Another Look at GPGPU

By Michael Feldman

April 13, 2007

The interest in the general-purpose computation on GPUs (GPGPU) is at an all-time high. If you've been reading this publication for the last several months, you've no doubt noticed we've devoted quite a bit of coverage to this topic since the middle of 2006. The event that triggered this upsurge in interest was AMD's acquisition of ATI in July of 2006, and the subsequent announcement of a product strategy that would bring graphics processors into the mainstream of general-purpose computing. In the fall of 2006 NVIDIA revealed its own GPGPU strategy with its CUDA initiative.

The movement of GPU towards mainstream computing has been taking place for some time. Because of the broader requirements from visualization and game software in recent years, graphics processors are shifting toward a more general-purpose architecture; they're becoming more programmable and more CPU-like. Now, with both AMD and NVIDIA spinning a compelling tale of graphics processors as high performance parallel processing engines, the promise of cheap HPC never seemed closer. But not everyone is cheerleading.

Ars Technica's Jon Stokes is one of those who is keeping his pompoms at his side. In a recent article he wrote: “Anybody's GPU, whether it's from NVIDIA or AMD/ATI, is a big, hot, power-hungry, beast of a coprocessor that's designed to do one thing extremely well: real-time 3D rendering for games. In fact, we can be even more specific and call a GPU a “Microsoft DirectX toaster.” These same DirectX toasters also just happen to offer significant speedups vs. a regular microprocessor for certain types of data-parallel workloads that are important in HPC.”

Speaking of NVIDIA specifically, he adds: “They have a floor wax that happens to taste pretty good, so they're trying to use it to break into the food business by marketing it as a dessert topping.”

OK. So Stokes is obviously not a fan. He doesn't reject the notion of general-purpose computing on GPUs outright; he just thinks the proper place for the current crop of GPUs is on the motherboards of gaming enthusiasts, not in the sockets of HPC servers. He brings up some of the downsides of doing HPC with graphics processors, namely high power usage, programmer difficulty, vendor lock-in, and backward compatibility. (He doesn't even mention the current lack of 64-bit floating-point support.) Most of these factors point to the current immaturity of the GPGPU world.

But the same disadvantages existed in x86 designs before competition, standard software libraries and tools, and processor technology advancements made that architecture suitable for supercomputing. These disadvantages are well understood by both AMD and NVIDIA and they're working to address them.

On the other hand, GPUs do have to overcome a hurdle that the x86 never faced: its reputation as a specialized device for graphics processing. In this instance, the success of GPUs in the game market cuts both ways. The high-volume chip production that results from the huge demand by the game industry provides low prices, which offers an incentive to enter the HPC market. But the market pressure to make GPUs more targeted to visualization applications in some cases pushes the design away from general-purpose computing. This seems like a Catch-22 type of model.

Some of this uneasiness is misplaced. All processors, even general-purpose CPUs, devote silicon that targets certain types of applications, for example, the SSE instructions on x86 for (coincidentally) stream processing. Also, the GPU manufacturers will probably end up developing separate lines of GPGPU-oriented offerings which are variants of their core graphics devices for gamers. Finding the proper balance between specialized and general-purpose technology will be the key.

There is a continuum of coprocessing specialization from FPGAs, to GPUs and Cell processors, to floating point coprocessors, like ClearSpeed boards. As you go from FPGAs (least specialized) to FP coprocessors (most specialized), prices go up as a reflection of volume demand, but the difficulty of programming the devices decreases. Cell processors and GPUs are somewhere in the middle and may represent a sweet spot for HPC acceleration, offering high performance/price and relatively easy, at or least attainable, programmability.

The bigger problem for GPUs may be PR. AMD and NVIDIA are going to have to convince system manufactures and ISVs that graphics processors will be a mainstream technology. The hardest part will be developing a GPGPU software ecosystem around these devices. Game developers and HPC programmers live in different worlds. To get the HPC crowd interested you have to stop talking about pixel shaders and DirectX and start talking about stream computing.

This is where companies like PeakStream and RapidMind can help. Their software development platforms are designed to hide the GPU's 'gaminess' from the programmer. In fact, the software interfaces in these platforms are such that the developer need not be concerned with the underlying processor hardware. At a somewhat lower level, AMD's CTM (“Close To Metal”) open hardware interface and NVIDIA's C compiler CUDA technology have been introduced to offer programmers high-level access to the graphics processors' capabilities. We're just at the beginning of the software side of GPGPU, so it's too early to say what the best programming model is. But everyone agrees that raising the level of software abstraction will help to drive GPUs into the mainstream.

As far as the suitability of the graphics hardware for HPC servers, the biggest problem will be power usage. Since the gamers were never that concerned about an extra 100 watts or so in their machines, energy-efficiency was never much of a design issue. But if you want to start putting high-powered GPUs in already overheated server nodes, the devices are going to have to run a lot cooler.

Ars Technica's Stokes has something to say on this topic as well. In an article published this week, he posits that GPUs will have to become less energy hoggish to penetrate into the HPC market. He believes that getting the devices onto 65 nm process technology may be a good way to do start. In general, GPUs are a process technology cycle behind CPUs; the current NVIDIA G80 devices are at 90 nm. The GPGPU trend may create the incentive to bring graphics processors into the same technology cycle as their CPU counterparts. Certainly as AMD starts creating the CPU/GPU Fusion hybrid processors, that process synchronization will have to occur. If Intel gets into the GPU game, they are almost sure to press their advantage in process technology for their graphics devices. This is just another example of how GPUs are becoming more CPU-like.

But it's not just that GPUs are becoming more like CPUs, it's that the applications are becoming more game-like, that is, more data parallel in nature. Seismic modeling, financial options pricing and computational biology are all examples of the kinds of workloads that can be greatly accelerated with graphics processors today. The next generation of software designed for increasingly sophisticated pattern recognition, data mining, and data analytics are also going to be rather well-suited to the GPU architecture. If, in five years, all the interesting software requires data parallelism, graphics processors are likely to be the commodity hardware solution. So get those pompoms ready.

—–

As always, comments about HPCwire are welcomed and encouraged. Write to me, Michael Feldman, at [email protected].

Subscribe to HPCwire's Weekly Update!

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

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire