Data Science Sparks the Gap Between HPC and Hyperscale


Nvidia’s new data science servers can handle the entire data science workflow.

In a wide-ranging keynote address that ran nearly three hours at the GPU Technology Conference in San Jose, Nvidia co-founder and chief executive officer Jensen Huang talked up the company’s successes and new products across its graphics, robotics, and AI and HPC lineups. Huang also brought Mellanox co-founder and chief executive officer Eyal Waldman onstage to welcome Mellanox into the Nvidia fold, just a week after purchasing the company for $6.9 billion.

In the HPC space, Nvidia is still basking in the glow of its 2018 achievements, which saw the company expand its dominance in both traditional and AI-flavored HPC with its Tesla V100 GPU. In that regard, Huang reminded GTC attendees that his company’s GPUs now power the most powerful supercomputers in the United States (Summit), Europe (Piz Daint), and Japan (ABCI) and are running inside 22 of the 25 most energy-efficient systems in the world. Huang also noted that GPU computing is penetrating more deeply into commercial domains like retail, manufacturing, and finance as demand for data analytics and machine learning in those areas continues to grow. “They will all be high performance computing customers,” he said.

But what Huang really wanted to get across was the idea that data science has become the new driver across the computing landscape, encompassing analytics, AI/machine learning, and inferencing. According to Huang, there are three factors that make data science such a big deal today: the generation of enormous amount of data from sensors and other devices, breakthroughs in machine learning algorithms that produce highly accurate models, and the availability of large amounts of computational hardware (meaning Nvidia GPUs). But, as in all things computational, there never seems to be enough. “Data science is the new HPC challenge,” Huang asserted.

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