If I recall correctly, a while ago AMD claimed world’s first OpenCL demo, but it was done on a single core (and then scaled up to all four) on a Phenom II X4 CPU. If this video is correct, Nvidia gets the pole position for being the first company to demonstrate OpenCL working on a GPU, which is “usage as intended”.

Judging from the video, Nvidia showed Nbody simulation changing following parameters: point size, velocity damping, softening factor, time step, cluster scale, and velocity scale. The company used a laptop equipped with Quadro FX 570M graphics card, e.g. GeForce 8600M after a “GL” tune-up. As far as the official debut of OpenCL go, KHRONOS Group launched the API on Siggraph Asia 2008 in Singapore, from where both AMD & Nvidia videos came from.

But don’t expect OpenCL drivers until both companies supply drivers to Apple. It looks like Snow Leopard will be the beginning of OpenCL on a PC platform and Windows Vista is sitting on backburner… that’s what Microsoft get for botching OS development and sucking up to allmighty Chipzilla.

The post Youtube video shows OpenCL running on Nvidia GPU appeared first on VR World.

Fast forward to January 2009, and we have SP performance at 933 GFLOPS, while achievable DP performance dipped down to 78 GFLOPS. This figure is roughly half of what Nvidia boasted about at the time of launch, and sheer evidence that both manufacturers like to overstate the performance of actual parts. What makes things interesting is the fact that Tesla GPGPU boards aren’t even most powerful parts in the Nvidia line-up. That “honor” goes to newly introduced GTX285 and 295. In professional line-up, Quadro FX 5800 has more “oomph”, thanks to higher shader clock. but even FX5800 will remain below 100 GFLOPS in dual-precision operations… making this GPU “just” 2.5x faster than quad-core Xeon processor.

Then again, if you activate parallel execution, CPU will drop to sub-10 GFLOPS values, while the GPU will remain at 78 GFLOPS for DP and 933 GFLOPS in single precision. At the same time, ATI’s architectural concept of “emulating” the DP units by pairing more processing units in one cluster resulted in actual peak performance of 900 GFLOPS for the 4870 part (claimed performance: 1.2 TFLOPS) and 250 GFLOPS for the Dual-Precision formats. This is an impressive difference, showcasing ATI’s lead from the architectural standpoint. Extractable performance is a bit different, since some ISVs managed to extract that performance, such as ElcomSoft password cracker, while some hit different walls and could not get better performance.

The real dilemma now is to wait and see what kind of computing performance lies with upcoming 40nm GPUs.

The post Nvidia’s discloses its DP performance limitations appeared first on VR World.

Under a not-so-scientific moniker Need For Speed Seminar Series, this 13-week course will feature domestic alumni such as Mark Hasegawa-Johnson, Dan Roth, Narendra Ahuja, Stephen Boppart, John C. Hart, Tom Huang and Seth Hutchinson, and guests such as Keith Thulborn (UI Chicago),  Sam Blackman (Elemental), Nikola Bozinovic (MotionDSP), Mark Johns (Tapulous) and Tim Sweeney (Epic).

More information is available if you visit the official site for Need for Speed seminars from Parallel@Illinois.

The post University of Illinois streams its Parallel@Illinois seminars appeared first on VR World.

This specification covers all GPGPU-capable hardware, regardless of that hardware being in servers, workstations, desktops, notebooks or handhelds – if your GPU is able to compute, the manufacturer only needs to adopt OpenCL support in the driver and that’s that.

That should not be an issue, with AMD/ATI and Nvidia strongly standing behind the standard. Computing engineers on both sides bickered about Brooke+, CAL or CUDA in the past, but both makers are firmly behind OpenCL as the way for the future.

So far, companies that developed and ratified this initial spec include 3Dlabs, Activision Blizzard, AMD, Apple, ARM, BARCO, Broadcom, Codeplay, Electronic Arts, Ericsson, Freescale, HI, IBM, Intel Corporation, Imagination Technologies, Kestrel Institute, Motorola, Movidia, Nokia, NVIDIA, QNX, RapidMind, Samsung, Seaweed, TAKUMI, Texas Instruments and Umea University.

You can learn more at the official page of OpenCL API, but I wasn’t able to check the site as it was hammered down with numerous requests (this story was written at 10:05AM CET).

The post GPGPU is the future: Khronos releases OpenCL API appeared first on VR World.

As a part of AMD STREAM, ATI will release its own video transcoder for free. Given the limitations and performance that Badaboom has, I wonder did ATI decided to do something more in “formats supported” area.

On the Folding@Home front, there are great expectations from this upcoming driver, especially if you own Radeon 4800 series product. ATI worked hard to remove the driver overhead which keeps the CPU core to 100% per GPU, and as a result, folding smaller proteins should improve by some 20%.

Looking at a bigger picture, ATI’s GPGPU engineers are working night and day on the upcoming OpenCL platform, so the future of CAL and other proprietary standards is up in the air. It is obvious that any pre-4800 hardware suffers from serious drawbacks in the architecture, but 4000 and beyond has the potential to “rock on” in the GPGPU world. Radeon 4000 series finally brought several key features that Nvidia featured from GeForce 8 and beyond, so we are going to have a heated GPGPU battle in 2009.

One thing is certain, though. As soon as Catalyst 8.12 appear (according to our sources, look for Terry “Saint Catalyst Claus” Makedon coming down the chimney around December 10th), go to game.amd.com and download the drivers. There is a myriad of improvements, and you should check out Catalyst 8.12 driver with updated Folding@Home core, currently in version 1.2.1.

You can try the 1.2.1 client right now, but expect a final version showing up around the release of Catalyst 8.12. December is going to be an exciting month for ATI’s F@H community.

The post ATI and Folding working on F@H 1.2.1: Performance, stability updates appeared first on VR World.

The interview was focusing on Bjorn3D’s noteworthy Folding@Home effort (the team is on track to crack into Top100), thus Michael gave some interesting thoughts, such as this one.

There are a lot of very good guides out there that will walk users through the required steps to fold with multiple GPUs like the ones on HardOCP or overclockers.co.uk, just not in SLI mode yet.
NVIDIA SLI is a great extension to parallel processing and we’re looking at methods to take advantage of it with Folding@home. Stay tuned.

As you can read for yourself, there is a lot of things that Nvidia wants to do with Folding@Home. ATI is currently working on own reshuffling, but at the end of the day, whoever makes the best GPU for protein folding, always have a higher goal in your mind.
In my own opinion, Folding@Home is purest form of “pay it forward”, because GPUs helped scientists to accelerate their research measured in human generations. Couple of months ago, I spoke to Vijay and he said that GPU’s accelerated the protein research by some 30 years. Let’s hope that ATI and Nvidia can decrease that by additional 20-30-40 years to real-time levels.
Currently, most powerful ATI and Nvidia GPUs are folding in the range of 300-600 nanoseconds/day. Achieving a second per day is something I am looking forward to. In 10 years time, we’ll hopefully be at 1 hour of protein life/day on GPUs.
Just to help you calculate, 1TFLOPS of computing power would calculate 1 milisecond of protein life PER DAY. For one second of protein life per day, we need 1PFLOPS. Thus, even PFLOPS GPUs could not calculate real-time.
Anyways, you can read the interview here, and regardless of where you are, I am inviting you to join Folding@Home project. My team number is 69864, but you’re more than welcome to join any team, or even stay outside teams. But we need your idle computing power. If you feel up to contributing a little, consider Folding@Home.

The post Nvidia to bring SLI support for Folding@Home appeared first on VR World.

All of the images in gallery are 1000 pixels wide… if you would like to see wider or narrower galleries, it would be great if I could get some feedback and suggest a resolution
The reason for it is quite simple: unlike companies that are advertising personal supercomputers and charging them tens of thousand dollars (Cray CX1 and Tyan PSC come to mind) – Nvidia is targeting a price of less than 9,999.99 USD. Yep, that’s correct, 4+ TFLOPS supercomputer for less than 10 grand.
At the time of writing, I haven’t been able to found are those Tesla cards using older 65nm or new, power-saving 55nm chips. If we would judge by chips that power new Quadros, 55nm should be under the hood, but then again, Nvidia has a large nVentory of older 65nm chips, so the company might be inclined to clean them out.
Somehow, I would bet that this is not the only supercomputer announcement from companies in Red-Green-Blue area. After all, certain motherboards from ASUS and GigaByte could fit six single-slot cards… can you say FireStream and 6 TFLOPS?
To end this article, here is some food for thought: Nvidia got inspiration to create this system after seeing tens of enthusiasts build their own supercomputers. 11 years ago, overclockers and gamers jump-started the IT industry we know today by overclocking and modding their own systems.
Can this underground movement turn desktop computers into handhelds and supercomputers onto desktops? Only time will tell. After all, what CPU, GPU or motherboard manufacturer does not encourage overclocking or offer an overclocking/gaming product line? After all, all of these products are based on overclocking Celeron 300A to 450 MHz, Dual-Celeron Abit BP6 and so on. Makes me wonder who will remember those crazy doctors that created FASTRA 10 years from now.

UPDATE Nov 18, 01:23AM CET: For some odd reason, gallery didn’t work – uploaded the pictures again. It should work now.

The post UPDATED: Nvidia set to launch Tesla powered Personal Supercomputer appeared first on VR World.

The post GPGPU Revolution: OpenCL to launch next week appeared first on VR World.

The motherboard for the ultimate workstation

The board supports both SLI and CrossFire in their respective maximum configurations (3 or 4 GPUs), but what makes this board really interesting is the fact that you could connect 12 LCD displays on it, or create a GPGPU/rendering/scientific/folding farm in a single case. Putting six ATI Radeon 4850 graphics cards would enable roughly 6TFLOPS of computing power. In case of Nvidia, you would have to pick up GeForce 9800GT (Palit has single-slot 1GB card) and have less theoretical computing power, but in terms of folding, you would be looking at 30-35.000 PPD system (at a cost of two GTX260 cards).
This is really impressive engineering feat from ASUS, with the only disappointment being usage of RealTek GbE controller. For a workstation motherboard, I would much happier if Marvell was on-board.
Storage-wise, you can install no less than eight SATA devices and not a single IDE device, since ASUS stayed in “Revolution” theme and killed of the PATA connector. Also, I found that a shared PS/2 port was also pretty neat solution, even though real revolution would be killing both PS/2 slots. This way, you still have one legacy part: PS/2.

There is one shared PS/2 port, for either keyboard or a mouse

Funny part of this story is that if anybody would have a time machine and go back to IBM engineers in 1986-7 frame and told them that only remain of their failed standard is going to be a keyboard/mouse connector, and that PS/2 connector will outlive PATA, I guess they would call you… crazy? Lunatic? Infidel?

P.S. If you’re wondering… yes, the answer is true. There are no technical issues that would prevent you from installing 3-Way SLI and 4-Way CrossFireX setup, consisting out of three GTX280 and two 4870X2 cards. Only problem is that you would have to have a watercooling setup, since you are limited to single-slot cooling solutions. I guess Asetek, CoolIT or somebody similar could come up with a solution for this “problem”.

The post ASUS kills PATA and PCI standards! appeared first on VR World.

Nvidia's client may not look as nice as ATI one, but it's the efficiency that counts...

Fast forward to GTX280 launch and the Vijay Pande team debuted the Folding@Home client for Nvidia chips as well. Nvidia and ATI lead a short marketing war who can fold better and things went quiet… apparently, for a reason.
The reason why things went quiet is probably the “inconvenient truth”: ATI showed up with Radeon 4800 series and demolished Nvidia’s dominance in the segment, with GTX260 and 280 going through radical price drops in order to stay competitive. However, ATI’s Radeon 4800 series has one field where the card is losing against 5-10x cheaper cards: Folding@Home.
The 10x argument lies in comparison between current ATI’s flagship, the  Radeon 4870X2 and Nvidia’s GeForce 9600GSO. This $50 card can easily out-fold ATI Radeon 4870X2, which retails for more than 500 USD/450EUR in respective markets.
In the past weeks, I’ve conducted a series of tests with various graphics cards (all that I own or could put my hands on), and the results were quite depressing if you own an ATI card. I’ve asked some of my contacts in AMD why the performance is so bad and the answers were ranging from “we wanted to make best gamer’s card, not a card for Folding” to sad silence. It seems to me that the difference lies in shader type and clock: ATI’s R6xx and RV7xx architecture lies around big fat units and lot of tiny ones (64+256 in case of Radeon 3800, 80+720 in case of Radeon 4800), and the clock is much lower than in case with GeForce cards. At the same time, Nvidia went the other route and came up with large number of “fat” units, while the company didn’t even count the “thin” (MADD) ones.
When we compare the GTX280 and 4870X2, comparisons are just astounding: in a period of a month, EVGA’s GTX280 SSC achieved an average of 6,802 points per day, while ATI Radeon 4870X2 managed puny 3,870 ppd. At the same time, I’ve witnessed higher PPD scores achieved even by two-year old GeForce 8800GTS 640 MB, which was quite a surprise. Around two weeks ago, I started following PPD numbers using FahMon on a large number of systems that mostly bear the same configuration: dua-core processor or more, 2GB system memory or more and the graphics cards. In all cases, with the help of my friends, I’ve managed to check FahMon and KakaoStats for rougly 25 cards and came to a surprising result.
With the recent update to the GPU2 client and new Fah_Core11.exe (ATI uses v1.17, Nvidia v1.15), the community witnessed further fall in number of completed packets per day. If you’re not familiar with Folding@Home packets, every package features certain number of mathematical simulations for tested protein – in case of Nvidia, packet consists out of 25 million, while ATI’s one features 10 million operations. However, due do different type of mathematical operations, Nvidia’s packet usually will result in 480 points, while ATI’s 10 million will return 548 points (or recently introduced ATI packets with 338 points).
Like I previously wrote, the table below is not the result of one packet score and Excel calculation, but rather continuous number crunching over the course of several weeks, with one week used for measurement.

Improvised Top 20 Folding@Home GPUs:

1. Nvidia GeForce GTX280 1GB (EVGA SSC)
2. Nvidia GeForce GTX260-216 898MB (EVGA SSC)
3. Nvidia GeForce GTX260 898MB (EVGA Superclocked)
4. Nvidia GeForce 9800GTX+ 512MB (ASUS TOP)
5. Nvidia Quadro FX 4600 SDI 768MB (PNY)
6. Nvidia GeForce 9800GTX 512MB (ASUS TOP)
7. Nvidia GeForce 8800GTX 768MB (Zotac AMP! Edition)
8. Nvidia GeForce 8800Ultra 768MB (XFX XXX Edition)
9. Nvidia GeForce 8800GTS 512MB (Gainward)
10. Nvidia GeForce 8800GT 512MB (Gainward)
11. Nvidia GeForce 9600GSO 768MB (EVGA)
12. Nvidia GeForce 8800GTS 640MB (LeadTek)
13. ATI Radeon 4870X2 2GB (PowerColor)
14. ATI Radeon 4870 512MB (PALIT)
15. Nvidia GeForce 9600GT 256MB (Zotac)
16. ATI Radeon 4850 512MB (PALIT)
17. ATI Radeon 3870 512MB (Sapphire Atomic)
18. ATI FireGL V8600 1GB (ATI)
19. Nvidia GeForce 8600GTS 256MB (XFX XXX Edition)
20. ATI Radeon 3850 256MB (Sapphire)

This is not a complete table by no means, since I am missing several new GPUs. But in this one, as you can see for yourself – results are quite dramatic for the red team. Two year old GeForce GPUs demolished otherwise-brilliant Radeon series, and it is incredible that even GeForce 9600 will outfold Radeon 4850. This is a rude wake-up call for guys at Markham, because this is just unbelievable.
Personally, I am running a combination of AMD Spider platform (9850BE + 790GX + ATI Radeon 4870X2) and hybrid Intel’s V8-Skulltrail platform with Quadro FX 4600 SDI.
Of course, everything can be changed with a simple driver update. I don’t understand what happened with AMD/ATI, company that lead the field of GPGPU computing for so long – why should AMD work on optimizing Folding@Home client… I am aware that AMD poached Mike Houston from Stanford to work on Brooke+ and now OpenCL APIs, but surely the performance didn’t went downhill from the influence of just one person. Or just maybe…
Overall, I hope that Catalyst 8.11 or 8.12 will bring more performance for ATI cards, since I do not believe that it would be so hard to optimize drivers for GPGPU/GPU Computing usage. For now, in Folding@Home, ATI is complete washout.

For the end of this article, if you find that your GPU cycles could be used for something good, I invite you to read the following article and join F@H family, regardless of what client (CPU or GPU) or team you choose in the end. Intel, AMD, ATI, Nvidia, Windows, Linux or Mac OS – it does not matter, just join – If you want, of course.

The post Nvidia’s $50 card destroys ATI’s $500 one or “Why ATI sucks in Folding?” appeared first on VR World.

Sapphire

Sadly, Sapphire was not allowed to make any changes, so FireStream 9250 continues to come with only 1GB of GDDR3 memory, while the most GPGPU scientists we talked about are talking about the need for massive amount of memory. Nvidia’s respond was 1st generation Tesla with 1.5 GB and the latest one with massive 4GB of GDDR3 memory.
We certainly hope that AMD will release FireStream with 2-4GB of memory, given their track record in professional graphics space. In any case, I congratulate to Sapphire for releasing the card.

The post AMD releasing professional cards to partners – Sapphire first appeared first on VR World.

All in all, the dawn of GPGPU computing is finally ending, and we’re heading forward into GPU accelerated world. Welcome.

The post Adobe ships GPU-accelerated Creative Studio 4, Flash Player 10 appeared first on VR World.