NVIDIA Launches First Kepler GPUs at Gamers; HPC Version Waiting in the Wings

By Michael Feldman

March 22, 2012

NVIDIA debuted its much-talked-about Kepler GPU this week, promising much better performance and energy efficiency than its previous generation Fermi-based products. The first offerings are mid-range graphics cards targeted at the heart of the desktop and notebook market, but the more powerful second-generation Kepler GPU for high performance computing is already in the pipeline.

The two new products introduced this week, the GeForce GTX 680 for desktop systems and the GeForce 600M GPUs for notebooks, are twice as energy efficient as their Fermi-based counterparts, according to NVIDIA. And although they represent more powerful graphics processors than the previous generation, the overriding design theme of the new architecture is performance per watt, rather than performance per square millimeter. According to Sumit Gupta, NVIDIA’s senior director of the Tesla GPU Computing business unit, that’s a fundamental change in the company’s architectural strategy. “This is the first time that power is a higher order concern than area,” he says.

That’s because, like nearly every chipmaker on the planet, NVIDIA’s fastest growing market segment is the mobile and notebook/ultrabook space. This architectural emphasis on energy efficiency also dovetails rather nicely with the GPU computing market, where power consumption is also a huge factor. That’s especially true for the Tesla GPU parts that end up in energy-sucking HPC servers. “Every market we’re in has become power sensitive,” says Gupta.

Upping the power efficiency in Kepler relied heavily on a tried-and-true technique, namely increasing the core count while lowering the clock speed. But the architecture is somewhat different. Underneath the covers, the cores are collected into what NVIDIA calls their Streaming Multiprocessors (SMs). In the Fermi version there were only 32 cores per SM. In the Kepler implementation, they reduced the control logic disproportionally and were able to squeeze in 192.

Boosting the core numbers was a no-brainer, given they were moving from the 40nm process technology with Fermi, to the 28nm node for Kepler. In the case of the GeForce GTX 680, for example, there are 1536 cores — three times as many as in the high-end Fermi GPUs, which topped out at 512 cores. At the same time they reduced the clock frequency from 1.5 GHz on the Fermi chip to just a shade over 1 GHz. Although each core is now doing less work, because there are more of them, throughput increases and does so with lower energy consumption.

CPU chipmakers have employed this strategy as well. But because of the greater complexity of the individual CPU cores and their reliance on limited memory bandwidth, core count increases are starting to stagnate (no CPU make ever tripled core count in one generation). Also, since a lot of applications are dependent on single-threaded performance, CPU chip makers try to hold the line on clock speed as much as possible. Ratcheting down the clock speed by a third, as NVIDIA has done here, is unheard for a CPU product.

For Kepler, NVIDIA is claiming a doubling of performance per watt compared to the Fermi-generation GeForce GTX 580. For real gaming applications, the new Kepler products are getting between 1.1 to and 2 times better the performance per watt. In some cases though, it can do even better.

For example, NVIDIA used their Samaritan demo, which illustrates photorealistic gaming, to show a 3X performance boost. Up until this week, that demo required three GeForce GTX 580 cards, drawing a total of 732 watts. It can now be run with a single 195-watt GeForce GTX 680.

To support all the extra throughput, memory bandwidth has been kicked up significantly. The interface on the GTX 680 supports 6.0 Gbps, which is 50 percent more than the 4.0 Gbps available on the GTX 580. According to Gupta, that’s the highest memory bandwidth for any commodity-based chip, NVIDIA or otherwise.

All of these architectural changes — more cores, slower clocks, and more memory bandwidth — will carry over into the second version of the Kepler GPU, a higher-end design which will be aimed primarily at GPU computing applications. This is the one the next-generation Tesla products will be based upon, and the one that will initially end up in two of the most powerful supercomputers in the world: Blue Waters at NCSA and Titan at ORNL.

According to Gupta, the second Kepler implementation will include a lot of capability not present in these first gaming-oriented products. In particular, it will have a lot more double-precision capability (which is not required for most graphics applications) and include new compute-specific features. And of course the raw power of these chips will be quite a bit higher than the mid-range graphics version introduced this week.

Although the company is not yet giving any of the speeds and feeds on the second Kepler, one would expect the core count and peak double precision performance to be two to three times higher, and memory bandwidth to get at least a 50 percent bump. Clock speed will almost certainly be whittled down from the current 1.3 GHz on the Tesla M2090, but perhaps not so aggressively as in these first Kepler gaming parts.

Presumably, the NVIDIA will stick with its 225 watt power envelope for the Tesla lineup, so the engineers just have to balance the core count and clock to land on that thermal design point. Given that power ceiling and the core count increase, NVIDIA should be able to deliver a Tesla GPU with between 1.3 and 1.5 teraflops of double precision performance. On the other hand, there is probably a case to be made to also offer less performant parts that consume less power.

In any case we’ll know soon enough. NVIDIA will probably do their paper launch of the HPC Kepler at the company’s GPU Technology Conference in May. And according to Gupta, the company is on track to put this version into production in Q4. If that goes according to plan, the new Kepler GPUs will be up and running on supercomputers before the end of the year.

Related Articles

NVIDIA Revs Up Tesla GPU

GPUs Will Morph ORNL’s Jaguar Into 20-Petaflop Titan

NCSA Signs Up Cray for Blue Waters Redo

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Advancing Modular Supercomputing with DEEP and DEEP-ER Architectures

February 24, 2017

Knowing that the jump to exascale will require novel architectural approaches capable of delivering dramatic efficiency and performance gains, researchers around the world are hard at work on next-generation HPC systems. Read more…

By Sean Thielen

Weekly Twitter Roundup (Feb. 23, 2017)

February 23, 2017

Here at HPCwire, we aim to keep the HPC community apprised of the most relevant and interesting news items that get tweeted throughout the week. Read more…

By Thomas Ayres

HPE Server Shows Low Latency on STAC-N1 Test

February 22, 2017

The performance of trade and match servers can be a critical differentiator for financial trading houses. Read more…

By John Russell

HPC Financial Update (Feb. 2017)

February 22, 2017

In this recurring feature, we’ll provide you with financial highlights from companies in the HPC industry. Check back in regularly for an updated list with the most pertinent fiscal information. Read more…

By Thomas Ayres

HPE Extreme Performance Solutions

O&G Companies Create Value with High Performance Remote Visualization

Today’s oil and gas (O&G) companies are striving to process datasets that have become not only tremendously large, but extremely complex. And the larger that data becomes, the harder it is to move and analyze it – particularly with a workforce that could be distributed between drilling sites, offshore rigs, and remote offices. Read more…

Rethinking HPC Platforms for ‘Second Gen’ Applications

February 22, 2017

Just what constitutes HPC and how best to support it is a keen topic currently. Read more…

By John Russell

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

ExxonMobil, NCSA, Cray Scale Reservoir Simulation to 700,000+ Processors

February 17, 2017

In a scaling breakthrough for oil and gas discovery, ExxonMobil geoscientists report they have harnessed the power of 717,000 processors – the equivalent of 22,000 32-processor computers – to run complex oil and gas reservoir simulation models. Read more…

By Doug Black

Advancing Modular Supercomputing with DEEP and DEEP-ER Architectures

February 24, 2017

Knowing that the jump to exascale will require novel architectural approaches capable of delivering dramatic efficiency and performance gains, researchers around the world are hard at work on next-generation HPC systems. Read more…

By Sean Thielen

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Drug Developers Use Google Cloud HPC in the Fight Against ALS

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Read more…

By Doug Black

Azure Edges AWS in Linpack Benchmark Study

February 15, 2017

The “when will clouds be ready for HPC” question has ebbed and flowed for years. Read more…

By John Russell

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

US, China Vie for Supercomputing Supremacy

November 14, 2016

The 48th edition of the TOP500 list is fresh off the presses and while there is no new number one system, as previously teased by China, there are a number of notable entrants from the US and around the world and significant trends to report on. Read more…

By Tiffany Trader

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

D-Wave SC16 Update: What’s Bo Ewald Saying These Days

November 18, 2016

Tucked in a back section of the SC16 exhibit hall, quantum computing pioneer D-Wave has been talking up its new 2000-qubit processor announced in September. Forget for a moment the criticism sometimes aimed at D-Wave. This small Canadian company has sold several machines including, for example, ones to Lockheed and NASA, and has worked with Google on mapping machine learning problems to quantum computing. In July Los Alamos National Laboratory took possession of a 1000-quibit D-Wave 2X system that LANL ordered a year ago around the time of SC15. Read more…

By John Russell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Leading Solution Providers

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. Read more…

By Tiffany Trader

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

Dell Knights Landing Machine Sets New STAC Records

November 2, 2016

The Securities Technology Analysis Center, commonly known as STAC, has released a new report characterizing the performance of the Knight Landing-based Dell PowerEdge C6320p server on the STAC-A2 benchmarking suite, widely used by the financial services industry to test and evaluate computing platforms. The Dell machine has set new records for both the baseline Greeks benchmark and the large Greeks benchmark. Read more…

By Tiffany Trader

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

What Knights Landing Is Not

June 18, 2016

As we get ready to launch the newest member of the Intel Xeon Phi family, code named Knights Landing, it is natural that there be some questions and potentially some confusion. Read more…

By James Reinders, Intel

  • arrow
  • Click Here for More Headlines
  • arrow
Share This