Nvidia Riding High as GPU Workloads and Capabilities Soar

By Alex Woodie

March 27, 2018

Don’t look now, but GPUs are gobbling up big workloads as massive data-crunching AI use cases proliferate around the world. That news couldn’t be sweeter for Nvidia, which is hosting its ninth annual GPU Technology Conference (GTC) this week in San Jose, California.

GPUs have come a long way from the early 1990s, when Jensen Huang, Chris Malachowsky, and Curtis Priem founded Nvidia. At the time, rendering lifelike graphics was one of the toughest computational challenges, so the trio decided to tackle the development of a massively multicore processor that could offload the graphics work from the main CPU.

The company’s GPUs became hot commodities among a certain demographic, i.e. young male video game enthusiasts. But making first-person shooters pop doesn’t explain how Nvidia’s stock has risen 1,900 percent over the past five years and made the Santa Clara company a $150 billion giant.

Rise of GPUs

Jensen Huang at SC17

We’ve known for some time that GPUs are good at other things besides driving high quality graphics for video games. In the mid 2000s, the high performance computing (HPC) crowd discovered how much faster GPUs are at math than CPUs, and so they started building them into supercomputers as accelerators.

More recently, Web giants like Google have discovered that GPUs are good at something else: deep learning.

By training neural networks upon massive amounts of data, hyperscalers discovered they could get an iterative improvement on machine learning tasks like image recognition and natural language processing (NLP). GPUs aren’t required for training neural networks, but they sure can make them run faster.

Nvidia isn’t the only player in the emerging deep learning world, but it’s one of the central figures that has made the deep learning revolution possible. What’s more, its GTC conferences are showcases for what’s becoming possible with the combination of GPUs and AI.

The rise of deep learning has given artificial intelligence (AI) a shot in the arm and opened the door to using computers to solve problems that previously were deemed unsolvable. Without GPU-powered deep learning, we would not have had a mad rush to develop the first truly autonomous car. The number of other use cases for deep learning — in radiology, pharmacology, bioinformatics, security, and more — continues to grow on a weekly basis.

Not every session at GTC this week is about AI, but it’s safe to say that a majority of them are. “This has become one of the premier conferences in the world around AI,” Greg Estes, vice president of developer programs at Nvidia, said in a press briefing yesterday.

More than 8,000 people have registered for the four-and-a-half day GTC conference, which is taking place in the San Jose McEnery Convention Center, a few miles from Nvidia’s headquarters. According to Estes, the number could exceed 9,000, which would blow away last year’s attendance figure of 6,500 attendees.

‘Meteoric Rise’

Ian Buck, vice president and general manager of accelerated computing at Nvidia, shared some impressive figures about the rise of GPUs around the world.

When Nvidia launched its first datacenter product in 2006, a single Nvidia GPU boasted a 4x performance advantage over a comparable CPU. With the “Volta” V100s installed in supercomputers today, the advantage has widened considerably.

Nvidia’s stock price has risen 1,900% over the past five years

“We are now 20x faster than a comparable CPU node. That’s 1.7x faster year-over-year performance, clearly outstripping Moore’s law,” he said.

What’s more, GPUs will power 50 percent of the floating point computational horsepower for the top 50 supercomputers in the world. “That’s a 15x increase in five years from where we were,” he continued. “We’re obviously experiencing a meteoric rise in GPU computing and acceleration.”

That type of growth would not be possible if Nvidia didn’t gamble by making big changes in how it developed hardware and how it developed software to exploit that hardware, Buck said.

“It really speaks to how we are innovating and how we think about our products and how we think about … the market,” Buck said. “We change our architectures. We change our instruction sets. We’re not afraid to do so because we’re actually developing a new kind of…accelerated computing model, that’s breaking the old rules and running this … faster than Moore’s law.”

Deep learning is delivering huge gains in that field. According to Buck, deep learning has delivered a 190x performance boost for image recognition compared to traditional machine learning methods. A neural language translator saw a 50x boost. Speech recognition is up 60x, while voice synthesis — or voice generation — has improved 36x.While the HPC market may have given Nvidia enterprise credibility, it’s the rise of AI and deep learning that seems to have investors excited. The company is working closely with nearly every major tech company — from IBM and SAP to Facebook and Google — to exploit the architectural advantages that its GPU architecture provides for powering neural networks.

The first generation of deep learning systems revolved around two main problems: image recognition and NLP. We’re now seeing deep learning being applied to a broader range of challenges, and Nvidia is at the forefront in making it happen with its GPUs and software frameworks.

“As these neural networks are getting smarter, they’re doing more things. They’re applying new use cases, they’re getting larger,” Buck said. “Naturally they have to do more and get more accurate. They’re growing.”

Nvidia CEO Huang takes the stage this morning to deliver his highly anticipated keynote address. In the past, this is how Nvidia made major announcements, like unveiling new GPUs. But far from just selling super-fast GPUs, the company is positioning itself to be a full-stack platform provider, and the shepherd of an ecosystem that’s emerging around AI and deep learning. With hundreds of partners, Nvidia is posed to be a key player as AI use cases expand, and GTC is its showcase for that work.

It’s all about creating “a different kind of computing,” Buck said. “We don’t try to build a processor that tries to run a fixed instruction set.  We’re building an entire platform and innovating in all layers of that stack.”

A version of this article originally appeared on our sister site, Datanami.

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