From the Editor | Main Blog Index
May 19, 2011
While NVIDIA has managed to capture the mindshare in the GPU computing space, especially in the high performance computing segment, AMD keeps plugging away. AMD's Fusion processor/OpenCL strategy is its long-term answer to NVIDIA's CUDA approach, but in the near-term, AMD is looking for other ways to follow NVIDIA's lead into the GPU server market.
This week AMD announced the FirePro V7800P, its first non-FireStream server GPU part. Unlike the compute-focused FireStreams, however, the new V7800P is designed to serve both graphics/visualization applications and GPU computing.
In particular, it supports Microsoft RemoteFX, a visualization technology for virtual desktop infrastructure (VDI). The idea is to serve up desktop visuals from clusters to various types of thin clients, relieving the local device of doing heavy-duty graphics. The advantage is that a single GPU can support the graphics (and audio) for multiple clients and do so at the price-power-performance efficiency of a datacenter. Microsoft has high hopes for RemoteFX and hopes to draw millions of users to the technology in 2012.
Not surprisingly, AMD would like to be a part of that -- thus the FirePro V7800P. But since the device can also double as a GPU accelerator, V7800P-equipped servers can crunch on GPGPU applications when they're not busy drawing pretty pictures for client devices. It's easy to imagine a scenario where a GPU cluster would be driving desktop graphics during working hours and running HPC codes at nights and on weekends.
According to Mitch Furman, the senior product manager for AMD's professional graphics products, the V7800P is essentially a superset of the FireStream 9350 product. While the latter part supports OpenCL and OpenGL, only FirePro supports the OpenGL optimization that are needed for serious CAD and DCC applications. And as mentioned before, only the FirePro card works with RemoteFX for VDI.
Compute-wise though, the two AMD graphics processors are equivalent, both encompassing the same GPU technology (Cypress), core count (1,440) and memory (2 GB). Like the 9350, the V7800P delivers about 2 single precision teraflops or 400 double precision gigaflops. The extra graphics support on the FirePro just makes it a two-for-one deal for the datacenter. "It's really an IT management ease-of-use solution," Furman told me.
AMD didn't invent the dual-use concept. NVIDIA introduced a graphics/visualization-capable Tesla part last year in the M2070Q, a variant of the M2070 with Quadro graphics features enabled (the Q stands for Quadro). Like the FirePro V7800P, the M2070Q is aimed at users who want to build an all-in-one visualization/compute cluster.
A number of OEMs picked up on the M2070Q including IBM, who is offering it with its iDataPlex dx360 M3 as a RemoteFX server that can also moonlight as a GPU computer. IBM literature says 84 of these servers, each with two M2070Qs, can support up to 1,000 users, or about six users per Tesla device, in a RemoteFX VDI environment.
Dell, on the other hand, opted for the FirePro V7800P in its dual-use GPU server offering. For this purpose, the company is using the new FirePro in their PowerEdge M610x blade. That's a nice endorsement for AMD, especially when you consider that Dell offers the regular NVIDIA M2050 and M2070 modules in that same server for straight up GPU computing.
For visualization applications, and especially VDI, the AMD offering looks somewhat more attractive. According to Furman, the FirePro V7800P can support up to 16 users per card, as opposed to the six claimed for the IBM-M2070Q setup. (That's for vanilla desktop graphics, not for things like HD Blue Ray streaming or 3D gaming.) That makes sense, given that the FirePro has about twice the raw graphics capability -- as evidenced by its 2 teraflops of single-precision -- compared to the Tesla part.
HPC-wise, the Tesla offering looks more compelling, with a slight edge over the FirePro part in double-precision performance (515 gigaflops versus 400 gigaflops), a substantial edge in memory capacity (6 GB versus 2 GB), and a huge advantage in its support for features like ECC memory, L1 and L2 caches, asynchronous data transfers, and concurrent kernels. All of that capability comes with a power penalty, however; the M2070Q chews up 225 watts, compared to the V7800P at a more modest 138 watts.
If you can live with some of the compute shortcomings, AMD's offering is certainly priced to sell. With an MSRP of $1,249, the FirePro V7800P is less than half the cost of a M2070Q. The retail price for the more compute-endowed Tesla module is $5,000 and up, although presumably IBM and other OEMs are getting a better deal than that. The more attractive price and lower wattage could be the reasons Dell decided to go with the AMD part for its M610x blade.
Whether dual-use GPU server infrastructure catches on or not remains to be seen. The compute-loving FireStream and Tesla parts are cheaper than their dual-GPU counterparts, so if you don't need to serve up graphics, the choice is obvious. The success of these GPUs will hinge on how important server-side visualization becomes, and if technologies like Microsoft's RemoteFX get some traction.
"We're definitely excited about putting graphics in the datacenter," said Furman "We've put a lot of CPUs in there. Now we have a reason to put our GPUs in there as well."
Posted by Michael Feldman - May 19, 2011 @ 6:53 PM, Pacific Daylight Time
![]()
Michael Feldman is the editor of HPCwire.
No Recent Blog Comments
In quieter times, sounding the bell of funding big science with big systems tends to resonate further than when ears are already burning with sour economic and national security news. For exascale's future, however, the time could be ripe to instill some sense of urgency....
Read more...
In a recent solicitation, the NSF laid out needs for furthering its scientific and engineering infrastructure with new tools to go beyond top performance, Having already delivered systems like Stampede and Blue Waters, they're turning an eye to solving data-intensive challenges. We spoke with the agency's Irene Qualters and Barry Schneider about..
Read more...
Large-scale, worldwide scientific initiatives rely on some cloud-based system to both coordinate efforts and manage computational efforts at peak times that cannot be contained within the combined in-house HPC resources. Last week at Google I/O, Brookhaven National Lab’s Sergey Panitkin discussed the role of the Google Compute Engine in providing computational support to ATLAS, a detector of high-energy particles at the Large Hadron Collider (LHC).
Read more...
May 23, 2013 |
The study of climate change is one of those scientific problems where it is almost essential to model the entire Earth to attain accurate results and make worthwhile predictions. In an attempt to make climate science more accessible to smaller research facilities, NASA introduced what they call ‘Climate in a Box,’ a system they note acts as a desktop supercomputer.
Read more...
May 22, 2013 |
At some point in the not-too-distant future, building powerful, miniature computing systems will be considered a hobby for high schoolers, just as robotics or even Lego-building are today. That could be made possible through recent advancements made with the Raspberry Pi computers.
Read more...
May 16, 2013 |
When it comes to cloud, long distances mean unacceptably high latencies. Researchers from the University of Bonn in Germany examined those latency issues of doing CFD modeling in the cloud by utilizing a common CFD and its utilization in HPC instance types including both CPU and GPU cores of Amazon EC2.
Read more...
May 15, 2013 |
Supercomputers at the Department of Energy’s National Energy Research Scientific Computing Center (NERSC) have worked on important computational problems such as collapse of the atomic state, the optimization of chemical catalysts, and now modeling popping bubbles.
Read more...
05/10/2013 | Cleversafe, Cray, DDN, NetApp, & Panasas | From Wall Street to Hollywood, drug discovery to homeland security, companies and organizations of all sizes and stripes are coming face to face with the challenges – and opportunities – afforded by Big Data. Before anyone can utilize these extraordinary data repositories, however, they must first harness and manage their data stores, and do so utilizing technologies that underscore affordability, security, and scalability.
04/15/2013 | Bull | “50% of HPC users say their largest jobs scale to 120 cores or less.” How about yours? Are your codes ready to take advantage of today’s and tomorrow’s ultra-parallel HPC systems? Download this White Paper by Analysts Intersect360 Research to see what Bull and Intel’s Center for Excellence in Parallel Programming can do for your codes.
In this demonstration of SGI DMF ZeroWatt disk solution, Dr. Eng Lim Goh, SGI CTO, discusses a function of SGI DMF software to reduce costs and power consumption in an exascale (Big Data) storage datacenter.
The Cray CS300-AC cluster supercomputer offers energy efficient, air-cooled design based on modular, industry-standard platforms featuring the latest processor and network technologies and a wide range of datacenter cooling requirements.