… and, while the battle still rages on, David appears to be winning.
In April of 2004 Nvidia hired Ian Buck, who specialized in GPGPU research at Stanford, setting their foundation for CUDA development. Two and a half years later Nvidia launched CUDA. In June of 2008 Nvidia announced their C1060 Tesla card intended solely for GPGPU processing. Pat Gelsinger, Senior VP and co-general manager of Intel’s Digital Enterprise Group, stated in an interview in July 2008 that CUDA will be little more than an “interesting footnote in the history of computing annals”. Pat confidently proclaimed that Intel’s Larrabee chip will easily best the GPGPU ecosystem that Nvidia had built. Larrabee would not only be a HPC powerhouse but it would be a better graphics card as well.
Fast forward to December 2009 and you will find that Intel has thrown in the towel from the Larrabee graphics perspective and the HPC future of Larrabee is still somewhat murky. Intel claims that Larrabee will still be used internally and externally by developers as a HPC platform. Pat Gelsinger the “confident” Intel executive now works for EMC. But Intel wasn’t the only CPU juggernaut bearing down on Nvidia. In 2006 AMD purchased ATI and announced its vision of the future… Fusion.
AMD Fusion is the codename for a future next-generation microprocessor design and the product of the merger between AMD and ATI, combining general processor execution as well as 3D geometry processing and other functions of modern GPUs into a single package. AMD announced at its Financial Analyst Day in December 2007 that their first two Fusion products codenamed Falcon and Swift would be available in 2009.
Fast forward to December 2009 and you will find no mention of AMDs Falcon and Swift. However, AMD does not appear to have thrown in the towel yet. They are still working on Fusion and claim they will have something out in 2010.
In January 2009 AMD announced at CES a system called the Fusion Render Cloud. The system will be powered by Phenom II processors with “more than 1,000″ ATI Radeon HD 4870 graphics cards. The Fusion Render Cloud is supposed to move rendering of complex interactive, three dimensional scenes up onto internet Fusion Render Cloud servers and, using OTOY’s software, stream the data to a simple browser on a connected device, bringing this kind of content to devices that haven’t been able to handle it yet because of ”device size, battery capacity, and processing power” — think, cell phones and other mobile devices. The system will be ready the second half of 2009.
Fast forward to December 2009 (last time I checked December was the last month of the second half of the year) and still no Fusion Render Cloud. Interestingly enough… from out of the blue… Nvidia announced their RealityServer platform. The platform is a powerful combination of Nvidia Tesla GPUs and 3D web services software that delivers interactive, photorealistic applications over the web, enabling product designers, architects and consumers to easily visualize 3D scenes with remarkable realism. The RealityServer platform runs in GPU-based cloud computing environment, accessible using web-connected PCs, netbooks, and smart phones, enabling 3D web applications to dynamically scale based on utilization requirements. Oh… and it was available for purchase the day they announced it.
So while David has not killed Goliath yet… he seems to be making steady progress on the hardware and software side putting actual products in the hands of his users. It doesn’t look like anyone has told David that he is going to be little more than an “interesting footnote in the history of computing annals”. If they have… I don’t think he’s listening.
Wednesday, December 16, 2009
Thursday, December 10, 2009
State of the GPGP Union
I’ve been following the GPGPU industry for a few years now. Initially Nvidia was the only player with a supported development platform. While they provided support for GPGPU on most of their cards, they only had support for double precision on their Tesla cards. If you were interested in doing anything serious with GPGPU you used Nvidia’s CUDA SDK and a Tesla C1060 card (or S1070 1U server).
Fast forward a year or so and the GPGPU landscape has changed significantly. AMD / ATI have entered the GPGPU arena with their stream computing and Fusion initiatives. Intel claimed that they would take over both the graphics market and the HPC market with their Larrabee initiative and then they recently started backpedaling. With all of the recent changes I thought it would be useful to take a look at current (and soon to be released) product offerings from AMD /ATI and Nvidia.
First let’s take a look at the desktop offerings. While AMD / ATI and Nvidia support GPGPU on a large number of cards I will limit my comparison to the high end devices. (If you're reading this through a feed aggregrator you will need to hit the actual site http://gpgpu-computing3.blogspot.com/ in a browser to get the tabular data... sorry)
Desktop Cards
While at first glance it might seem as though the AMD / ATI cards offer better performance at a cheaper cost. You should be cautious in this assessment. Often times what these vendors are reporting is theoretical performance not actual. Additionally depending on what types of algorithms you are running you might be more limited by memory than the number of processors. Additionally Nvidia has been specifically modifying the design of their GPUs to make them better at performing GPGPU tasks while AMD / ATI has just gotten into the game. AMD / ATI is working with SciSoft to hammer out an OpenCL GPGPU benchmark suite. Once this is completed then we will be able to intelligently make comparisons.
Another important factor is product availability. Both ATI cards are currently available, but due to low fabrication yields, in limited numbers. On the Nvidia side only the C1060 cards are currently available. The C2050 and C2070 are rumored to be available Q2 2010. Nvidia does have what they are calling “The Mad Science Program”. This sales initiative will allow you to purchase current generation Tesla products that can be upgraded when the next gen products are released. All you have to do is pay the difference and ship back the old product.
Well that covers the desktop side of the GPGPU world but what about server side solutions? Well on the server side there are a slew of solutions based on Nvidia GPUs. Nvidia has a M1060 card that they sell to OEMs. The M1060 is designed specifically to be integrated by OEMs into server based solutions. The M1060 is similar to the C1060 minus the “thermal solution”. The M1060 relies on the server’s fans to cool it. This makes the card much smaller so that you can pack more of them into a server while still providing adequate cooling. As far as I know AMD / ATI has no such animal. In theory you could drop an ATI card into any Dell or HP server that has a PCIe x 16 slot in it but I wouldn’t be surprised if it overheated from time to time.
Another server based solution that Nvidia has is their GPU 1U offload servers. The term “server” is perhaps not the best descriptive term for these devices. They do not run an OS or have any CPUs. They are basically a 1U box containing a power supply, 4 GPUs, some heat syncs, and fans. These GPU offload servers need to be connected to a “pizza box” that actually runs an OS via a PCIe x 16 extension cable. Your GPGPU program runs on the “pizza box” and loads your GPU kernel across the extension cable, copies your data across the cable, crunches the numbers on the GPU offload server, and then finally copies your results back. Nvidia 1U offload servers info:
Offload Servers
The S1070 is available today but the S2050 / S2070 will not be out until sometime midyear 2010. Nvidia’s “Mad Science” program applies to these devices as well. As far as I know ATI has nothing currently that is comparable to these.
Something else that must be taken into consideration is OS support for the development toolkits (CUDA, OpenCL) provided by each vendor.
Supported OSs
Overall I’m trying to be objective and not have any bias towards AMD / ATI or Nvidia but it is difficult. I have been doing GPGPU for a few years now and it has all been on Nvidia based products. Once AMD / ATI has a product that I can evaluate on my OS of choice (REL) and they start providing server side solutions I may change my tune… but for now… I’m sticking with Nvidia.
Fast forward a year or so and the GPGPU landscape has changed significantly. AMD / ATI have entered the GPGPU arena with their stream computing and Fusion initiatives. Intel claimed that they would take over both the graphics market and the HPC market with their Larrabee initiative and then they recently started backpedaling. With all of the recent changes I thought it would be useful to take a look at current (and soon to be released) product offerings from AMD /ATI and Nvidia.
First let’s take a look at the desktop offerings. While AMD / ATI and Nvidia support GPGPU on a large number of cards I will limit my comparison to the high end devices. (If you're reading this through a feed aggregrator you will need to hit the actual site http://gpgpu-computing3.blogspot.com/ in a browser to get the tabular data... sorry)
Desktop Cards
While at first glance it might seem as though the AMD / ATI cards offer better performance at a cheaper cost. You should be cautious in this assessment. Often times what these vendors are reporting is theoretical performance not actual. Additionally depending on what types of algorithms you are running you might be more limited by memory than the number of processors. Additionally Nvidia has been specifically modifying the design of their GPUs to make them better at performing GPGPU tasks while AMD / ATI has just gotten into the game. AMD / ATI is working with SciSoft to hammer out an OpenCL GPGPU benchmark suite. Once this is completed then we will be able to intelligently make comparisons.
Another important factor is product availability. Both ATI cards are currently available, but due to low fabrication yields, in limited numbers. On the Nvidia side only the C1060 cards are currently available. The C2050 and C2070 are rumored to be available Q2 2010. Nvidia does have what they are calling “The Mad Science Program”. This sales initiative will allow you to purchase current generation Tesla products that can be upgraded when the next gen products are released. All you have to do is pay the difference and ship back the old product.
Well that covers the desktop side of the GPGPU world but what about server side solutions? Well on the server side there are a slew of solutions based on Nvidia GPUs. Nvidia has a M1060 card that they sell to OEMs. The M1060 is designed specifically to be integrated by OEMs into server based solutions. The M1060 is similar to the C1060 minus the “thermal solution”. The M1060 relies on the server’s fans to cool it. This makes the card much smaller so that you can pack more of them into a server while still providing adequate cooling. As far as I know AMD / ATI has no such animal. In theory you could drop an ATI card into any Dell or HP server that has a PCIe x 16 slot in it but I wouldn’t be surprised if it overheated from time to time.
Another server based solution that Nvidia has is their GPU 1U offload servers. The term “server” is perhaps not the best descriptive term for these devices. They do not run an OS or have any CPUs. They are basically a 1U box containing a power supply, 4 GPUs, some heat syncs, and fans. These GPU offload servers need to be connected to a “pizza box” that actually runs an OS via a PCIe x 16 extension cable. Your GPGPU program runs on the “pizza box” and loads your GPU kernel across the extension cable, copies your data across the cable, crunches the numbers on the GPU offload server, and then finally copies your results back. Nvidia 1U offload servers info:
Offload Servers
The S1070 is available today but the S2050 / S2070 will not be out until sometime midyear 2010. Nvidia’s “Mad Science” program applies to these devices as well. As far as I know ATI has nothing currently that is comparable to these.
Something else that must be taken into consideration is OS support for the development toolkits (CUDA, OpenCL) provided by each vendor.
Supported OSs
Overall I’m trying to be objective and not have any bias towards AMD / ATI or Nvidia but it is difficult. I have been doing GPGPU for a few years now and it has all been on Nvidia based products. Once AMD / ATI has a product that I can evaluate on my OS of choice (REL) and they start providing server side solutions I may change my tune… but for now… I’m sticking with Nvidia.
Monday, December 7, 2009
Intel Gives Up on Larrabee
It looks like Intel is throwing in the towel on Larrabee. After investing hundreds of millions of dollars in development costs and demoing Larrabee at SC09 Intel seems to have given up on Larrabee as a general purpose GPU. They claim that Larrabee will still have a place in the HPC world but that they are abandoning their assault on the desktop graphics market.
When Intel embarked down the path to take on Nvidia and ATI with their Larrabee product line many insiders wondered if Intel had the GPU expertise to compete with Nvidia and ATI. After all this isn't the first time Intel tried to build a GPU and they didn't meet with much success the first time around. While everyone agreed that Intel had deep enough pockets to pull it off, the main question was were they willing to spend what it would take to be successful. Well it looks like we have the answer... No.
When Intel embarked down the path to take on Nvidia and ATI with their Larrabee product line many insiders wondered if Intel had the GPU expertise to compete with Nvidia and ATI. After all this isn't the first time Intel tried to build a GPU and they didn't meet with much success the first time around. While everyone agreed that Intel had deep enough pockets to pull it off, the main question was were they willing to spend what it would take to be successful. Well it looks like we have the answer... No.
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