Newsgroup: it.comp.hardware.video-3d
Titolo: ATI stream computing, x1950xtx 36 volte più veloce di un athlon64 in DP

WHILE ATI AND NVIDIA lock horns over the ultimate trophy in 3DMarks or game 
frame rates, another aspect of GPU performance has now gained recognition: 
its performance in FP-intensive non-GPU applications, from molecular 
simulation and financial modeling to object interaction physics.
Why move it to the GPU? Well, it can now do the job nearly as well as 
proprietary FPGA-based accelators at often just one-tenth of the cost. Also, 
64 bit pixel precision pipelines ultimately should lead to the full 64-bit 
FP processing capability.
Since standard code used for such apps envision a lot of branching and 
conditional program flow, ATI is at an advantage over Nvidia at present with 
its more complex architecture that provides for such applications. In some 
cases, the routines and datasets in question can comfortably fit within the 
GPU's on-board memory - but in others, you need all the memory you can get, 
if for instance operating on large arrays.
Now, enter HyperTransport 3.0 and a future ATIMD GPU connected directy to it 
via the Torrenza platform. It can still have its pool of dedicated fast, 
wide memory for graphics use, but at the same time, it can access the whole 
system memory at CPU-like speed via HT 3.0, at low latency and, if need be, 
cache coherency. In this case, this GPU would become a true coprocessor, in 
nearly the same fashion as the 8087 was to 8086 some 25 years ago.
Is it a coincidence then that ATI "Stream Computing" initiative, enabling 
ATI GPUs to work in concert with CPUs to solve complex computational 
problems, comes exactly at the time of them being acquired by AMD? After 
all, the scientists, engineers and financial wizards can hardly resist the ~ 
360 peak GFLOPs of an ATI X1950XTX, when a comparably priced Intel Core 2 
Duo E6700 at 2.66GHz gives you 'only' 42 peak GFLOPs in single precision, 
half that in double precision - and current Athlon64 top CPUs are half of 
the Intel figure.
Now you don't even need to go for sadomasohistic assembly language 
programming of your (non)GPU, if using PeakStream number crunching 
programming interface, providing plethora of GPU-based common math 
operations function calls for easier coding. The company's solution covers 
far more than the game physics, enabling you to use the R580 series GPUs as 
accelerated FPUs. Also, Stanford University has a new distributed computing 
application using the ATI GPUs for disease research computation - far from 
GPU intended use, isn't it?
FPGA and dedicated accelerators will be most directly affected - not only 
are these products far more expensive, but also more cumbersome and, for the 
users outside US / EU, they are often declared as controlled export items - 
unlike the plain vanilla GPUs.
Now, both R600 and G80 are expected to break the half teraflop peak 
processing power per chip - and, quite possibly, widen the range of apps 
able to be accelerated on them too. At the same time, there are no major 
per-core FPU performance boosts planned beyond what today's Intel Core2 and 
tomorrow's AMD K8L can do.
In summary, GPUs may be the trick in bringing the teraflop computing power 
to the desktop (SLI's G80 or Xfired R600 will surely do the trick - if the 
app uses it), and petaflop power to mid-sized supercomputing clusters - 
affordably. With Torrenza, AMD holds the advantage right now in implementing 
these early, unless Intel decides to give CSI to the X86 platform a bit 
earlier for tightly coupled co-processing - yet again after some 20 years.

"DefSys Wii60" <m...@telnet.cos> wrote in message 
news:4526...@newsgate.x-privat.org...
http://folding.stanford.edu/download.html
As promised last week, Stanford University has released a beta version of 
its Folding@home client that runs on ATI's Radeon X1900- and Radeon 
X1950-series graphics cards. The GPU client is available for download 
half-way down this page in either console, graphical, or graphical with 
"fancy graphics" versions for Windows XP. However, as Stanford's Beta FAQ 
page states, users need specific versions of ATI's Catalyst drivers and 
Microsoft's DirectX runtime in order to run the software. Only ATI's 
Catalyst 6.5 or 6.10 Beta drivers will work; the 6.6 and 6.7 releases 
reportedly suffer from major performance problems, and recent 6.8 and 6.9 
versions simply don't work with the GPU client at all. Users also need 
DirectX 9.0c version 4.09.0000.0904 or later installed. If you own a Radeon 
X1900-series graphics card and manage to get the beta GPU client working, 
don't forget to join team 2630!
http://techreport.com/etc/2006q4/stream-computing/index.x?pg=1
I SPENT LAST WEEK taking in information like a sponge, and this week, it's 
still slowly seeping out. My IDF wrap covered Intel's future directions in 
processor technology, which had a lot to do with highly parallel 
floating-point processing. Many of my conversations with folks other than 
Intel last week touched on this same theme-whether it was about ATI's stream 
computing initiative, Nvidia's plans to counter, or the battle over gaming 
physics. Read on for my take on ATI's foray into what it calls stream 
computing.
ATI's stream computing kickoff
Last Friday, ATI invited a number of journalists and analysts to a short but 
information-packed event devoted to its new stream computing initiative. ATI 
is using the phrase "stream computing" to refer to the class of applications 
more commonly referred to under the GPGPU label, an acronym which refers to 
general-purpose processing on a graphics processing unit. CEO Dave Orton 
explained that ATI chose the term stream computing because the class of 
computing problems the GPU handles well are primarily about data flow, a 
characteristic that separates these problems from the types of computation 
at which CPUs have traditionally excelled.
Orton identified a number of specific areas where ATI sees opportunities for 
GPUs to accelerate computation, including medical research, analysis of 
video and audio data for security applications (such as facial recognition), 
financial analysis, seismic modeling for oil and gas exploration, media 
search applications, physics simulations in video games, and media encoding.
CEO Dave Orton explains ATI's Stream computing initiative
In these areas, he said, the GPU has the potential to be "orders of 
magnitude" faster than CPUs due to its nature as a highly parallel 
floating-point processor. Orton pegged the floating-point power of today's 
top Radeon GPUs with 48 pixel shader processors at about 375 gigaflops, with 
64 GB/s of memory bandwidth. The next generation, he said, could potentially 
have 96 shader processors and will exceed half a teraflop of computing 
power.
Orton was quick to emphasize that ATI is not looking to compete directly 
with CPUs, just to find and address a set of problems that map especially 
well to the GPU. He described the CPU-GPU relationship as complementary and 
symbiotic. He also made it clear that the day's events were not part of a 
new product launch. ATI is just inaugurating a new direction in seeking out 
this business, he said, and showcasing some actual applications where the 
GPU has been fruitfully applied.
Much of the rest of the event was devoted to speakers who had actual stream 
computing applications to discuss or demo.
Folding@Radeon
Stanford's Vijay Pande talks Folding on a GPU
First among them was Vijay Pande of Stanford University, Professor of 
Chemistry and Director of the Folding@Home project. TR readers should be 
very much familiar with Folding, since we field one of the top ten Folding 
teams in the world. Pande was there to talk about the new beta Folding 
client that uses the GPU. Currently, it only runs on newer Radeons, where it 
shows big performance increases-between 20 and 40 times the speed of a CPU. 
Pande said the client is presently achieving around 100 gigaflops per GPU. 
To give some perspective, he then demonstrated the graphical versions of the 
CPU and GPU clients side by side, and the GPU version showed constant 
motion, while the CPU one chunked along at a few frames per second.
This particular implementation of stream computing has now gone live. The 
FAH project released the first beta of the client to the public earlier this 
week.
I talked with Pande about the possibility of a Folding client for Nvidia 
GPUs, and he had some interesting things to say. The Folding team has 
obviously been working with Nvidia, as well as ATI. In fact, Pande said 
Nvidia has their code and is running it internally. At present, though, 
ATI's GPUs are about eight times as fast as Nvidia's. He was hopeful Nvidia 
could close that gap, but noted that even a 4X gap is pretty large-and ATI 
is getting faster all of the time.
The bottom line for Pande and his colleagues, of course, is how Folding on a 
GPU can further research about diseases like Parkinson's and Alzheimer's. 
Pande characterized the move to GPU Folding as one that opens new 
possibilities. 

 and Radeon X1950-series graphics cards.
Funziona...
Peccato per il consumo di corrente veramente esagerato.
-- 
Ciao,
Luca
*** 
http://digilander.libero.it/luciferino_666/
 *** 

 interaction physics.
[cut]
posso solo dire....fantastico!
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 Peccato per il consumo di corrente veramente esagerato.
considerato che vai 36 volte più veloce di un athlon64, val la pena tenere 
accesa la radeon... pensa te come sarà possibile accellerare tutti i task FP 

 tutti i task FP
L'hardware corre troppo rispetto al software, l'annoso problema del PC è 
proprio che le potenzialità vengono sfruttate solo in piccola parte e sempre 
in ritardo.


 sempre in ritardo.
io intanto sogno una patch per photoshop che consenta di usare le 
potenzialità fp della radeon per i filtri....
invece di aspettare 3 secondi e mezzo, solo un decimo di secondo per 
filtro....
STUPENDO, speriamo lo implementino 


cazzarola sarebbe l'ideale, e per tutti i calcoli "multimediali" che alla 
fine son tutti in FP. Finalmente si potrà lavorare su file a 4800dpi :D
