graphics
spreads like wild fire, a processing unit specifically for graphic is necessary. This is
because the modern CPUs strongly favor lower latency of operations with clock cycles in
the nanoseconds. The trade-off area where our CPUs were not performing well was that
of massive fine grain parallelism [1]. In other words, CPUs focus on the throughput of the
pipelines, but not an individual operation. Therefore, CPU yielded rather inefficient
system for graphics processing any more.
On the other hand, GPUs are specifically designed to execute literally billions for
small user-written programs per second [1]. That means GPUs can handle the
complicated graphical data with a much faster speed comparing to CPU.
Unfortunately, it seems that CPU technology has been near the bottleneck now.
Due to the limitation of semi-conductor, people start building a CPU with more cores
instead of having a higher clock speed. As the result of this, the improvement is no
longer obvious. On the other side, the GPU technology hasn’t met the bottleneck yet, so
it still has a lot room to develop.
Because of this, GPU starts sharing the normal data processing with CPU. For
example, the CUDA technology from NVIDIA is a parallel computing architecture. It
instructs the computer to “co-process” on the CPU and GPU [2]. Based on this
technology, the processing speed is increased more than 100 times faster as figure 1
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shown [2]. So,
GPUs are not
only a graphical
processing unit,
but also an
important part of the central processing pipeline now.
The two major GPU producers in nowadays are NVIDIA and AMD (formerly ATI).
Nvidia marketed the “GeForce 256”, which is labeled as the “the world’s first GPU” in
1999, while its rival ATI Technologies announced the term VPU (visual processing unit)
with “Radeon 9700” in 2002. Since then, the endless war between NVIDIA and ATI (now
AMD graphic) has officially begun.
Introduction
This project is going to visualize the development of desktop graphic card since
2006. The program is going to analyze and represent the architecture of the GPU, the
price, and the performance in several graphs. Users are able to control what elements
to show on the graph by control panel.
Figure 2: nVidia, ATI, and AMD graphic logo
Figure 1: Processing time [2]
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There are three types of graphs. The
first one is regular X-Y Axis 2D scatter plot with
changeable Y axis as figure 3. The y axis could
be transistors count, memory clock rate,
fabrication process, fillrate, power
consumption (known as TDP), or release price.
The X axis is the timeline from 2006 to 2012.
The second type of graph is a
performance benchmark score chart. This
chart is a line graph for 2 brands, 6 categories of graphic
cards. The categories include entry-level graphic, mid-range graphic, and high-end
graphic for both AMD and nVidia.
The third type of graph is a
Performance/Price Ratio Chart based on time and
release price as figure 4 shown. The X axis is time
from 2006 to 2012, and Y axis is the release Price.
The size of each square represents the
performance/price ratio. In other word, the more
valuable the graphic card is, its square will be
bigger. Also, all graphic cards will be colored
based on their brand and targeting market.
Figure 3: scatter plot
Figure 4: P/P ratio chart
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This project is intended to help users to find the best selection of graphic cards.
More importantly, users are able to visualize the history, and predict the future of GPU
based on the trend.
How to Use
This program REQUIRES: FLTK library (v 1.3 tested), GLUT library, C++ compiler
(Visual C++ 2011 tested).
1) Press “Click here to start” on the menu bar, click “Select data” to open a file
browser. Select the data file which should be named as “data.txt”. After loading
the data, “Finish reading data” should be displayed in the window.
2) Press the corresponding button to display the chart you want. For the first chart
“scatter plot of specifications”, select what Y axis represents with the dropdown
menu, Press “Apply” button to display. For other two graph,
just Press the button and the chart will be displayed.
3) For all three charts, the color mapping is located on the top of each chart.
4) For price/performance ratio chart”, user can click on the window and the
information of those video cards in that region will be displayed on the screen.
Press the mouse again to restore the chart.
Benchmark Software
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Figure 6: PassMark screenshot[4]
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I used three different benchmark score in order to represent a all-around
performance for different rendering situation.
PassMark Video Benchmarks :
PassMark video card
benchmarks has been designed to
benchmark the how well your video
card performs when using the most
common features of DirectX. It renders
a number of scenes to the screen in
windowed or full screen mode. As such, Performance Test requires DirectX version 9 or
above. With an extra DirectX 10 test being available if you have Vista and a DirectX10
compatible video card[4]. This benchmark has three different tests, including simple text,
medium test, and complex test.
This program focus on Vertex Shader 2.0, Pixel Shader 2.0, and other OpenGL
effect. This is a representation of basic rendering perfromance.
3DMARK 06 :
3DMark06 is a benchmark from Futuremark.
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3DMark06 is a PC
benchmark suite designed
to test the DirectX9
performance of your
graphics card. A 3DMark
score is an overall measure
of your system's 3D gaming
capabilities, based on
comprehensive real-time 3D graphics and processor tests. By comparing your score with
those submitted by millions of other gamers you can see how your gaming rig performs,
making it easier to choose the most effective upgrades or finding other ways to optimize
your system[5].
This benchmark shows the ability to process game/software based on DirectX 9.0
as well as SM2.0/ SM3.0. This is a representation for last generation game performance.
3DMARK 11 :
A newer version of
3DMARK 06 which support
newest DirectX 11.
This software is
designed to measure your PC’s
gaming performance 3DMark 11
Figure 8 :3DMARK 11[6]
Figure 7: 3DMARK 06[5]
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makes extensive use of all the new features in DirectX 11 including tessellation, compute
shaders and multi-threading. Trusted by gamers worldwide to give accurate and unbiased
results, 3DMark 11 is the best way to consistently and reliably test DirectX 11 under game-like
loads[6].
This benchmark focus on the future compatibility and DirectX 11 rendering ability. It is
the representation of new game performance.
Discussion
My goal is to analyze the history of graphic card development, and predict the
future. So, the fabrication process is a great representation of "developing". GPU has a
very similar developing history with CPU, so it is normal to assume the GPU will meet
the bottleneck of developing due to the limitation of fabrication process.
When the fabrication was still high, people could try to integrate more
transistors into one chip in order to achieve better processing power. So they lower the
fabrication process, and put more and more transistors into one chip that has the same
size.
So the transistors number is another critical component for measuring and
estimating the power of a GPU. I assume the transistors count has been increasing since
several years ago, and it will still go up because of its direct relation with performance.
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On the other hand, memory clock affects the processing ability. Besides the GPU,
memory controls the output thread, so some companies start to boost up the memory
clock for faster processing speed. I can assume that the memory clock has been
increased a lot.
The price of video cards are always categorized into certain levels. So it is
suppose to be equally spread along time.
For the performance measurement, how could we get the overall performance
score with three benchmark score? I thing we shall focus on the "sustainability" of a
graphic. In other words, a graphic card that support newest technology shall have higher
score since it will still perform well in the future. Some high-end last generation graphic
card can still get high score due to its high core clock and memory clock. But its actual
game performance may be worse because they do not support the new technology
which most future games may require.
As the result of this, I give 3DMARK 11 score 50% weight. 3DMARK06 and
PassMark shares the other 50% of score. I think this score reflects the future of a video
card, but also show the ability to handle old game and software.
Initial Assumption
I assume the data set is already sorted into old released date to new released
date, and the first part has nVidia video card only while the second part has AMD only.
Figure 10:Memory Clock
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Result
The first graph is the transistors
count chart(figure 9). Obviously, the
overall trend of transistors number
increment is proportional to time. Also,
we can see the difference between last
year's top model and this year's top
model is really huge. The main stream is
increasing with a rate of 500 to 600 transistors per year.
The memory clock chart shows
an unexpected result. The memory clock
of AMD's produces do not change a lot.
On the other side, nVidia increases their
clock rate every 2 years for a better
performance. It seems that AMD does
not focus on memory overclocking
comparing to nVidia. NVidia is increasing
their memory clock by around 100% every two years.
Figure 9 :Transistors
Figure 10:fab process
Figure 11: fillrate
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This is the fabrication
process chart. As my expect, the
fabrication development has been
really fast from 2006 to 2008. It
takes only 2 year to step up from 90
nm to 55 nm. However, it remains
the same since 2009. This is caused
by the difficulty of solving physical
problem of semi-conductor. Also,
AMD is always leading this
improvement.
The fillrate is another
representation of performance. The
higher the fillrate is, the better the
performance is. From the chart, we
can see the fillrate has similar trend as
transistors,
with roughly 20 to 30 more
GigaTexture/s for one year.
Figure 12: TDP
Figure 13: Price
Figure 14: Performance
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The power consumption shows how much energy is needed for each graphic
card. Normally, a faster video card requires more energy. We can still see the top
models have obvious upward trend, but the mainstream of power consumption does
not increase a lot due to the increment of transistors number. Because of the new
power management system and the more effective components, the power supply unit
of GPU need less power to power up same amount of transistors. Therefore, even
though the total amount of transistors increased exponentially, the net power
consumption remains at a reasonable level.
The price scatter plot is equally
spread into different price range as I
expect.
The Performance score line
chart show the trend of performance
increment. We can see the total
performance is increasing by 30% to
Figure 15: P/P
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40% per year. Referring to the price chart above, the price of each group of video card is
relatively stable. This cause the new video cards are always better and more valuable
than the previous generation.
The last chart is
Performance/Price Ratio chart. It
also shows a obvious trend.
As I observe in the
performance chart, the new video
card are more valuable than old
video cards. Especially for low mid-range to high entry-level video cards from both
companies, they provide a great performance/price ratio for normal daily users. As we
can see, the big performance/price ratio products are concentrated at the bottom-right
corner.
Another fact is that high-end and mid-range video cards are more valuable in
long-term condition. For those video cards before 2008, the entry-level product are not
able to keep their performance because of the limitation of hardware even though they
had really high p/p ratio when they were released. Compare to this, the mid-range and
high-end video cards are able to handle the newer rendering duty due to its high clock
speed and better architecture even though they do not support the new APIs or
languages.
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Conclusion
The first problem, which group of video cards are most valuable for us?
NVidia cooperates with a lot of gaming companies, so their video cards are
optimized for many games; AMD is far behind. From the graph, nVidia mid-range video
cards' performance are really close to AMD's high-end. More importantly, its price is
much lower than any of the high-end video card. So a mid-range nVidia video card
(gtx560) is the best buy for normal gamers with limited budget. Of course, AMD's mid-
ranges are also great, but they are lack of pre-optimization by game companies. If you
really want to buy a computer and will not upgrade it for 4 to 5 years, you shall buy an
AMD high-end video card (hd6970 or hd6990) to maximize the long-term performance.
The second question is to predict the future of GPU industry.
I have read a article "End of Moore's Law". It stated that computers are going to
be as fast as it is physically possible for them to be. Of course, we can still make more,
but they will take more space, time, and power, and will cost money, which, though at a
minimum, cannot be reduced any further[7]. This is the problem, and it is already
reflected into the slowness of fabrication process development in recent years.
I believe computers will continue to get faster, for a while, after the hardware
stops getting faster. But the incentives and focus will shift. They starts to put more GPU
on one board for faster performance, and it works for now (the top video cards with
most fillrate have been dominated by dual-core video cards since 2008). But the power
consumption and heat problem can't be solved easily. They also tried to get new
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technology to boost the GPU to run faster, but most of them are still in the research
process.
So, I think the companies will shift their focus from hardware to software; At the
same time, trying to innovate some new technology or material to get rid of the
bottleneck of Moore's Law.
Reference
[1]. "High Scalability - High Scalability - GPU vs CPU Smackdown : The Rise of Throughput-
Oriented Architectures." High Scalability. Web. 24 Feb. 2012.
<http://highscalability.com/blog/2010/12/3/gpu-vs-cpu-smackdown-the-rise-of-
throughput-oriented-archite.html>.
[2]. Laszweski, Gregor Von. "Towards Flow Cytometry Data Clustering on Graphics
Processing Units." By Jeremy Espenshade, Doug Roberts, and James Cavenaugh.
Rochester: Rochester Institute of Technology. Web. 24 Feb. 2012.
[3]. "NVIDIA Developer Zone." What Is CUDA. Web. 24 Feb. 2012.
<http://developer.nvidia.com/what-cuda>
[4]. PassMark CO. "Video Card Test Information." PassMark Software. Web. 16 Mar.
2012. <http://www.videocardbenchmark.net/gpu_test_info.html>.
[5]. "Futuremark." - World's Most Popular Benchmarks and PC Performance Tests. Web. 17
Mar. 2012. <http://www.futuremark.com/benchmarks/3dmark06/introduction/>.
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[6]. "3DMark 11 Video Card Benchmark for Windows 7 PC." 3DMark11 for Windows 7
DirectX 11 Benchmark Tests. Web. 17 Mar. 2012.
<http://www.3dmark.com/3dmark11/>.
[7]. "End Of Moores Law." Cunningham & Cunningham, Inc. 27 Dec. 2011. Web. 17 Mar.
2012. <http://c2.com/cgi/wiki?EndOfMooresLaw>.