Subject: Graphics Cards | January 20, 2016 - 03:26 PM | Scott Michaud
Tagged: nvidia, linux, tesla, fermi, kepler, maxwell
It's nice to see long-term roundups every once in a while. They do not really provide useful information for someone looking to make a purchase, but they show how our industry is changing (or not). In this case, Phoronix tested twenty-seven NVIDIA GeForce cards across four architectures: Tesla, Fermi, Kepler, and Maxwell. In other words, from the GeForce 8 series all the way up to the GTX 980 Ti.
Image Credit: Phoronix
Nine years of advancements in ASIC design, with a doubling time-step of 18 months, should yield a 64-fold improvement. The number of transistors falls short, showing about a 12-fold improvement between the Titan X and the largest first-wave Tesla, although that means nothing for a fabless semiconductor designer. The main reason why I include this figure is to show the actual Moore's Law trend over this time span, but it also highlights the slowdown in process technology.
Performance per watt does depend on NVIDIA though, and the ratio between the GTX 980 Ti and the 8500 GT is about 72:1. While this is slightly better than the target 64:1 ratio, these parts are from very different locations in their respective product stacks. Swapping the 8500 GT for the following year's 9800 GTX, which leads to a comparison between top-of-the-line GPUs of their respective times, and you see a 6.2x improvement in performance per watt versus the GTX 980 Ti. On the other hand, that part was outstanding for its era.
I should note that each of these tests take place on Linux. It might not perfectly reflect the landscape on Windows, but again, it's interesting in its own right.
Subject: General Tech, Graphics Cards, Systems | November 21, 2013 - 09:47 PM | Scott Michaud
Tagged: nvidia, tesla, supercomputing
GPUs are very efficient in terms of operations per watt. Their architecture is best suited for a gigantic bundle of similar calculations (such as a set of operations for each entry of a large blob of data). These are the tasks which also take up the most computation time especially for, not surprisingly, 3D graphics (where you need to do something to every pixel, fragment, vertex, etc.). It is also very relevant for scientific calculations, financial and other "big data" services, weather prediction, and so forth.
Tokyo Tech KFC achieves over 4 GigaFLOPs per watt of power draw from 160 Tesla K20X GPUs in its cluster. That is about 25% more calculations per watt than current leader of the Green500 (CINECA Eurora System in Italy, with 3.208 GFLOPs/W).
One interesting trait: this supercomputer will be cooled by oil immersion. NVIDIA offers passively cooled Tesla cards which, according to my understanding of how this works, suit very well to this fluid system. I am fairly certain that they remove all of the fans before dunking the servers (I figured they would be left on).
By the way, was it intentional to name computers dunked in giant vats of heat-conducting oil, "KFC"?
Intel has done a similar test, which we reported on last September, submerging numerous servers for over a year. Another benefit of being green is that you are not nearly as concerned about air conditioning.
NVIDIA is actually taking it to the practical market with another nice supercomputer win.
Other NVIDIA Supercomputing News:
- IBM and NVIDIA collaborate on GPU-accelerating IBM's enterprise software.
- Piz Daint, powered by Tesla K20X GPUs, greenest PFLOP-scale supercomputer.
Subject: General Tech, Graphics Cards | November 18, 2013 - 03:33 PM | Scott Michaud
Tagged: tesla, nvidia, K40, GK110b
The Tesla K20X ruled NVIDIA's headless GPU portfolio for quite some time now. The part is based on the GK110 chip with 192 shader cores disabled, like the GeForce Titan, and achieved 3.9 TeraFLOPs of compute performance (1.31 TeraFLOPs in double precision). Also, like the Titan, the K20X offers 6GB of memory.
The Tesla K40X
So the layout was basically the following: GK104 ruled the gamer market except for the, in hindsight, oddly-positioned GeForce Titan which was basically a Tesla K20X without a few features like error correction (ECC). The Quadro K6000 was the only card to utilize all 2880 CUDA cores.
Then, at the recent G-Sync event, NVIDIA CEO Jen-Hsun Huang announced the GeForce GTX 780Ti. This card uses the GK110b processor and incorporates all 2880 CUDA cores albeit with reduced double-precision performance (for the 780 Ti, not for GK110b in general). So now we have Quadro and GeForce with the full power Kepler, your move Tesla.
And they did, the Tesla K40 launched this morning and it brought more than just cores.
A brief overview
The GeForce launch was famous for its inclusion of GPU Boost, a feature absent in the Tesla line. It turns out that NVIDIA was paying attention to the feature but wanted to include it in a way that suited data centers. GeForce cards boost based on the status of the card, its temperature or its power draw. This is apparently unsuitable for data centers because they would like every unit operating at a very similar performance. The Tesla K40 has a base clock of 745 MHz but gives the data center two boost clocks that they can manually set: 810 MHz and 875 MHz.
Relative performance benchmarks
The Tesla K40 also doubles the amount of RAM to 12GB. Of course this allows for the GPU to work on larger data sets without streaming in the computation from system memory or worse.
There is currently no public information on pricing for the Tesla K40 but it is available starting today. What we do know are the launch OEM partners: ASUS, Bull, Cray, Dell, Eurotech, HP, IBM, Inspur, SGI, Sugon, Supermicro, and Tyan.
If you are interested in testing out a K40, NVIDIA has remotely hosted clusters that your company can sign up for at the GPU Test Drive website.
Subject: General Tech | April 24, 2013 - 01:38 PM | Jeremy Hellstrom
Tagged: Steve Scott, nvidia, HPC, tesla, logan, tegra
The Register had a chance to sit down with Steve Scott, once CTO of Cray and now CTO of NVIDIA's Tesla projects to discuss the future of their add-in cards as well as that of x86 in the server room. They discussed Tegra and why it is not receiving the same amount of attention at NVIDIA as Tegra is, as well as some of the fundamental differences in the chips both currently and going forward. NVIDIA plans to unite GPU and CPU onto both families of chips, likely with a custom interface as opposed to placing them on the same die, though both will continue to be designed for very different functions. A lot of the article focuses on Tegra, its memory bandwidth and most importantly its networking capabilities as it seems NVIDIA is focused on the server room and providing hundreds or thousands of interconnected Tegra processors to compete directly with x86 offerings. Read on for the full interview.
"Jen-Hsun Huang, co-founder and CEO of Nvidia has been perfectly honest about the fact that the graphics chip maker didn't intend to get into the supercomputing business. Rather, it was founded by a bunch of gamers who wanted better graphics cards to play 3D games. Fast forward two decades, though, and the Nvidia Tesla GPU coprocessor and the CUDA programming environment have taken the supercomputer world by storm."
Here is some more Tech News from around the web:
- AMD pins future growth to embedded marketplace @ The Register
- AMD announces new embedded G-series SoC @ DigiTimes
- TSMC captures almost 50 percent of foundry market thanks to 28nm demand @ The Inquirer
- $45 BeagleBone Black Keeps Eyes on the Pi's @ Linux.com
- BlackBerry OS 10.1 leaks its secret goo over all the web @ The Register
- Samsung MV900F Wi-Fi 16.3MP Digital Camera Review @ ModSynergy
- i’m Watch: A Smartwatch Review @ TechwareLabs
Subject: General Tech, Graphics Cards | March 20, 2013 - 01:47 PM | Tim Verry
Tagged: tesla, tegra 3, supercomputer, pedraforca, nvidia, GTC 2013, GTC, graphics cards, data centers
There is a lot of talk about heterogeneous computing at GTC, in the sense of adding graphics cards to servers. If you have HPC workloads that can benefit from GPU parallelism, adding GPUs gives you computing performance in less physical space, and using less power, than a CPU only cluster (for equivalent TFLOPS).
However, there was a session at GTC that actually took things to the opposite extreme. Instead of a CPU only cluster or a mixed cluster, Alex Ramirez (leader of Heterogeneous Architectures Group at Barcelona Supercomputing Center) is proposing a homogeneous GPU cluster called Pedraforca.
Pedraforca V2 combines NVIDIA Tesla GPUs with low power ARM processors. Each node is comprised of the following components:
- 1 x Mini-ITX carrier board
1 x Q7 module (which hosts the ARM SoC and memory)
- Current config is one Tegra 3 @ 1.3GHz and 2GB DDR2
- 1 x NVIDIA Tesla K20 accelerator card (1170 GFLOPS)
- 1 x InfiniBand 40Gb/s card (via Mellanox ConnectX-3 slot)
- 1 x 2.5" SSD (SATA 3 MLC, 250GB)
The ARM processor is used solely for booting the system and facilitating GPU communication between nodes. It is not intended to be used for computing. According to Dr. Ramirez, in situations where running code on a CPU would be faster, it would be best to have a small number of Intel Xeon powered nodes to do the CPU-favorable computing, and then offload the parallel workloads to the GPU cluster over the InfiniBand connection (though this is less than ideal, Pedraforca would be most-efficient with data-sets that can be processed solely on the Tesla cards).
While Pedraforca is not necessarily locked to NVIDIA's Tegra hardware, it is currently the only SoC that meets their needs. The system requires the ARM chip to have PCI-E support. The Tegra 3 SoC has four PCI-E lanes, so the carrier board is using two PLX chips to allow the Tesla and InfiniBand cards to both be connected.
The researcher stated that he is also looking forward to using NVIDIA's upcoming Logan processor in the Pedraforca cluster. It will reportedly be possible to upgrade existing Pedraforca clusters with the new chips by replacing the existing (Tegra 3) Q7 module with one that has the Logan SoC when it is released.
Pedraforca V2 has an initial cluster size of 64 nodes. While the speaker was reluctant to provide TFLOPS performance numbers, as it would depend on the workload, with 64 Telsa K20 cards, it should provide respectable performance. The intent of the cluster is to save power costs by using a low power CPU. If your sever kernel and applications can run on GPUs alone, there are noticeable power savings to be had by switching from a ~100W Intel Xeon chip to a lower-power (approximately 2-3W) Tegra 3 processor. If you have a kernel that needs to run on a CPU, it is recommended to run the OS on an Intel server and transfer just the GPU work to the Pedraforca cluster. Each Pedraforca node is reportedly under 300W, with the Tesla card being the majority of that figure. Despite the limitations, and niche nature of the workloads and software necessary to get the full power-saving benefits, Pedraforca is certainly an interesting take on a homogeneous server cluster!
In another session relating to the path to exascale computing, power use in data centers was listed as one of the biggest hurdles to getting to Exaflop-levels of performance, and while Pedraforca is not the answer to Exascale, it should at least be a useful learning experience at wringing the most parallelism out of code and pushing GPGPU to the limits. And that research will help other clusters use the GPUs more efficiently as researchers explore the future of computing.
The Pedraforca project built upon research conducted on Tibidabo, a multi-core ARM CPU cluster, and CARMA (CUDA on ARM development kit) which is a Tegra SoC paired with an NVIDIA Quadro card. The two slides below show CARMA benchmarks and a Tibidabo cluster (click on image for larger version).
Stay tuned to PC Perspective for more GTC 2013 coverage!
Subject: General Tech, Graphics Cards | March 19, 2013 - 06:52 PM | Tim Verry
Tagged: GTC 2013, tyan, HPC, servers, tesla, kepler, nvidia
Server platform manufacturer TYAN is showing off several of its latest servers aimed at the high performance computing (HPC) market. The new servers range in size from 2U to 4U chassis and hold up to 8 Kepler-based Tesla accelerator cards. The new product lineup consists of two motherboards and three bare-bones systems. The S7055 and S7056 are the motherboards while the FT77-B7059, TA77-B7061, and FT48-B7055.
The TA77-B7061 is the smallest system, with support for two Intel Xeon E5-2600 processors and four Kepler-based Tesla accelerator cards. The FT48-B7055 has si7056 specifications but is housed in a 4U chassis. Finally, the FT77-B7059 is a 4U system with support for two Intel Xeon E5-2600 processors, and up to eight Tesla accelerator cards. The S7055 supports a maximum of 4 GPUs while the S7056 can support two Tesla cards, though these are bare boards so you will have to supply your own cards, processors, and RAM (of course).
According to TYAN, the new Kepler-based HPC systems will be available in Q2 2013, though there is no word on pricing yet.
Stay tuned to PC Perspective for further GTC 2013 Coverage!
Subject: General Tech | March 18, 2013 - 02:23 PM | Jeremy Hellstrom
Tagged: nvidia, hack, GTX 690, K5000, K10, quadro, tesla, linux
It will take a bit of work with a soldering iron but Hack a Day has posted an article covering how to mod one of the GPUs on a GTX690 into thinking it is either a Quadro K5000 or Tesla K10. More people will need to apply this mod and test it to confirm that the performance of the GPU actually does match or at least compare to the professional level graphics but the ID string is definitely changed to match one of those two much more expensive GPUs. They also believe that a similar mod could be applied to the new TITAN graphics card as it is electronically similar to the GTX690. Of course, if things go bad during the modification you could kill a $1000 card so do be careful.
"If hardware manufacturers want to keep their firmware crippling a secret, perhaps they shouldn’t mess with Linux users? We figure if you’re using Linux you’re quite a bit more likely than the average Windows user to crack something open and see what’s hidden inside. And so we get to the story of how [Gnif] figured out that the NVIDIA GTX690 can be hacked to perform like the Quadro K5000. The thing is, the latter costs nearly $800 more than the former!"
Here is some more Tech News from around the web:
- The TR Podcast 130: A series of grunts about convertible tablets
- Microsoft updates its Kinect for Windows SDK @ The Inquirer
- Asustek to launch new Intel-based smartphone in June @ DigiTimes
- The 2013 Top 7 Best Linux Distributions for You @ Linux.com
- Watch out, office bods: A backdoor daemon lurks in HP LaserJets @ The Register
Subject: General Tech | November 23, 2012 - 01:03 PM | Jeremy Hellstrom
Tagged: gpgpu, amd, nvidia, Intel, phi, tesla, firepro, HPC
The skeptics were right to question the huge improvements seen when using GPGPUs in a system for heavy parallel computing tasks. The cards do help a lot but the 100x improvements that have been reported by some companies and universities had more to do with poorly optimized CPU code than with the processing power of GPGPUs. This news comes from someone who you might not expect to burst this particular bubble, Sumit Gupta is the GM of NVIDIA's Tesla team and he might be trying to mitigate any possible disappointment from future customers which have optimized CPU coding and won't see the huge improvements seen by academics and other current customers. The Inquirer does point out a balancing benefit, it is obviously much easier to optimize code in CUDA, OpenCL and other GPGPU languages than it is to code for multicored CPUs.
"Both AMD and Nvidia have been using real-world code examples and projects to promote the performance of their respective GPGPU accelerators for years, but now it seems some of the eye popping figures including speed ups of 100x or 200x were not down to just the computing power of GPGPUs. Sumit Gupta, GM of Nvidia's Tesla business told The INQUIRER that such figures were generally down to starting with unoptimised CPU."
Here is some more Tech News from around the web:
- Intel reportedly speeds up development of low-power processors @ DigiTimes
- Firefox and Opera squish big buffer overflow bugs @ The Register
- Hexing MAC address reveals Wifi passwords @ The Register
- Cisco Linksys EA6500 Smart Wi-Fi Router Review @ Legit Reviews
- Camera shootout: Samsung Galaxy S III vs S III mini @ Hardware.info
- Black Friday Tech Deals @ TechReviewSource
- Lawrence 'Empire Strikes Back' Kasdan to pen future Star Wars script @ The Register
- Win Corsair AX860i, AX760i, AX860 & AX760 power supplies @ Kitguru
Podcast #227 - Golden Z77 Motherboard from ECS, High Powered WiFi from Amped Wireless, Supercomputing GPUs and more!
Subject: General Tech | November 15, 2012 - 02:10 PM | Ken Addison
Tagged: titan, thor, tesla, s1000, podcast, nvidia, k20x, Intel, golden board, firepro, ECS, dust, Amped Wireless, amd
PC Perspective Podcast #227 - 11/15/2012
Join us this week as we talk about a Golden Z77 Motherboard from ECS, High Powered WiFi from Amped Wireless, Supercomputing GPUs and more!
The URL for the podcast is: http://pcper.com/podcast - Share with your friends!
- iTunes - Subscribe to the podcast directly through the Store
- RSS - Subscribe through your regular RSS reader
- MP3 - Direct download link to the MP3 file
Hosts: Ryan Shrout, Jeremy Hellstrom, Josh Walrath, and Allyn Malventano
Program length: 1:07:04
Podcast topics of discussion:
- Join us for the Hitman: Absolution Game Stream
- Week in Reviews:
- 0:18:00 This Podcast is brought to you by MSI!
News items of interest:
- 0:19:00 A renaissance of game types that have been sadly missing
- 0:24:00 You missed our live Medal of Honor Game Stream - loser!
- 0:26:12 NVIDIA launches Tesla K20X Card, Powers Titan Supercomputer
- 0:30:15 AMD Launches Dual Tahiti FirePro S10000
- 0:38:00 Some guy leaves Microsoft - is the Start Menu on its way back??
- 0:41:40 AMD is apparently not for sale
- 0:46:05 ECS joins the Thunderbolt family with a new Z77 motherboard
- 1-888-38-PCPER or email@example.com
- http://twitter.com/ryanshrout and http://twitter.com/pcper
Subject: General Tech | November 12, 2012 - 06:29 AM | Tim Verry
Tagged: tesla, supercomputer, nvidia, k20x, HPC, CUDA, computing
Graphics card manufacturer NVIDIA launched a new Tesla K20X accelerator card today that supplants the existing K20 as the top of the line model. The new card cranks up the double and single precision floating point performance, beefs up the memory capacity and bandwidth, and brings some efficiency improvements to the supercomputer space.
While it is not yet clear how many CUDA cores the K20X has, NVIDIA has stated that it is using the GK110 GPU, and is running with 6GB of memory with 250 GB/s of bandwidth – a nice improvement over the K20’s 5GB at 208 GB/s. Both the new K20X and K20 accelerator cards are based on the company’s Kepler architecture, but NVIDIA has managed to wring out more performance from the K20X. The K20 is rated at 1.17 TFlops peak double precision and 3.52 TFlops peak single precision while the K20X is rated at 1.31 TFlops and 3.95 TFlops.
The K20X manages to score 1.22 TFlops in DGEmm, which puts it at almost three times faster than the previous generation Tesla M2090 accelerator based on the Fermi architecture.
Aside from pure performance, NVIDIA is also touting efficiency gains with the new K20X accelerator card. When two K20X cards are paired with a 2P Sandy Bridge server, NVIDIA claims to achieve 76% efficiency versus 61% efficiency with a 2P Sandy Bridge server equipped with two previous generation M2090 accelerator cards. Additionally, NVIDIA claims to have enabled the Titan supercomputer to reach the #1 spot on the top 500 green supercomputers thanks to its new cards with a rating of 2,120.16 MFLOPS/W (million floating point operations per second per watt).
NVIDIA claims to have already shipped 30 PFLOPS worth of GPU accelerated computing power. Interestingly, most of that computing power is housed in the recently unveiled Titan supercomputer. This supercomputer contains 18,688 Tesla K20X (Kepler GK110) GPUs and 299,008 16-core AMD Opteron 6274 processors. It will consume 9 megawatts of power and is rated at a peak of 27 Petaflops and 17.59 Petaflops during a sustained Linpack benchmark. Further, when compared to Sandy Bridge processors, the K20 series offers up between 8.2 and 18.1 times more performance at several scientific applications.
While the Tesla cards undoubtedly use more power than CPUs, you need far fewer numbers of accelerator cards than processors to hit the same performance numbers. That is where NVIDIA is getting its power efficiency numbers from.
NVIDIA is aiming the accelerator cards at researchers and businesses doing 3D graphics, visual effects, high performance computing, climate modeling, molecular dynamics, earth science, simulations, fluid dynamics, and other such computationally intensive tasks. Using CUDA and the parrallel nature of the GPU, the Tesla cards can acheive performance much higher than a CPU-only system can. NVIDIA has also engineered software to better parrellelize workloads and keep the GPU accelerators fed with data that the company calls Hyper-Q and Dynamic Parallelism respectively.
It is interesting to see NVIDIA bring out a new flagship, especially another GK110 card. Systems using the K20 and the new K20X are available now with cards shipping this week and general availability later this month.
You can find the full press release below and a look at the GK110 GPU in our preview.
Anandtech also managed to get a look inside the Titan supercomputer at Oak Ridge National Labratory, where you can see the Tesla K20X cards in action.