Posts in category: nVidia AI

Two key components of enterprise AI just snapped in place thanks to longtime partners who pioneered virtual desktops, virtual graphics workstations and more.

Taking their partnership to a new level, VMware and NVIDIA are uniting accelerated computing and virtualization to bring the power of AI to every company.

It’s a collaboration that will enable users to run data analytics and machine learning workloads in containers or virtual machines, secured and managed with familiar VMware tools. It will create a new sweet spot in hybrid cloud computing with greater control, lowered costs and expanded performance.

The partnership plants behind the firewalls of private companies the power of AI that public clouds provide from the world’s largest AI data centers.

The two companies will demonstrate these capabilities this week at VMworld.

Welcome to the Modern, Accelerated Data Center

Thanks to this collaboration, users will be able to run AI and data science software from NGC Catalog, NVIDIA’s hub for GPU-optimized AI software, using containers or virtual machines in a hybrid cloud based on VMware Cloud Foundation. It’s the kind of accelerated computing that’s a hallmark of the modern data center.

NVIDIA and VMware also launched a related effort enabling users to build a more secure and powerful hybrid cloud accelerated by NVIDIA BlueField-2 DPUs. These data processing units are built to offload and accelerate software-defined storage, security and networking tasks, freeing up CPU resources for enterprise applications.

Enterprises Gear Up for AI

Machine learning lets computers write software humans never could. It’s a capability born in research labs that’s rapidly spreading to data centers across every industry from automotive and banking to healthcare, retail and more.

The partnership will let VMware users train and run neural networks across multiple GPUs in public and private clouds. It also will enable them to share a single GPU across multiple jobs or users thanks to the multi-instance capabilities in the latest NVIDIA A100 GPUs.

To achieve these goals, the two companies will bring GPU acceleration to VMware vSphere to run AI and data-science jobs at near bare-metal performance next to existing enterprise apps on standard enterprise servers. In addition, software and models in NGC will support VMware Tanzu.

With these links, AI workloads can be virtualized and virtual environments become AI-ready without sacrificing system performance. And users can create hybrid clouds that give them the choice to run jobs in private or public data centers.

Companies will no longer need standalone AI systems for machine learning or big data analytics that are separate from their IT resources. Now a single enterprise infrastructure can run AI and traditional workloads managed by VMware tools and administrators.

“We’re providing the best of both worlds by bringing mature management capabilities to bare-metal systems and great performance to virtualized AI workloads,” said Kit Colbert, vice president and CTO of VMware’s cloud platform group.

Demos Show the Power of Two

Demos at VMworld will show a platform that delivers AI results fast as the public cloud and robust enough to tackle critical jobs like fighting COVID-19. They will run containers from NVIDIA NGC, managed by Tanzu, on VMware Cloud Foundation.

We’ll show those same VMware environments also tapping into the power of BlueField-2 DPUs to secure and accelerate hybrid clouds that let remote designers collaborate in an immersive, real-time environment.

That’s just the beginning. NVIDIA is committed to giving VMware the support to be a first-class platform for everything we build. In the background, VMware and NVIDIA engineers are driving a multi-year effort to deliver game-changing capabilities.

Colbert of VMware agreed. “We view the two initiatives we’re announcing today as initial steps, and there is so much more we can do. We invite customers to tell us what they need most to help prioritize our work,” he said.

To learn more, register for the early-access program and tune in to VMware sessions at GTC 2020 next week.

The data center’s grid is about to plug in to a new source of power.

It rides a kind of network interface card called a SmartNIC. Its smarts and speed spring from an ASIC called a data processing unit.

In short, the DPU packs the power of data center infrastructure on a chip.

DPU-enabled SmartNICs will be available for millions of virtualized servers thanks to a collaboration between VMware and NVIDIA. They bring advances in security and storage as well as networking that will stretch from the core to the edge of the corporate network.

What’s more, the companies announced a related initiative that will put the power of the public AI cloud behind the corporate firewall. It enables enterprise AI managed with familiar VMware tools.

Lighting Up the Modern Data Center

Together, these efforts will give users the choice to run machine learning workloads in containers or virtual machines, secured and managed with familiar VMware tools. And they will create a new sweet spot in hybrid cloud computing with greater control, lowered costs and the highest performance.

Laying the foundation for these capabilities, the partnership will help users build more secure and powerful distributed networks inside VMware Cloud Foundation, powered by the NVIDIA BlueField-2 DPU. It’s the Swiss Army knife of data center infrastructure that can accelerate security, storage, networking, and management tasks, freeing up CPUs to focus on enterprise applications.

The DPU’s jobs include:

• Blocking malware
• Advanced encryption
• Network virtualization
• Load balancing
• Intrusion detection and prevention
• Data compression
• Packet switching
• Packet inspection
• Managing pools of solid-state and hard-disk storage

Our DPUs can run these tasks today across two ports, each carrying traffic at 100 Gbit/second. That’s an order of magnitude faster than CPUs geared for enterprise apps. The DPU is taking on these jobs so CPU cores can run more apps, boosting vSphere and data center efficiency.

As a result, data centers can handle more apps and their networks will run faster, too.

“The BlueField-2 SmartNIC is a fundamental building block for us because we can take advantage of its DPU hardware for better network performance and dramatically reduced cost to operate data center infrastructure,” said Kit Colbert, vice president and CTO of VMware’s cloud platform group.

Securing the Data Center with DPUs

DPUs also will usher in a new era of advanced security.

Today, most companies run their security policies on the same CPUs that run their applications. That kind of multitasking leaves IT departments vulnerable to malware or attacks in the guise of a new app.

With the BlueField DPU, all apps and requests can be vetted on a processor isolated from the application domain, enforcing security and other policies. Many cloud computing services already use this approach to create so-called zero-trust environments where software authenticates everything.

VMware is embracing SmartNICs in its products as part of an initiative called Project Monterey. With SmartNICs, corporate data centers can take advantage of the same advances Web giants enjoy.

“These days the traditional security perimeter is gone. So, we believe you need to root security in the hardware of the SmartNIC to monitor servers and network traffic very fast and without performance impacts,” said Colbert.

See DPUs in Action at VMworld

The companies are demonstrating these capabilities this week at VMworld. For example, the demo below shows how virtual servers running VMware ESXi clients can use Bluefield-2 DPUs to stop a distributed denial-of-service attack in a server cluster.

Leading OEMs are already preparing to bring the capabilities of DPUs to market. NVIDIA also plans to support BlueField-2 SmartNICs across its portfolio of platforms including its EGX systems for enterprise and edge computing.

You wouldn’t hammer a nail with a monkey wrench or pound in a screw with a hammer — you need to use the right tool for the job. To build the modern data center network, that means using an NVIDIA DPU enabled by VMware.

Drug discovery is like searching for the right jigsaw tile — in a puzzle box with 1060 molecular-size pieces. AI and HPC tools help researchers more quickly narrow down the options, like picking out a subset of correctly shaped and colored puzzle pieces to experiment with.

An effective small-molecule drug will bind to a target enzyme, receptor or other critical protein along the disease pathway. Like the perfect puzzle piece, a successful drug will be the ideal fit, possessing the right shape, flexibility and interaction energy to attach to its target.

But it’s not enough just to interact strongly with the target. An effective therapeutic must modify the function of the protein in just the right way, and also possess favorable absorption, distribution, metabolism, excretion and toxicity properties — creating a complex optimization problem for scientists.

Researchers worldwide are racing to find effective vaccine and drug candidates to inhibit infection with and replication of SARS-CoV-2, the virus that causes COVID-19. Using NVIDIA GPUs, they’re accelerating this lengthy discovery process — whether for structure-based drug design, molecular docking, generative AI models, virtual screening or high-throughput screening.

Identifying Protein Targets with Genomics

To develop an effective drug, researchers have to know where to start. A disease pathway — a chain of signals between molecules that trigger different cell functions — may involve thousands of interacting proteins. Genomic analyses can provide invaluable insights for researchers, helping them identify promising proteins to target with a specific drug.

With the NVIDIA Clara Parabricks genome analysis toolkit, researchers can sequence and analyze genomes up to 50x faster. Given the unprecedented spread of the COVID pandemic, getting results in hours versus days can have an extraordinary impact on understanding the virus and developing treatments.

To date, hundreds of institutions, including hospitals, universities and supercomputing centers, in 88 countries have downloaded the software to accelerate their work — to sequence the viral genome itself, as well as to sequence the DNA of COVID patients and investigate why some are more severely affected by the virus than others.

Another method, cryo-EM, uses electron microscopes to directly observe flash-frozen proteins — and can harness GPUs to shorten processing time for the complex, massive datasets involved.

Using CryoSPARC, a GPU-accelerated software built by Toronto startup Structura Biotechnology, researchers at the National Institutes of Health and the University of Texas at Austin created the first 3D, atomic-scale map of the coronavirus, providing a detailed view into the virus’ spike proteins, a key target for vaccines, therapeutic antibodies and diagnostics.

GPU-Accelerated Compound Screening

Once a target protein has been identified, researchers search for candidate compounds that have the right properties to bind with it. To evaluate how effective drug candidates will be, researchers can screen drug candidates virtually, as well as in real-world labs.

New York-based Schrödinger creates drug discovery software that can model the properties of potential drug molecules. Used by the world’s biggest biopharma companies, the Schrödinger platform allows its users to determine the binding affinity of a candidate molecule on NVIDIA Tensor Core GPUs in under an hour and with just a few dollars of compute cost — instead of many days and thousands of dollars using traditional methods.

Generative AI Models for Drug Discovery

Rather than evaluating a dataset of known drug candidates, a generative AI model starts from scratch. Tokyo-based startup Elix, Inc., a member of the NVIDIA Inception virtual accelerator program, uses generative models trained on NVIDIA DGX Station systems to come up with promising molecular structures. Some of the AI’s proposed molecules may be unstable or difficult to synthesize, so additional neural networks are used to determine the feasibility for these candidates to be tested in the lab.

With DGX Station, Elix achieves up to a 6x speedup on training the generative models, which would otherwise take a week or more to converge, or to reach the lowest possible error rate.

Molecular Docking for COVID-19 Research

With the inconceivable size of the chemical space, researchers couldn’t possibly test every possible molecule to figure out which will be effective to combat a specific disease. But based on what’s known about the target protein, GPU-accelerated molecular dynamics applications can be used to approximate molecular behavior and simulate target proteins at the atomic level.

Software like AutoDock-GPU, developed by the Center for Computational Structural Biology at the Scripps Research Institute, enables researchers to calculate the interaction energy between a candidate molecule and the protein target. Known as molecular docking, this computationally complex process simulates millions of different configurations to find the most favorable arrangement of each molecule for binding. Using the more than 27,000 NVIDIA GPUs on Oak Ridge National Laboratory’s Summit supercomputer, scientists were able to screen 1 billion drug candidates for COVID-19 in just 12 hours. Even using a single NVIDIA GPU provides more than 230x speedup over using a single CPU.

In Illinois, Argonne National Laboratory is accelerating COVID-19 research using an NVIDIA A100 GPU-powered system based on the DGX SuperPOD reference architecture. Argonne researchers are combining AI and advanced molecular modelling methods to perform accelerated simulations of the viral proteins, and to screen billions of potential drug candidates, determining the most promising molecules to pursue for clinical trials.

Accelerating Biological Image Analysis

The drug discovery process involves significant high-throughput lab experiments as well. Phenotypic screening is one method of testing, in which a diseased cell is exposed to a candidate drug. With microscopes, researchers can observe and record subtle changes in the cell to determine if it starts to more closely resemble a healthy cell. Using AI to automate the process, thousands of possible drugs can be screened.

Digital biology company Recursion, based in Salt Lake City, uses AI and NVIDIA GPUs to observe these subtle changes in cell images, analyzing terabytes of data each week. The company has released an open-source COVID dataset, sharing human cellular morphological data with researchers working to create therapies for the virus.

Future Directions in AI for Drug Discovery

As AI and accelerated computing continue to accelerate genomics and drug discovery pipelines, precision medicine — personalizing individual patients’ treatment plans based on insights about their genome and their phenotype — will become more attainable.

Increasingly powerful NLP models will be applied to organize and understand massive datasets of scientific literature, helping connect the dots between independent investigations. Generative models will learn the fundamental equations of quantum mechanics and be able to suggest the optimal molecular therapy for a given target.

To learn more about how NVIDIA GPUs are being used to accelerate drug discovery, check out talks by Schrödinger, Oak Ridge National Laboratory and Atomwise at the GPU Technology Conference next week.

For more on how AI and GPUs are advancing COVID research, read our blog stories and visit the COVID-19 research hub.

Subscribe to NVIDIA healthcare news here. 

Back to school was destined to look different this year.

With the world adapting to COVID-19, safety measures are preventing a return to in-person teaching in many places. Also, students learning through conventional video conferencing systems often feel the content is difficult to read, or teachers block the words written on presentation boards.

Faced with these challenges, educators at Prefectural University of Hiroshima in Japan envisioned a high-quality remote learning system with additional features not possible with traditional video conferencing.

They chose a distance-learning solution from Sony that links lecturers and students across their three campuses. It uses AI to make it easy for presenters anywhere to engage their audiences and impart information using captivating video. Thanks to these innovations, lecturers at Prefectural University can now teach students simultaneously on three campuses linked by a secure virtual private network.

AI Helps Lecturers Get Smarter About Remote Learning

At the heart of Prefectural’s distance learning system is Sony’s REA-C1000 Edge Analytics Appliance, which was developed using the NVIDIA Jetson Edge AI platform. The appliance lets teachers and speakers quickly create dynamic video presentations without using expensive video production gear or learning sophisticated software applications.

Sony’s exclusive AI algorithms run inside the appliance. These deep learning models employ techniques such as automatic tracking, zooming and cropping to allow non-specialists to produce engaging, professional-quality video in real time.

Users simply connect the Edge Analytics Appliance to a camera that can pan, tilt and zoom; a PC; and a display or recording device. In Prefectural’s case, multiple cameras capture what a lecturer writes on the board, questions and contributions from students, and up to full HD images depending on the size of the lecture hall.

Managing all of this technology is made simple for the lecturers. A touchscreen panel facilitates intuitive operation of the system without the need for complex adjustment of camera settings.

Teachers Achieve New Levels of Transparency

One of the landmark applications in the Edge Analytics Appliance is handwriting extraction, which lets students experience lectures more fully, rather than having to jot down notes.

The application uses a camera to record text and figures as an instructor writes them by hand on a whiteboard or blackboard, and then immediately draws them as if they are floating in front of the instructor.

Students viewing the lecture live from a remote location or from a recording afterward can see and recognize the text and diagrams, even if the original handwriting is unclear or hidden by the instructor’s body. The combined processing power of the compact, energy-efficient Jetson TX2 and Sony’s moving/unmoving object detection technology makes the transformation from the board to the screen seamless.

Handwriting extraction is also customizable: the transparency of the floating text and figures can be adjusted, so that characters that are faint or hard to read can be highlighted in color, making them more legible — and even more so than the original content written on the board.

Create Engaging Content Without Specialist Resources

Another innovative application is Chroma key-less CG overlay, using state-of-the-art algorithms from Sony, like moving-object detection, to produce class content without the need for large-scale video editing equipment.

Like a personal greenscreen for presenters, the application seamlessly places the speaker in front of any animations, diagrams or graphs being presented.

Previously, moving-object detection algorithms required for this kind of compositing could only be run on professional workstations. With Jetson TX2, Sony was able to include this powerful deep learning-based feature within the compact, simple design of the Edge Analytics Appliance.

A Virtual Camera Operator

Numerous additional algorithms within the appliance include those for color-pattern matching, shape recognition, pose recognition and more. These enable features such as:

• PTZ Auto Tracking — automatically tracks an instructor’s movements and ensures they stay in focus.
• Focus Area Cropping — crops a specified portion from a video recorded on a single camera and creates effects as if the cropped portion were recorded on another camera. This can be used to generate, for example, a picture-in-picture effect, where an audience can simultaneously see a close-up of the presenter speaking against a wide shot of the rest of the stage.
• Close Up by Gesture — automatically zooms in on and records students or audience members who stand up in preparation to ask a question.

With the high-performance Jetson platform, the Edge Analytics Appliance can easily handle a wide range of applications like these. The result is like a virtual camera operator that allows people to create engaging, professional-looking video presentations without the expertise or expense previously required to do so.

Officials at Prefectural University of Hiroshima say the new distance learning initiative has already led to greater student and teacher satisfaction with remote learning. Linking the university’s three campuses through the system is also fostering a sense of unity among the campuses.

“We chose Sony’s Edge Analytics Appliance for our new distance learning design because it helps us realize a realistic and comfortable learning environment for students by clearly showing the contents on the board and encouraging discussion. It was also appealing as a cost-effective solution as teachers can simply operate without additional staff,” said Kyousou Kurisu, director of public university corporation, Prefectural University of Hiroshima.

Sony plans to continually update applications available on the Edge Analytics Appliance. So, like any student, the system will only get better over time.

You may have never heard of Pat Hanrahan, but you have almost certainly seen his work.

His list of credits includes three Academy Awards, and his work on Pixar’s RenderMan rendering technology enabled Hollywood megahits Toy Story, Finding Nemo, Cars and Jurassic Park.

Hanrahan also founded Tableau Software — snatched up by Salesforce last year for nearly $16 billion — and has mentored countless technology companies as a Stanford professor.

Hanrahan is the most recent winner of the Turing Award, along with his longtime friend and collaborator Ed Catmull, a former president at Pixar and Disney Animation Studios. The award — a Nobel Prize, of sorts, in computer science —  was for their work in 3D computer graphics and computer-generated imagery.

He spoke Thursday at NTECH, NVIDIA’s annual internal engineering conference. The digital event was followed by a virtual chat between NVIDIA CEO Jensen Huang and Hanrahan, who taught a computer graphics course at NVIDIA’s Silicon Valley campus during its early days.

While the theme of his address was “You Can Be an Innovator,” the main takeaway is that a “curiosity about how things work” is a prerequisite.

Hanrahan said his own curiosity for art and studying how Rembrandt painted flesh tones led to a discovery. Artists of that Baroque period, he said, applied a technique in oil painting with layers, called impasto, for depth of skin tone. This led to his own deeper study of light’s interaction with translucent surfaces.

“Artists, they sort of instinctively figured it out,” he said. “They don’t know about the physics of light transport. Inspired by this whole idea of Rembrandt’s, I came up with a mathematical model.”

Hanrahan said innovative people need to be instinctively curious. He tested that out himself when interviewing job candidates in the early days of Pixar. “I asked everybody that I wanted to hire into the engineering team, ‘How does a toilet work?’ To be honest, most people did not know how their toilet worked,” he said, “and these were engineers.”

At the age of seven 7, he’d already lifted the back cover of the toilet to find out what made it work.

Hanrahan worked with Steve Jobs at Pixar. Jobs’s curiosity and excitement about touch-capacitive sensors — technology that dated back to the 1970s — would eventually lead to the touch interface of the iPhone, he said.

After the talk, Huang joined the video feed from his increasingly familiar kitchen at home and interviewed Hanrahan. The wide-ranging conversation was like a time machine, with questions and reminisces looking back 20 years and discussions peering forward to the next 20.

A new supercomputer under construction is designed to tackle some of the planet’s toughest life sciences challenges by speedily crunching vast quantities of environmental data.

The Japan Agency for Marine-Earth Science and Technology, or JAMSTEC, has commissioned tech giant NEC to build the fourth generation of its Earth Simulator. The new system, scheduled to become operational in March, will be based around SX-Aurora TSUBASA vector processors from NEC and NVIDIA A100 Tensor Core GPUs, all connected with NVIDIA Mellanox HDR 200Gb/s InfiniBand networking.

This will give it a maximum theoretical performance of 19.5 petaflops, putting it in the highest echelons of the TOP500 supercomputer ratings.

The new system will benefit from a multi-architecture design, making it suited to various research and development projects in the earth sciences field. In particular, it will act as an execution platform for efficient numerical analysis and information creation, coordinating data relating to the global environment.

Its work will span marine resources, earthquakes and volcanic activity. Scientists will gain deeper insights into cause-and-effect relationships in areas such as crustal movement and earthquakes.

The Earth Simulator will be deployed to predict and mitigate natural disasters, potentially minimizing loss of life and damage in the event of another natural disaster like the earthquake and tsunami that hit Japan in 2011.

Earth Simulator will achieve this by running large-scale simulations at high speed in ways previous generations of Earth Simulator couldn’t. The intent is also to have the system play a role in helping governments develop a sustainable socio-economic system.

The new Earth Simulator promises to deliver a multitude of vital environmental information. It also represents a quantum leap in terms of its own environmental footprint.

Earth Simulator 3, launched in 2015, offered a performance of 1.3 petaflops. It was a world beater at the time, outstripping Earth Simulators 1 and 2, launched in 2002 and 2009, respectively.

The fourth-generation model will deliver more than 15x the performance of its predecessor, while keeping the same level of power consumption and requiring around half the footprint. It’s able to achieve these feats thanks to major research and development efforts from NVIDIA and NEC.

The latest processing developments are also integral to the Earth Simulator’s ability to keep up with rising data levels.

Scientific applications used for earth and climate modelling are generating increasing amounts of data that require the most advanced computing and network acceleration to give researchers the power they need to simulate and predict our world.

NVIDIA Mellanox HDR 200Gb/s InfiniBand networking with in-network compute acceleration engines combined with NVIDIA A100 Tensor Core GPUs and NEC SX-Aurora TSUBASA provides JAMSTEC a world-leading marine research platform critical for expanding earth and climate science and accelerating discoveries.

When it comes to autonomous vehicle simulation testing, every detail must be on point.

With its high-fidelity automotive simulation model (ASM) on NVIDIA DRIVE Sim, global automotive supplier dSPACE is helping developers keep virtual self-driving true to the real world. By combining the modularity and openness of the DRIVE Sim simulation software platform with highly accurate vehicle models like dSPACE’s, every minor aspect of an AV can be thoroughly recreated, tested and validated.

The dSPACE ASM vehicle dynamics model makes it possible to simulate elements of the car — suspension, tires, brakes — all the way to the full vehicle powertrain and its interaction with the electronic control units that power actions such as steering, braking and acceleration.

As the world continues to work from home, simulation has become an even more crucial tool in autonomous vehicle development. However, to be effective, it must be able to translate to real-world driving.

dSPACE’s modeling capabilities are key to understanding vehicle behavior in diverse conditions, enabling the exhaustive and high-fidelity testing required for safe self-driving deployment.

Detailed Validation

High-fidelity simulation is more than just a realistic-looking car driving in a recreated traffic scenario. It means in any given situation, the simulated vehicle will behave just as a real vehicle driving in the real world would.

If an autonomous vehicle suddenly brakes on a wet road, there are a range of forces that affect how and where the vehicle stops. It could slide further than intended or fishtail, depending on the weather and road conditions. These possibilities require the ability to simulate dynamics such as friction and yaw, or the way the vehicle moves vertically.

The dSPACE ASM vehicle dynamics model includes these factors, which can then be compared with a real vehicle in the same scenario. It also tests how the same model acts in different simulation environments, ensuring consistency with both on-road driving and virtual fleet testing.

A Comprehensive and Diverse Platform

The NVIDIA DRIVE Sim platform taps into the computing horsepower of NVIDIA RTX GPUs to deliver a revolutionary, scalable, cloud-based computing platform, capable of generating billions of qualified miles for autonomous vehicle testing.

It’s open, meaning both users and partners can incorporate their own models in simulation for comprehensive and diverse driving scenarios.

dSPACE chose to integrate its vehicle dynamics ASM with DRIVE Sim due to its ability to scale for a wide range of testing conditions. When running on the NVIDIA DRIVE Constellation platform, it can perform both software-in-the-loop and hardware-in-the-loop testing, which includes the in-vehicle AV computer controlling the vehicle in the simulation process. dSPACE’s broad expertise and long track-record in hardware-in-the-loop simulation make for a seamless implementation of ASM on DRIVE Constellation.

Learn more about the dSPACE ASM vehicle dynamics in the DRIVE Sim platform at the company’s upcoming GTC session — register before Sept. 25 to receive Early Bird pricing.

Jeff Herbst is a fixture of the AI startup ecosystem. Which makes sense since he’s the VP of business development at NVIDIA and head of NVIDIA Inception, a virtual accelerator that currently has over 6,000 members in a wide range of industries.

Ahead of the GPU Technology Conference, taking place Oct. 5-9, Herbst joined AI Podcast host Noah Kravitz to talk about what opportunities are available to startups at the conference, and how NVIDIA Inception is accelerating startups in every industry.

Herbst, who now has almost two decades at NVIDIA under his belt, studied computer graphics at Brown University and later became a partner at a Silicon Valley premier technology law firm. He’s served as a board member and observer for dozens of startups over his career.

On the podcast, he provides his perspective on the future of the NVIDIA Inception program. As AI continues to expand into every industry, Herbst predicts that more and more startups will incorporate GPU computing.

Those interested can learn more through NVIDIA Inception programming at GTC, which will bring together the world’s leading AI startups and venture capitalists. They’ll participate in activities such as the NVIDIA Inception Premier Showcase, where some of the most innovative AI startups in North America will present, and a fireside chat with Herbst, NVIDIA founder and CEO Jensen Huang, and several CEOs of AI startups.

Key Points From This Episode:

• Herbst’s interest in supporting an AI startup ecosystem began in 2008 at the NVISION Conference — the precursor to GTC. The conference held an Emerging Company Summit, which brought together startups, reporters and VCs, and made Herbst realize that there were many young companies using GPU computing that could benefit from NVIDIA’s support.
• Herbst provides listeners with an insider’s perspective on how NVIDIA expanded from computer graphics to the cutting edge of AI and accelerated computing, describing how it was clear from his first days at the company that NVIDIA envisioned a future where GPUs were essential to all industries.

Tweetables:

“We love startups. Startups are the future, especially when you’re working with a new technology like GPU computing and AI” — Jeff Herbst [14:06]

“NVIDIA is a horizontal platform company — we build this amazing platform on which other companies, particularly software companies, can build their businesses” — Jeff Herbst [27:49]

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Eliu Huerta is harnessing AI and high performance computing (HPC) to observe the cosmos more clearly.

For several years, the astrophysics researcher has been chipping away at a grand challenge, using data to detect signals produced by collisions of black holes and neutron stars. If his next big design for a neural network is successful, astrophysicists will use it to find more black holes and study them in more detail than ever.

Such insights could help answer fundamental questions about the universe. They may even add a few new pages to the physics textbook.

Huerta studies gravitational waves, the echoes from dense stellar remnants that collided long ago and far away. Since Albert Einstein first predicted them in his theory of relativity, academics debated whether these ripples in the space-time fabric really exist.

Researchers ended the debate in 2015 when they observed gravitational waves for the first time. They used pattern-matching techniques on data from the Laser Interferometer Gravitational-Wave Observatory (LIGO), home to some of the most sensitive instruments in science.

Detecting Black Holes Faster with AI

Confirming the presence of just one collision took a supercomputer to process data the instruments could gather in a single day. In 2017, Huerta’s team showed how a deep neural network running on an NVIDIA GPU could find gravitational waves with the same accuracy in a fraction of the time.

“We were orders of magnitude faster and we could even see signals the traditional techniques missed and we did not train our neural net for,” said Huerta, who leads AI and gravity groups at the National Center for Supercomputing Applications at the University of Illinois, Urbana-Champaign.

The AI model Huerta used was based on data from tens of thousands of waveforms. He trained it on a single NVIDIA GPU in less than three hours.

Seeing in Detail How Black Holes Spin

This year, Huerta and two of his students created a more sophisticated neural network that can detect how two colliding black holes spin. Their AI model even accurately measured the faint signals of a small black hole when it was merging with a larger one.

It required data on 1.5 million waveforms. An IBM POWER9-based system with 64 NVIDIA V100 Tensor Core GPUs took 12 hours to train the resulting neural network.

To accelerate their work, Huerta’s team got access to 1,536 V100 GPUs on 256 nodes of the IBM AC922 Summit supercomputer at Oak Ridge National Laboratory.

Taking advantage of NVIDIA NVLink, a connection between Summit’s GPUs and its IBM POWER9 CPUs, they trained the AI model in just 1.2 hours.

The results, described in a paper in Physics Letters B, “show how the combination of AI and HPC can solve grand challenges in astrophysics,” he said.

Interestingly, the team’s work is based on WaveNet, a popular AI model for converting text-to-speech. It’s one of many examples of how AI technology that’s rapidly evolving in consumer and enterprise use cases is crossing over to serve the needs of cutting-edge science.

The Next Big Leap into Black Holes

So far, Huerta has used data from supercomputer simulations to detect and describe the primary characteristics of gravitational waves. Over the next year, he aims to use actual LIGO data to capture the more nuanced secondary characteristics of gravitational waves.

“It’s time to go beyond low-hanging fruit and show the combination of HPC and AI can address production-scale problems in astrophysics that neither approach can accomplish separately,” he said.

The new details could help scientists determine more accurately where black holes collided. Such information could help them more accurately calculate the Hubble constant, a measure of how fast the universe is expanding.

The work may require tracking as many as 200 million waveforms, generating training datasets 100x larger than Huerta’s team used so far. The good news is, as part of their July paper, they’ve already determined their algorithms can scale to at least 1,024 nodes on Summit.

Tallying Up the Promise of HPC+AI

Huerta believes he’s just scratching the surface of the promise of HPC+AI. “The datasets will continue to grow, so to run production algorithms you need to go big, there’s no way around that,” he said.

Meanwhile, use of AI is expanding to adjacent areas. The team used neural nets to classify the many, many galaxies found in electromagnetic surveys of the sky, work NVIDIA CEO Jensen Huang highlighted in his GTC keynote in May.

Separately, one of Huerta’s grad students used AI to describe the turbulence when neutron stars merge more efficiently than previous techniques. “It’s another place where we can go into the traditional software stack scientists use and replace an existing model with an accelerated neural network,” Huerta said.

To accelerate the adoption of its work, the team has released as open source code its AI models for cosmology and gravitational wave astrophysics.

“When people read these papers they may think it’s too good to be true, so we let them convince themselves that we are getting the results we reported,” he said.

The Road to Space Started at Home

As is often the case with landmark achievements, there’s a parent to thank.

“My dad was an avid reader. We spent lots of time together doing math and reading books on a wide range of topics,” Huerta recalled.

“When I was 13, he brought home The Meaning of Relativity by Einstein. It was way over my head, but a really interesting read.

“A year or so later he bought A Brief History of Time by Stephen Hawking. I read it and thought it would be great to go to Cambridge and learn about gravity. Years later that actually happened,” he said.

The rest is a history that Huerta is still writing.

For more on Huerta’s work, check on an article from Oak Ridge National Laboratory.

At top: An artist’s impression of gravitational waves generated by binary neutron stars. Credit: R. Hurt, Caltech/NASA Jet Propulsion Lab

Paul Edwards is helping carry the age-old business of giving loans into the modern era of AI.

Edwards started his career modeling animal behavior as a Ph.D. in numerical ecology. He left his lab coat behind to lead a group of data scientists at Scotiabank, based in Toronto, exploring how machine learning can improve predictions of credit risk.

The team believes machine learning can both make the bank more profitable and help more people who deserve loans get them. They aim to share later this year some of their techniques in hopes of nudging the broader industry forward.

Scorecards Evolve from Pencils to AI

The new tools are being applied to scorecards that date back to the 1950s when calculations were made with paper and pencil. Loan officers would rank applicants’ answers to standard questions, and if the result crossed a set threshold on the scorecard, the bank could grant the loan.

With the rise of computers, banks replaced physical scorecards with digital ones. Decades ago, they settled on a form of statistical modeling called a “weight of evidence logistic regression” that’s widely used today.

One of the great benefits of scorecards is they’re clear. Banks can easily explain their lending criteria to customers and regulators. That’s why in the field of credit risk, the scorecard is the gold standard for explainable models.

“We could make machine-learning models that are bigger, more complex and more accurate than a scorecard, but somewhere they would cross a line and be too big for me to explain to my boss or a regulator,” said Edwards.

Machine Learning Models Save Millions

So, the team looked for fresh ways to build scorecards with machine learning and found a technique called boosting.

They started with a single question on a tiny scorecard, then added one question at a time. They stopped when adding another question would make the scorecard too complex to explain or wouldn’t improve its performance.

The results were no harder to explain than traditional weight-of-evidence models, but often were more accurate.

“We’ve used boosting to build a couple decision models and found a few percent improvement over weight of evidence. A few percent at the scale of all the bank’s applicants means millions of dollars,” he said.

XGBoost Upgraded to Accelerate Scorecards

Edwards’ team understood the potential to accelerate boosting models because they had been using a popular library called XGBoost on an NVIDIA DGX system. The GPU-accelerated code was very fast, but lacked a feature required to generate scorecards, a key tool they needed to keep their models simple.

Griffin Lacey, a senior data scientist at NVIDIA, worked with his colleagues to identify and add the feature. It’s now part of XGBoost in RAPIDS, a suite of open-source software libraries for running data science on GPUs.

As a result, the bank can now generate scorecards 6x faster using a single GPU compared to what used to require 24 CPUs, setting a new benchmark for the bank. “It ended up being a fairly easy fix, but we could have never done it ourselves,” said Edwards.

GPUs speed up calculating digital scorecards and help the bank lift their accuracy while maintaining the models’ explainability. “When our models are more accurate people who are deserving of credit get the credit they need,” said Edwards.

Riding RAPIDS to the AI Age

Looking ahead, Edwards wants to leverage advances from the last few decades of machine learning to refresh the world of scorecards. For example, his team is working with NVIDIA to build a suite of Python tools for scorecards with features that will be familiar to today’s data scientists.

“The NVIDIA team is helping us pull RAPIDS tools into our workflow for developing scorecards, adding modern amenities like Python support, hyperparameter tuning and GPU acceleration,” Edwards said. “We think in six months we could have example code and recipes to share,” he added.

With such tools, banks could modernize and accelerate the workflow for building scorecards, eliminating the current practice of manually tweaking and testing their parameters. For example, with GPU-accelerated hyperparameter tuning, a developer can let a computer test 100,000 model parameters while she is having her lunch.

With a much bigger pool to choose from, banks could select scorecards for their accuracy, simplicity, stability or a balance of all these factors. This helps banks ensure their lending decisions are clear and reliable and that good customers get the loans they need.

Digging into Deep Learning

Data scientists at Scotiabank use their DGX system to handle multiple experiments simultaneously. They tune scorecards, run XGBoost and refine deep-learning models. “That’s really improved our workflow,” said Edwards.

“In a way, the best thing we got from buying that system was all the support we got afterwards,” he added, noting new and upcoming RAPIDS features.

Longer term, the team is exploring use of deep learning to more quickly identify customer needs. An experimental model for calculating credit risk already showed a 20 percent performance improvement over the best scorecard, thanks to deep learning.

In addition, an emerging class of generative models can create synthetic datasets that mimic real bank data but contain no information specific to customers. That may open a door to collaborations that speed the pace of innovation.

The work of Edwards’ team reflects the growing interest and adoption of AI in banking.

“Last year, an annual survey of credit risk departments showed every participating bank was at least exploring machine learning and many were using it day-to-day,” Edwards said.