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!

From Exasperation to Exascale: HPE’s Nic Dubé on Frontier’s Untold Story

December 2, 2022

The Frontier supercomputer – still fresh off its chart-topping 1.1 Linpack exaflops run and maintaining its number-one spot on the Top500 list – was still very much in the spotlight at SC22 in Dallas last month. Six Read more…

At SC22, Carbon Emissions and Energy Costs Eclipsed Hardware Efficiency

December 2, 2022

The race to ever-better flops-per-watt and power usage effectiveness (PUE) has, historically, dominated the conversation over sustainability in HPC – but at SC22, held last month in Dallas, something felt different. Ac Read more…

HPC Career Notes: December 2022 Edition

December 1, 2022

In this monthly feature, we’ll keep you up-to-date on the latest career developments for individuals in the high-performance computing community. Whether it’s a promotion, new company hire, or even an accolade, we’ Read more…

IBM Quantum Summit: Osprey Flies; Error Handling Progress; Quantum-centric Supercomputing

December 1, 2022

Part scorecard, part grand vision, IBM’s annual Quantum Summit held last month is a fascinating snapshot of IBM’s progress, evolving technology roadmap, and issues facing the quantum landscape broadly. Thankfully, IB Read more…

AWS Introduces a Flurry of New EC2 Instances at re:Invent

November 30, 2022

AWS has announced three new Amazon Elastic Compute Cloud (Amazon EC2) instances powered by AWS-designed chips, as well as several new Intel-powered instances – including ones targeting HPC – at its AWS re:Invent 2022 Read more…

AWS Solution Channel

Shutterstock 110419589

Thank you for visiting AWS at SC22

Accelerate high performance computing (HPC) solutions with AWS. We make extreme-scale compute possible so that you can solve some of the world’s toughest environmental, social, health, and scientific challenges. Read more…

 

shutterstock_1431394361

AI and the need for purpose-built cloud infrastructure

Modern AI solutions augment human understanding, preferences, intent, and even spoken language. AI improves our knowledge and understanding by delivering faster, more informed insights that fuel transformation beyond anything previously imagined. Read more…

Quantum Riches and Hardware Diversity Are Discouraging Collaboration

November 28, 2022

Quantum computing is viewed as a technology for generations, and the spoils for the winners are huge, but the diversity of technology is discouraging collaboration, an Intel executive said last week. There are close t Read more…

From Exasperation to Exascale: HPE’s Nic Dubé on Frontier’s Untold Story

December 2, 2022

The Frontier supercomputer – still fresh off its chart-topping 1.1 Linpack exaflops run and maintaining its number-one spot on the Top500 list – was still v Read more…

At SC22, Carbon Emissions and Energy Costs Eclipsed Hardware Efficiency

December 2, 2022

The race to ever-better flops-per-watt and power usage effectiveness (PUE) has, historically, dominated the conversation over sustainability in HPC – but at S Read more…

HPC Career Notes: December 2022 Edition

December 1, 2022

In this monthly feature, we’ll keep you up-to-date on the latest career developments for individuals in the high-performance computing community. Whether it Read more…

IBM Quantum Summit: Osprey Flies; Error Handling Progress; Quantum-centric Supercomputing

December 1, 2022

Part scorecard, part grand vision, IBM’s annual Quantum Summit held last month is a fascinating snapshot of IBM’s progress, evolving technology roadmap, and Read more…

AWS Introduces a Flurry of New EC2 Instances at re:Invent

November 30, 2022

AWS has announced three new Amazon Elastic Compute Cloud (Amazon EC2) instances powered by AWS-designed chips, as well as several new Intel-powered instances Read more…

Quantum Riches and Hardware Diversity Are Discouraging Collaboration

November 28, 2022

Quantum computing is viewed as a technology for generations, and the spoils for the winners are huge, but the diversity of technology is discouraging collaborat Read more…

2022 HPC Road Trip: Los Alamos

November 23, 2022

With SC22 in the rearview mirror, it’s time to get back to the 2022 Great American Supercomputing Road Trip. To refresh everyone’s memory, I jumped in the c Read more…

QuEra’s Quest: Build a Flexible Neutral Atom-based Quantum Computer

November 23, 2022

Last month, QuEra Computing began providing access to its 256-qubit, neutral atom-based quantum system, Aquila, from Amazon Braket. Founded in 2018, and built o Read more…

Nvidia Shuts Out RISC-V Software Support for GPUs 

September 23, 2022

Nvidia is not interested in bringing software support to its GPUs for the RISC-V architecture despite being an early adopter of the open-source technology in its GPU controllers. Nvidia has no plans to add RISC-V support for CUDA, which is the proprietary GPU software platform, a company representative... Read more…

RISC-V Is Far from Being an Alternative to x86 and Arm in HPC

November 18, 2022

One of the original RISC-V designers this week boldly predicted that the open architecture will surpass rival chip architectures in performance. "The prediction is two or three years we'll be surpassing your architectures and available performance with... Read more…

AWS Takes the Short and Long View of Quantum Computing

August 30, 2022

It is perhaps not surprising that the big cloud providers – a poor term really – have jumped into quantum computing. Amazon, Microsoft Azure, Google, and th Read more…

Chinese Startup Biren Details BR100 GPU

August 22, 2022

Amid the high-performance GPU turf tussle between AMD and Nvidia (and soon, Intel), a new, China-based player is emerging: Biren Technology, founded in 2019 and headquartered in Shanghai. At Hot Chips 34, Biren co-founder and president Lingjie Xu and Biren CTO Mike Hong took the (virtual) stage to detail the company’s inaugural product: the Biren BR100 general-purpose GPU (GPGPU). “It is my honor to present... Read more…

AMD Thrives in Servers amid Intel Restructuring, Layoffs

November 12, 2022

Chipmakers regularly indulge in a game of brinkmanship, with an example being Intel and AMD trying to upstage one another with server chip launches this week. But each of those companies are in different positions, with AMD playing its traditional role of a scrappy underdog trying to unseat the behemoth Intel... Read more…

Tesla Bulks Up Its GPU-Powered AI Super – Is Dojo Next?

August 16, 2022

Tesla has revealed that its biggest in-house AI supercomputer – which we wrote about last year – now has a total of 7,360 A100 GPUs, a nearly 28 percent uplift from its previous total of 5,760 GPUs. That’s enough GPU oomph for a top seven spot on the Top500, although the tech company best known for its electric vehicles has not publicly benchmarked the system. If it had, it would... Read more…

JPMorgan Chase Bets Big on Quantum Computing

October 12, 2022

Most talk about quantum computing today, at least in HPC circles, focuses on advancing technology and the hurdles that remain. There are plenty of the latter. F Read more…

Using Exascale Supercomputers to Make Clean Fusion Energy Possible

September 2, 2022

Fusion, the nuclear reaction that powers the Sun and the stars, has incredible potential as a source of safe, carbon-free and essentially limitless energy. But Read more…

Leading Solution Providers

Contributors

UCIe Consortium Incorporates, Nvidia and Alibaba Round Out Board

August 2, 2022

The Universal Chiplet Interconnect Express (UCIe) consortium is moving ahead with its effort to standardize a universal interconnect at the package level. The c Read more…

Nvidia, Qualcomm Shine in MLPerf Inference; Intel’s Sapphire Rapids Makes an Appearance.

September 8, 2022

The steady maturation of MLCommons/MLPerf as an AI benchmarking tool was apparent in today’s release of MLPerf v2.1 Inference results. Twenty-one organization Read more…

SC22 Unveils ACM Gordon Bell Prize Finalists

August 12, 2022

Courtesy of the schedule for the SC22 conference, we now have our first glimpse at the finalists for this year’s coveted Gordon Bell Prize. The Gordon Bell Pr Read more…

Intel Is Opening up Its Chip Factories to Academia

October 6, 2022

Intel is opening up its fabs for academic institutions so researchers can get their hands on physical versions of its chips, with the end goal of boosting semic Read more…

AMD’s Genoa CPUs Offer Up to 96 5nm Cores Across 12 Chiplets

November 10, 2022

AMD’s fourth-generation Epyc processor line has arrived, starting with the “general-purpose” architecture, called “Genoa,” the successor to third-gen Eypc Milan, which debuted in March of last year. At a launch event held today in San Francisco, AMD announced the general availability of the latest Epyc CPUs with up to 96 TSMC 5nm Zen 4 cores... Read more…

AMD Previews 400 Gig Adaptive SmartNIC SOC at Hot Chips

August 24, 2022

Fresh from finalizing its acquisitions of FPGA provider Xilinx (Feb. 2022) and DPU provider Pensando (May 2022) ), AMD previewed what it calls a 400 Gig Adaptive smartNIC SOC yesterday at Hot Chips. It is another contender in the increasingly crowded and blurry smartNIC/DPU space where distinguishing between the two isn’t always easy. The motivation for these device types... Read more…

Google Program to Free Chips Boosts University Semiconductor Design

August 11, 2022

A Google-led program to design and manufacture chips for free is becoming popular among researchers and computer enthusiasts. The search giant's open silicon program is providing the tools for anyone to design chips, which then get manufactured. Google foots the entire bill, from a chip's conception to delivery of the final product in a user's hand. Google's... Read more…

Not Just Cash for Chips – The New Chips and Science Act Boosts NSF, DOE, NIST

August 3, 2022

After two-plus years of contentious debate, several different names, and final passage by the House (243-187) and Senate (64-33) last week, the Chips and Science Act will soon become law. Besides the $54.2 billion provided to boost US-based chip manufacturing, the act reshapes US science policy in meaningful ways. NSF’s proposed budget... Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire