Monthly Archives: October 2020

Amid a pandemic that’s put much of the world’s work, learning, even family reunions online, two of the leaders who have made today’s virtual world possible met Thursday on, where else — Zoom — to talk about what’s next.

NVIDIA CEO Jensen Huang and Zoom CEO Eric Yuan spoke Thursday at the online video conference company’s Zoomtopia user event in a casual, wide-ranging conversation.

“If not for what Zoom has done, the recent pandemic would be unbearable,” Huang said. The present situation, Huang explained, “has accelerated the future, it has brought forward the urgency of a digital future.”

In front of a virtual audience from all over the globe, the two spoke about their entrepreneurial journeys, NVIDIA’s unique company culture, and how NVIDIA is knitting together the virtual and real worlds to help NVIDIA employees collaborate.

Huang’s appearance at Zoomtopia follows NVIDIA’s GPU Technology Conference last week, where Huang outlined NVIDIA’s view of data center computing and introduced new technologies in data centers, edge AI and healthcare.

Yuan playfully wore a leather jacket, matching Huang’s trademark attire—and briefly displayed a sleek virtual kitchen as his backdrop, paying tribute to the presentations Huang has given from his kitchen this year—began their conversation with Huang by asking about his early life.

“I was fortunate that my parents worked hard and all of the people I was surrounded by worked hard,” Huang said, adding that he was focused on on school and sports, especially table tennis. “To me working is living, working is breathing and, to me, it’s not work at all — I enjoy it too much.”

It’s NVIDIA’s mission, Huang said, that continues to motivate him, as the company has gone from inventing the GPU to pioneering new possibilities in robotics and AI.

The common thread: since the beginning, NVIDIA has had a singular focus on accelerated computing.

“We built a time machine,” Huang said, touching on NVIDIA’s work in drug discovery as an example. “So, instead of a particular drug taking 10 years to discover, we would like drugs and therapies and vaccines to be discovered in months.”

Zoom and NVIDIA, Huang said, share a “singular purpose and a sense of destiny,” Huang said, one that has made the world a better place.

“The fact that Zoom existed and your vision came to reality means we can be together even if we’re not together,” Huang said.

“You can look at your work and imagine the impact on society and the benefits it will bring and somehow it’s your job to do it,” Huang said. “If you don’t do it, no one else will — and that’s thrilling to me, I love that feeling.”

Yuan also asked about NVIDIA’s culture and the future of work, one which Huang believes will increasingly meld the physical and the virtual worlds.

Today, for example, we might report to your colleagues that we’ll be WFH, or working from home.

Office lingo, however, may change to reflect the new reality, where being at the office isn’t necessarily the norm.

“In the future we will say we’re ‘going to the office,’” Huang said. “Today we say ‘WFH,’ in the future we will say ‘GTO.’”

Tools such as Zoom enable colleagues to meet, face to face, from home, from an office, from anywhere in the world.

More and more, work will take place in a hybrid of office and home, physical and virtual reality.

NVIDIA, for example, has created a platform called NVIDIA Omniverse that lets colleagues working in different places and with different tools collaborate in real time.

“The Adobe world can connect to the Catia world and so on,” Huang said. “We can have different designers working with each other at their homes.”

The present moment has “brought forward the urgency of a digital future, it has made us aware that completely physical is not sufficient, that completely digital is not sufficient,” Huang said. “The future is a mixed reality world.”

Four new supercomputers backed by a pan-European initiative will use NVIDIA’s data center accelerators, networks and software to advance AI and high performance computing.

They include one system dubbed Leonardo, unveiled today at Italy’s CINECA research center, using NVIDIA technologies to deliver the world’s most powerful AI system. The four mark the first of eight systems to be announced this year targeting spots among the world’s 50 most powerful computers.

Together, they’ll form a regional network, “an engine to power Europe’s data economy,” said EuroHPC, the group driving the effort, in a white paper outlining its goals.

The systems will apply AI and data analytics across scientific and commercial applications that range from fighting COVID-19 and climate change to the design of advanced airplanes, cars, drugs and materials.

Joining Leonardo are a wave of new AI supercomputers planned for the Czech Republic, Luxembourg and Slovenia that will act as national centers of competence, expanding skills and creating jobs.

NVIDIA GPUs, InfiniBand Power Latest Systems

All four supercomputers announced use NVIDIA Ampere architecture GPUs and NVIDIA Mellanox HDR InfiniBand networks to tap an ecosystem of hundreds of HPC and AI applications. Atos, an NVIDIA systems partner headquartered in France, will build three of the four systems; Hewlett Packard Enterprise will construct the fourth.

The new systems join 333 of the world’s TOP500 supercomputers powered by NVIDIA GPUs, networking or both.

NVIDIA GPUs accelerate 1,800 HPC applications, nearly 800 of them available today in the GPU application catalog and NGC, NVIDIA’s hub for GPU-optimized software.

The new systems all use HDR 200Gb/s InfiniBand for low latency, high throughput and in-network computing. It’s the latest version of InfiniBand, already powering supercomputers across Europe.

A Brief Tour of Europe’s Latest Supercomputers

Leonardo will be the world’s fastest AI supercomputer. Atos is harnessing nearly 14,000 NVIDIA Ampere architecture GPUs and HDR 200Gb/s InfiniBand networking to deliver a system with 10 exaflops of AI performance. It will use the InfiniBand Dragonfly+ network topology to deliver both flexibility and scalable performance.

Researchers at CINECA will apply that power to advance science, simulating planetary forces behind climate change and molecular movements inside a coronavirus. The center is perhaps best known for its work on Quantum Espresso, a suite of open source codes for modeling nanoscale materials for jobs such as engineering better batteries.

A new supercomputer in Luxembourg called MeluXina, also part of the EuroHPC network, will connect 800 NVIDIA A100 GPUs on HDR 200Gb/s InfiniBand links. The system, to be built by Atos and powered by green energy from wood waste, will pack nearly 500 petaflops of AI performance.

MeluXina will address commercial applications and scientific research. It plans to offer access to users leveraging HPC and AI to advance work in financial services as well as manufacturing and healthcare.

Eastern Europe Powers Up

The new Vega supercomputer at the Institute of Information Science in Maribor, Slovenia, (IZUM) will be based on the Atos BullSequana XH2000 system. The supercomputer, named after Slovenian mathematician Jurij Vega, includes 240 A100 GPUs and 1,800 HDR 200Gb/s InfiniBand end points.

Vega will help “ensure a new generation of experts and developers, as well as the wider Slovenian community, can meet new challenges within our national consortium and contribute to regional and European HPC initiatives,” said Aleš Bošnjak, IZUM’s director in a statement issued by EuroHPC.

The IT4Innovations National Supercomputing Center will host what’s expected to become the most powerful supercomputer in the Czech Republic. It will use 560 NVIDIA A100 GPUs to deliver nearly 350 petaflops of AI performance — 7x the capabilities of the center’s existing system.

The supercomputer will be based on the HPE Apollo 6500 systems from Hewlett Packard Enterprise (HPE). It will serve researchers at the VSB – Technical University of Ostrava, where it’s based, as well as an expanding set of external academic and industrial users employing a mix of simulations, data analytics and AI.

The story of Europe’s ambitions in HPC and AI is still being written.

EuroHPC has yet to announce its plans for systems in Bulgaria, Finland, Portugal and Spain. And beyond that work, the group has already sketched out plans that stretch to 2027.

Using AI and a supercomputer simulation, Ken Dill’s team drew the equivalent of wanted posters for a gang of proteins that make up COVID-19. With a little luck, one of their portraits could identify a way to arrest the coronavirus with a drug.

When the pandemic hit, “it was terrible for the world, and a big research challenge for us,” said Dill, who leads the Laufer Center for Physical & Quantitative Biology at Stony Brook University, in Long Island, New York.

For a decade, he helped the center assemble the researchers and tools needed to study the inner workings of proteins — complex molecules that are fundamental to cellular life. The center has a history of applying its knowledge to viral proteins, helping others identify drugs to disable them.

“So, when the pandemic came, our folks wanted to spring into action,” he said.

AI, Simulations Meet at the Summit

The team aimed to use a combination of physics and AI tools to predict the 3D structure of more than a dozen coronavirus proteins based on lists of the amino acid strings that define them. It won a grant for time on the IBM-built Summit supercomputer at Oak Ridge National Laboratory to crunch its complex calculations.

“We ran 30 very extensive simulations in parallel, one on each of 30 GPUs, and we ran them continuously for at least four days,” explained Emiliano Brini, a junior fellow at the Laufer Center. “Summit is a great machine because it has so many GPUs, so we can run many simulations in parallel,” he said.

“Our physics-based modeling eats a lot of compute cycles. We use GPUs almost exclusively for their speed,” said Dill.

Sharing Results to Help Accelerate Research

Thanks to the acceleration, the predictions are already in. The Laufer team quickly shared them with about a hundred researchers working on a dozen separate projects that conduct painstakingly slow experiments to determine the actual structure of the proteins.

“They indicated some experiments could be done faster if they had hunches from our work of what those 3D structures might be,” said Dill.

Now it’s a waiting game. If one of the predictions gives researchers a leg up in finding a weakness that drug makers can exploit, it would be a huge win. It could take science one step closer to putting a general antiviral drug on the shelf of your local pharmacy.

Melding Machine Learning and Physics

Dill’s team uses a molecular dynamics program called MELD. It blends physical simulations with insights from machine learning based on statistical models.

AI provides MELD key information to predict a protein’s 3D structure from its sequence of amino acids. It quickly finds patterns across a database of atomic-level information on 200,000 proteins gathered over the last 50 years.

MELD uses this information in compute-intensive physics simulations to determine the protein’s detailed structure. Further simulations then can predict, for example, what drug molecules will bind tightly to a specific viral protein.

“So, both these worlds — AI inference and physics simulations — are playing big roles in helping drug discovery,” said Dill. “We get the benefits of both methods, and that combination is where I think the future is.”

MELD runs on CUDA, NVIDIA’s accelerated computing platform for GPUs. “It would take prohibitively long to run its simulations on CPUs, so the majority of biological simulations are done on GPUs,” said Brini.

Playing a Waiting Game

The COVID-19 challenge gave Laufer researchers with a passion for chemistry a driving focus. Now they await feedback on their work on Summit.

“Once we get the results, we’ll publish what we learn from the mistakes. Many times, researchers have to go back to the drawing board,” he said.

And every once in a while, they celebrate, too.

Dill hosted a small, socially distanced gathering for a half-dozen colleagues in his backyard after the Summit work was complete. If those results turn up a win, there will be a much bigger celebration extending far beyond the Stony Brook campus.

In the battle against COVID-19, Greater Paris University Hospitals – Public Assistance Hospital of Paris (AP-HP is the French acronym) isn’t just on the medical front lines — it’s on the data front lines as well.

With a network of 39 hospitals treating 8.3 million patients each year, AP-HP is a major actor in the fight against COVID-19.

Along with its COVID-19 cases comes an awful lot of data, including now geodata that can potentially help lessen the impact of the pandemic. AP-HP, which partners with seven universities, already had the ability to analyze large amounts of medical data. It had previously created dashboards that combined cancer cases and geodata. So, it was logical to pursue and extend its role during the pandemic.

The expected volume of COVID-19 data and geodata would probably have tested AP-HP’s data crunching capacity. To mitigate this critical challenge, the hospital’s information systems administrators turned to Kinetica, a provider of streaming data warehouses and real-time analytics and a member of the NVIDIA Inception program for AI startups.

Kinetica’s offering harnesses the power of NVIDIA GPUs to quickly convert case location data into usable intelligence. And in the fight against COVID-19, speed is everything.

The project team also used NVIDIA RAPIDS to speed up the machine learning algorithms integrated into the platform. RAPIDS accelerates analytics and data science pipelines on NVIDIA GPUs by taking advantage of GPU parallelism and high memory bandwidth.

“Having the ability to perform this type of analysis in real time is really important during a pandemic,” said Hector Countouris, the project lead at AP-HP. “And more data is coming.”

Analyzing COVID Contact Data

What Countouris and his colleagues are most focused on is using COVID-related geodata to understand where virus “hot spots” are and the dynamic of the outbreak. Looking for cluster locations can help decision-making at the district or region level.

In addition, they’re looking at new signals to improve early detection of COVID patients. This includes working with data from other regional agencies.

If patients are diagnosed with COVID, they’ll be asked by the relevant agencies via a phone call about their recent whereabouts and contacts to help with contact tracing. This is the first time that a wide range of data from different partners in the Paris area will be integrated to allow for contact tracing and timely alerts about a potential exposure. The result will be a newfound ability to see how clusters of COVID-19 cases evolve.

“We hope that in the near future we will be able to follow how a cluster evolves in real time,” said Countouris.

The goal is to enable public health decision-makers to implement prevention and control measures and assess their effectiveness. The data can also be integrated with other demographic data to study the viral spread and its possible dependency on socio-economics and other factors.

Attacking Bottlenecks with GPUs

Prior to engaging with Kinetica, such data-intensive projects involved so much time for loading the data that they couldn’t be analyzed quickly enough to deliver real-time benefits.

“Now, I don’t feel like I have a bottleneck,” said Countouris. “We are continuously integrating data and delivering dashboards to decision makers within hours. And with robust real-time pipelines allowing for continuous data ingestion, we can now focus on building better dashboards.”

In the past, to get data in a specific and usable format, they would need to do a lot of pre-processing. With Kinetica’s Streaming Data Warehouse powered by NVIDIA V100 Tensor Core GPUs, that’s no longer the case. Users can access the much richer datasets they demand.

Kinetica’s platform is available on NVIDIA NGC, a catalog of GPU-optimized AI containers that let enterprises quickly operationalize extreme analytics, machine learning and data visualization. This eliminates complexity and lets organizations deploy cloud, on-premises or hybrid models for optimal business operations.

“I don’t think we could meet user expectations for geodata without GPU power,” he said. “There is just too much data and geodata to provide for too many users at the same time.”

AP-HP’s COVID-related work has already built a foundation upon which to do follow-up work related to emergency responses in general. The hospital information system’s interest for that kind of data is far from over.

“The fact that we helped the decision-making process and that officials are using our data is the measure of success,” said Countouris. “We have a lot to do. This is only the beginning.”

Countouris presented the team’s work last week at the GPU Technology Conference. Registered GTC attendees can view the talk on demand. It will be available for replay to the general public early next month.

Kinetica will also be part of the NVIDIA Startup Village Booth at the HLTH conference, presenting on Oct. 16 at 2 p.m. Pacific time.