How Nvidia sees data science as its next big market
Nvidia CEO Jensen Huang is betting that data science will meld data centers with high-performance computing. Here’s why it makes sense.
The infrastructure behind the video game industry is increasingly leading to new enterprise technology and business use cases to the point where you’ll have to pay attention to both to connect the dots for the future.
In a week in which the Game Developers Forum (see all GameSpot coverage) and Nvidia’s GPU Technology Conference (GTC) were happening at the same time, the overlap between the enterprise and gaming were hard to ignore. Nvidia and its graphics processors and subsequent ecosystem of developers focused on artificial intelligence. Machine learning and high-performance computing were a common thread between the two events.
Nvidia is a big driver behind this intersection of video games and the enterprise. CEO Jensen Huang outlined Nvidia’s strategy during the company’s analyst meeting. Huang’s comments related to how Nvidia got into the data center game — even more so since the company bought Mellanox. Let’s look at Huang’s approach:
“No longer was it sufficient to just accelerate graphics. We had to first simulate the physics and then accelerate the graphics. Because you have to simulate the water. You have to simulate the leaves blowing into wind. You have to simulate things, particle physics, as buildings crumbled. And so it was impossible to have animated all of that. We decided to simulate that. So we expanded the aperture of our accelerator, and we invented this idea, called CUDA, so that we could expand not just accelerating graphics, but the domain of virtual reality. About that time, when we transitioned from a graphics accelerator to a domain accelerator, we became an accelerated computing company. An accelerator accelerates a function. An accelerated computing platform accelerates a domain of applications.”
An accelerated computing company enables specific use cases via architecture more than one generalized effort.
Huang explained:
“There’s only one computer architecture that can boil the ocean, and that’s called the CPU. It’s general purpose. That’s its nature. That’s its weakness, too. Its strength is that it can run everything. It’s weakness is that it doesn’t run anything super well . . . This accelerated computing architecture must have vertical domains to focus on, otherwise known as the counter of horizontal, vertical. And so we select verticals strategically — strategically and methodically — so that we can: One, make a contribution by the time that it’s necessary. It’s sufficiently large to be able to sustain the enormous investment that we put into it. But it’s not so large as essentially a horizontal problem.”
GTC coverage: The future of graphics is unquestionably AI image generation: Jensen Huang | Pascal GeForce GTX cards could get ray tracing with driver update
From an approach that worked for graphics and video games, Nvidia is leveraging its software, GPUs, and stack to:
Autonomous vehicles broadly defined well beyond cars and trucks to forklifts and anything that moves. (See: Nvidia partners with Mercedes on artificial intelligence.)Medical imaging and healthcare. Smart cities.
Huang said:
“Cities of the future, factories of the future, buildings of the future will have three characteristics. The first characteristic is tons of sensors. The second characteristic? A bunch of computation at the edge — basically, the reflexes of that robotic city. It doesn’t have to go to a cognitive brain in the cloud. And then, third: Connected to a cognitive brain in the cloud. Those three characteristics are so that you can make decisions and plan.”
High-performance computing and data science.
Huang said:
“And the reason for that is this… Data science is the only high-performance computing problem we know where there’s millions of people. Millions of people in different fields of science, healthcare, financial services — they call them quants, insurance companies, retail, logistics, travel. You name it. Every single industry will benefit from data science.”
(Image: Nvidia)
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Add it up, and you can’t take Nvidia’s technology on ray tracing, which will apply to video games primarily, and not think about how it applies to other specific use cases.
Also: Nvidia goes Nano for latest Jetson release | Nvidia GauGAN takes rough sketches and creates ‘photo-realistic’ landscape images | Nvidia unwraps RTX and T4-based hardware and cloud instances
Jeffery Fisher, executive vice president Nvidia, told analysts that Nvidia has developed a server that’s optimized for cloud gaming. “We’ll sell a complete server. And on top of that, we will run our GeForce Now service, license to telcos, share revenue as this scales out. This gives us the opportunity to hit markets that we don’t currently address. And it gives telcos the opportunity to bring in more value-added customers into their ecosystem,” said Fisher.
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