GPU Challenges: A Q&A with NVIDIA’s David Kirk

By Nicole Hemsoth

June 22, 2011

At ISC this year, there are plenty of sessions devoted to manycore processors, especially in the role of HPC accelerators. Not surprisingly, a lot of these are centered on the current sweetheart of manycore: GPUs. One of the most well-attended sessions here at ISC’11 was “The GPU Debate” between NVIDIA Fellow David Kirk and LSU professor Thomas Sterling, where the two bantered about the architecture, its evolution as a general-purpose HPC processor, and its roadmap to exascale.

HPCwire caught up with Kirk and asked him about some of the specific challenges of GPU computing today and how he views the role of integrated CPU-GPU architectures as they come into play.

HPCwire: Is there any thought at NVIDIA to proposing CUDA as an open standard for the GPU/manycore computing community?

David Kirk: There are no plans to turn CUDA into an open standard at this point. Right now, the only processors we see being deployed widely in servers are x86 CPUs and NVIDIA GPUs and these are all supported by CUDA toolkits today. NVIDIA offers developers choice – choice to use CUDA C, CUDA C++, CUDA Fortran, OpenCL, or DirectCompute to program CPU-GPU systems. We chair the OpenCL working group, we have collaborated closely with Microsoft on DirectCompute and continue to do so as they evolve these platforms. But CUDA is our platform for innovation. We recently released CUDA 4.0, which is a huge leap forward in programmer productivity with features like unified virtual addressing and the new Thrust C++ template library. We continue to move CUDA forward at a rapid pace.

HPCwire: There has been plenty of talk about the problems involved in hanging a GPU processor off of a PCI bus for use as an external accelerator – I/O overhead and the software messiness of having to do explicit data transfers. What do you think are the biggest limitations of the current GPU processors from a hardware point of view, in regard to high performance computing?

Kirk: The PCIe bottleneck concern is hotly debated and we hear about it a lot. We are aware of very few applications that are bottlenecked by transfer speeds. Incidentally, the PCIe bus is often not the slowest bus in the system. Network and disk interfaces are slower, and in many systems the CPU memory path is slower!

That being said, there are two things that have changed since this concern first surfaced. First, we now have 6 GB of on-board memory and second, our new NVIDIA GPUDirect technology is eliminating the CPU and GPU memory bottlenecks from the path.

These enhancements reduce the PCIe bottleneck. Data can directly stream from storage to the GPU memory via GPUDirect and the larger GPU memory enables more data to reside on the GPU without communicating to the CPU. Our future GPU architectures will continue to reduce dependence on and communication with the CPU, thus eventually very significantly limiting the PCIe bottleneck. By the way, Vincent Natoli summarized it nicely in his recent HPCwire article.

I personally believe though, that the biggest limitation of GPU computing is the misconception that it’s too hard. Put this into whichever bucket you wish — ease of use of the software, the programmability of the hardware, the performance, per watt, per dollar. However you slice it, there have been many reasons cited as to why not to adopt GPU computing.

We’ll be the first to say that parallel computing is challenging. I personally co-teach the parallel computing course, along with Dr. Wen-mei Hwu, at the University of Illinois at Urbana-Champaign, so I know first-hand what it is like to switch the mindset from a purely serial based model to thinking about problems in a multi-threaded parallel environment.

But the rewards are significant. Change two percent of your code and in many cases you can see up to a 10X increase in performance. That’s a pretty big bang for your software development buck. And, we live in a parallel computing world now, so serial programming is no longer a viable option.

HPCwire: Same question for software side. What are the biggest limitations of the current GPU computing software frameworks?

Kirk: One of the most common concerns I hear from the community is the portability aspect of CUDA and the fact that it only runs on NVIDIA GPUs. As I said before, we remain agnostic on language. Fortran, Python, C, C++, Java, OpenCL, DirectCompute – we support all these languages, either internally or through 3rd parties. If you choose to use NVIDIA GPUs, then we will ensure that have you the widest choice of languages.

With regards to the portability of the hardware platform, PGI has just announced the first version of CUDA x86, that enables CUDA code to be compiled down to x86 CPUs. This facilitates easier-than-ever deployment of CUDA-enabled applications across hybrid GPU/CPU systems and is an important milestone in the increased portability of CUDA. There are also several tools created by universities and 3rd-parties to convert CUDA source code to OpenCL source code, which can be compiled for any platform that supports OpenCL. So, portability is no longer a realistic objection but more of an excuse.

Training the millions of software developers who are already in the industry to program in parallel – that is the biggest challenge facing HPC and parallel computing in general. This is where the elegance of the CUDA parallel programming model really helps and the reason why it has caught on so quickly and so widely. CUDA C/C++ is an incredibly powerful language of authorship, and we have found that it is quite easy to learn.

HPCwire: Do you think the appearance of heterogeneous CPU-GPU processors portends the demise of discrete GPUs – for GPU computing or otherwise? Do you think it will spell the end of “pure” CPUs?

Kirk: A lot of folks believe that integrating CPUs and GPUs together is a panacea. As you well know, this is easy for NVIDIA to do. We have the highest volume integrated CPU-GPU SoC shipping today: our Tegra mobile SoC. But if you scale this to HPC, the challenge is that you have to compromise either on the performance of the CPU or that of the GPU. The silicon area is fixed, so you have to put a medium performance CPU with a medium performance GPU. Not exactly HPC! We find that none of our customers ever ask us for less performance.

For the foreseeable future, there will be a market for a discrete CPU and a discrete GPU – the performance users, whether in HPC or in gaming or CAD workstations, need the best of both. But a swing we already see happening is that applications are leaning more on the GPU for performance than on the CPU — both gaming and HPC. This is because performance scaling on CPUs seems to have reached an end. Laptops are not going beyond dual-core x86 CPUs. Even on HPC, application performance is not scaling beyond 4 cores. They end up choking on memory bandwidth.

Clearly, the personal computer experience is going to be dominated by SoCs with integrated ARM cores and GPUs. This is happening today and will be solidified by support for ARM in Windows Next. But as I said above, we expect that there will be a CPU + GPU market for a very long time to come.

HPCwire: How will users be able to port codes developed today with CUDA, OpenCL and accelerator-directives to the future shared-memory architectures of CPU-GPU integrated processors envisioned by “Project Denver” AMD Fusion, etc.?

Kirk: The beauty about the CUDA programming model is that it was designed for CPU-GPU based heterogeneous architectures. Whether the CPU and GPU are integrated does not change the programming model. Integration is simply a cost consideration. After all, we have been working on Tegra — ARM + GPU SoCs — for just as long as we have been working on CUDA. Other driver-level APIs like OpenCL treat the GPU as a device that is separate from the CPU (host) and this means that OpenCL as defined today has to be extended to support an integrated CPU-GPU device. This means that applications written with the CUDA toolkits will just work on our integrated CPU-GPU devices.

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!

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate scientists the ability to use machine learning to identify e Read more…

By Rob Farber

Mellanox Reacts to Activist Investor Pressures in Letter to Shareholders

March 16, 2018

Activist investor Starboard Value has been exerting pressure on Mellanox Technologies to increase its returns. In response, the high-performance networking company on Monday, March 12, published a letter to shareholders outlining its proposal for a May 2018 extraordinary general meeting (EGM) of shareholders and highlighting its long-term growth strategy and focus on operating margin improvement. Read more…

By Staff

Quantum Computing vs. Our ‘Caveman Newtonian Brain’: Why Quantum Is So Hard

March 15, 2018

Quantum is coming. Maybe not today, maybe not tomorrow, but soon enough. Within 10 to 12 years, we’re told, special-purpose quantum systems will enter the commercial realm. Assuming this happens, we can also assume that quantum will, over extended time, become increasingly general purpose as it delivers mind-blowing power. Read more…

By Doug Black

HPE Extreme Performance Solutions

Achieve Optimal Performance at Scale with High Performance Fabrics for HPC

High Performance Computing (HPC) is unlocking a new era of speed and productivity to fuel business transformation. Rapid advancements in HPC capabilities are helping organizations operate faster and more effectively than ever, but in today’s fast-paced marketplace, a new generation of technologies is required to reach greater scalability and cost-efficiency. Read more…

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise IT in its willingness to outsource computational power. The m Read more…

By Chris Downing

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate Read more…

By Rob Farber

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise I Read more…

By Chris Downing

Stephen Hawking, Legendary Scientist, Dies at 76

March 14, 2018

Stephen Hawking passed away at his home in Cambridge, England, in the early morning of March 14; he was 76. Born on January 8, 1942, Hawking was an English theo Read more…

By Tiffany Trader

Hyperion Tackles Elusive Quantum Computing Landscape

March 13, 2018

Quantum computing - exciting and off-putting all at once - is a kaleidoscope of technology and market questions whose shapes and positions are far from settled. Read more…

By John Russell

Part Two: Navigating Life Sciences Choppy HPC Waters in 2018

March 8, 2018

2017 was not necessarily the best year to build a large HPC system for life sciences say Ari Berman, VP and GM of consulting services, and Aaron Gardner, direct Read more…

By John Russell

Google Chases Quantum Supremacy with 72-Qubit Processor

March 7, 2018

Google pulled ahead of the pack this week in the race toward "quantum supremacy," with the introduction of a new 72-qubit quantum processor called Bristlecone. Read more…

By Tiffany Trader

SciNet Launches Niagara, Canada’s Fastest Supercomputer

March 5, 2018

SciNet and the University of Toronto today unveiled "Niagara," Canada's most-powerful supercomputer, comprising 1,500 dense Lenovo ThinkSystem SD530 high-perfor Read more…

By Tiffany Trader

Part One: Deep Dive into 2018 Trends in Life Sciences HPC

March 1, 2018

Life sciences is an interesting lens through which to see HPC. It is perhaps not an obvious choice, given life sciences’ relative newness as a heavy user of H Read more…

By John Russell

Inventor Claims to Have Solved Floating Point Error Problem

January 17, 2018

"The decades-old floating point error problem has been solved," proclaims a press release from inventor Alan Jorgensen. The computer scientist has filed for and Read more…

By Tiffany Trader

Japan Unveils Quantum Neural Network

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it's early enough that ultimate leadership is still something of an open questi Read more…

By Tiffany Trader

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown Read more…

By Tiffany Trader

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

Leading Solution Providers

GlobalFoundries, Ayar Labs Team Up to Commercialize Optical I/O

December 4, 2017

GlobalFoundries (GF) and Ayar Labs, a startup focused on using light, instead of electricity, to transfer data between chips, today announced they've entered in Read more…

By Tiffany Trader

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

V100 Good but not Great on Select Deep Learning Aps, Says Xcelerit

November 27, 2017

Wringing optimum performance from hardware to accelerate deep learning applications is a challenge that often depends on the specific application in use. A benc Read more…

By John Russell

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par Read more…

By Tiffany Trader

AMD Wins Another: Baidu to Deploy EPYC on Single Socket Servers

December 13, 2017

When AMD introduced its EPYC chip line in June, the company said a portion of the line was specifically designed to re-invigorate a single socket segment in wha Read more…

By John Russell

World Record: Quantum Computer with 46 Qubits Simulated

December 18, 2017

Scientists from the Jülich Supercomputing Centre have set a new world record. Together with researchers from Wuhan University and the University of Groningen, Read more…

New Blueprint for Converging HPC, Big Data

January 18, 2018

After five annual workshops on Big Data and Extreme-Scale Computing (BDEC), a group of international HPC heavyweights including Jack Dongarra (University of Te Read more…

By John Russell

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