Posts in category: nVidia AI

Online commerce has rocketed to trillions of dollars worldwide in the past decade, serving billions of consumers. Behind the scenes of this explosive growth in online sales is personalization driven by recommender engines.

Recommenders make shopping deeply personalized. While searching for products on e-commerce sites, they find you. Or suggestions can just appear. This wildly delightful corner of the internet is driven by ever more massive datasets and models.

NVIDIA Merlin is the rocket fuel of recommenders. Boosting training and inference, it enables businesses of all types to better harness data to build recommenders accelerated by NVIDIA GPUs.

The stakes are higher than ever for online businesses. Online sales in 2021 were expected to reach nearly $5 trillion worldwide, according to eMarketer, up nearly 17 percent from the prior year.

On some of the world’s largest online sites, even a 1 percent gain in relevance accuracy of recommendations can yield billions more sales.

Investment in recommender systems has become one of the biggest competitive advantages of internet giants today.

The market for recommenders is expected to reach $15.13 billion by 2026, up from $2.12 billion in 2020, according to Mordor Intelligence. The largest and fastest growing segment of the market for recommender engines is in the Asia Pacific region, according to the research firm.

But an industry challenge is that improved relevance requires more data and processing. This data consists of trillions of user-product interactions — clicks, views,  — on billions of products and consumer profiles.

Data of this scale can take days to train models. Yet the faster you can spin out new models informed by more data, the better your relevance.

The Merlin collection of models, methods, and libraries, includes tools for building deep learning-based systems capable of handling terabytes of data that can provide better predictions and increase clicks.

SNAP Taps Merlin and GPUs for Inference Upside

U.S. digital advertising is expected to reach $191.1 billion in 2021, up 25.5 percent from the year before, according to eMarketer.

Snap, parent company to social media app Snapchat, is based in Santa Monica, Calif., and has more than 300 million daily active users. It creates ad revenue from its social photo and video messaging service.

“We will continue to focus on delivering strong results for our advertising partners and innovating to expand the capabilities of our platform and better serve our community,” said Snap CEO Evan Spiegel in its third-quarter earnings statement.

The technical hurdle for Snap is that it seeks to continue to develop its workload’s higher-cost ranking models and expand into more complex models while reducing costs.

The company used NVIDIA GPUs and Merlin to boost its content ranking capabilities.

“Snap used NVIDIA GPUs and Merlin software to improve machine learning inference cost efficiency by 50 percent and decrease serving latency by 2x, providing the compute headroom to experiment and deploy heavier, more accurate ad and content ranking models,” said Nima Khajehnouri, VP of engineering at Snap.

Tencent Boosts Model Training With Merlin’s HugeCTR

Entertainment giant Tencent, which operates the enormously popular messaging service WeChat and payments platform WeChat Pay, is China’s largest company by market capitalization.

Its engineers need to rapidly iterate on models for its advertising recommendation system, putting increasing demands on its training performance.

“The advertising business is a relatively important business inside Tencent and the recommendation system is used to increase the overall advertising revenue,” said Xiangting Kong, expert engineer at Tencent.

The problem is that accuracy of advertising recommendation can only be improved by training more sample data, including more sample features, but this leads to longer training times that affect model update frequency.

“HugeCTR, as a recommendation training framework, is integrated into the advertising recommendation training system to make the update frequency of model training faster, and more samples can be trained to improve online effects,” he said.

After the training model performance is improved, more data can be trained to improve the accuracy of the model, increasing advertising revenue, he added.

Meituan Reduces Costs With NVIDIA A100 GPUs

Meituan’s business is at a crowded intersection of food, entertainment and on-demand services, among its 200 service categories. The Chinese internet giant has more than 667 million active users and 8.3 million active merchants.

Jun Huang, a senior technical expert at Meituan, said that if his team can greatly improve performance, it usually prefers to train more samples and more complex models.

The problem for Meituan was that as its models became more and more complex, it became difficult to optimize the training framework deeply, said Huang.

“We are working on integrating NVIDIA HugeCTR into our training system based on A100 GPUs. The cost is also greatly reduced. This is a preliminary optimization result, and there is still much room to optimize in the future,” he said.

Meituan recently reported its average number of transactions per transacting users increased to 32.8 for the trailing 12 months of the second quarter of 2021, compared with 25.7 for the trailing 12 months of the second quarter of 2020.

Learn more about NVIDIA Merlin. Learn more about NVIDIA Triton.

Editor’s note: This post is one in a series that features individual creators and developers who use NVIDIA Omniverse to boost their artistic processes.

Growing up in the Philippines, award-winning filmmaker Jae Solina says he turned to movies for a reminder that the world was much larger than himself and his homeland.

He started the popular YouTube channel JSFILMZ a decade ago as a way to share home videos he made for fun.

Since then, he’s expanded the channel to showcase his computer graphics-based movies, which have won the Best Animation and Best Super Short Film awards at the Las Vegas Independent Film Festival.

He also posts tutorials for virtual filmmaking with tools, including NVIDIA Omniverse — a physically accurate 3D design collaboration platform exclusively available with NVIDIA RTX GPUs and part of the NVIDIA Studio suite of creator tools.

Making tutorials is a way of paying it forward for Solina, as he is self-taught, gaining his computer graphics skills from other artists’ YouTube videos.

Solina now lives in Las Vegas with his wife and two kids, balancing filmmaking with part-time school and a full-time job.

“The only thing stopping you from creating something is your effort and imagination,” he said. “There are so many free tools like Blender or Omniverse that are readily available, enabling us to create what we want.”

Virtual Film Production

Solina creates computer graphics-based animation films, which can usually take large amounts of time and money, he said. NVIDIA Omniverse eases this process.

“With Omniverse, I don’t have to wait a full week to render a 30-second animation,” Solina said. “The rendering speed in Omniverse is superb and saves me a lot of time, which is important when balancing my filmmaking, non-creative work and family.”

Solina uses an NVIDIA GeForce RTX 3060 GPU, as well as Omniverse apps like Audio2Face, Create and Machinima to create his films virtually.

He also uses Omniverse Connectors for 3D applications like Blender and Autodesk Maya, as well as Reallusion’s iClone and Character Creator, with which he edits motion-capture data.

As a solo filmmaker, Solina said his main challenge is finding virtual assets — like characters and environments — that are photorealistic enough to use for movies.

“My process can definitely be a bit backwards, since the ideal method would be to write a script and then find the assets to make the story come alive,” he said. “But when I’m limited in my resources, I have to think of a storyline that fits a character or an environment I find.”

New support for the Omniverse ecosystem provided by 3D marketplaces and digital asset libraries helps solve this challenge — with thousands of Omniverse-ready assets for creators, all based on Universal Scene Description format.

Looking forward, Solina plans to create a short film entirely inside Omniverse.

Explore the NVIDIA Omniverse Instagram, gallery, forums and Medium channel. Check out Omniverse tutorials on Twitter and YouTube, and join our Discord server and Twitch channel to chat with the community.

At the National Retail Federation’s annual trade show, conversations tend to touch on recurring themes: “Will we be able to stock must-have products for next Christmas?,” “What incentives can I offer to loyal workers?” and “What happens to my margins if Susie Consumer purchases three of the same dresses online and returns two?”

The $26 trillion global retail industry is undergoing accelerated change, brought on by the pandemic and rapidly changing consumer habits. Now, it’s looking for accelerated problem solving using NVIDIA AI to address increasingly acute labor, logistics and supply chain challenges that are accompanying those changes.

Working with an ecosystem of more than 100 startups, equipment providers and software partners, NVIDIA offers an AI Enterprise platform for retailers and quick-service restaurants that helps speed the creation of intelligent stores, AI-driven forecasting, interactive chatbots, voice-enabled order taking and hyperpersonalized recommendations, and logistics and store optimization using digital twin technologies for simulation.

A Labor Crisis

Labor shortages have become a critical issue. In September and October, accommodation and food services businesses lost 1.6 million, or 6.2 percent, of their workforce, while 1.4 million people quit their retail jobs, according to the U.S. Bureau of Labor Statistics.

One way to address the problem is by creating autonomous shopping experiences. AiFi, AWM and Trigo’s autonomous shopping platforms, each shown at NRF, provide a seamless store checkout process. Customers can walk into a store, grab the items they want and pay with their mobile phone on their way out. Beyond addressing labor shortages, these autonomous stores provide live inventory management and prevent shrink.

Store associates are the face of retail organizations, so it makes sense to reduce the time they spend on tasks that aren’t customer facing, such as performing inventory counts or scanning for out-of-stock items. Spacee is using computer vision and AI to help retailers handle these basic, repetitive tasks.

NVIDIA partners Everseen and Graymatics provide asset protection applications at the point of sale to reduce shrinkage and provide customers a faster self-checkout experience. Deep North’s store analytics application is used for queue management, to optimize labor scheduling and store merchandising, resulting in increased sales.

All these startups are using the NVIDIA AI platform to deliver real-time recommendations in stores and distribution centers.

NVIDIA Tokkio conversational AI avatars and the NVIDIA Riva conversational AI framework, as well as recommendation engines based on the NVIDIA Merlin application framework, also help improve the customer experience and solve labor shortages by allowing for automated order taking and upsell based on customer shopping history.

Vistry.AI is delivering drive-thru automated order taking with a speech and recommendation engine, as well as computer vision applications for queue prediction, to predict when orders are ready, ensure food freshness and accelerate curbside pickup.

A Broken Supply Chain

The supply chain is the lifeblood of the retail industry; it was on life support for many in 2021 as attempts to recover from pandemic-related shutdowns around the world were stymied by trucker and dock worker shortages, inclement weather and persistent shortfalls in key food and electronics components.

According to a November report from Adobe Digital Insights, online shoppers in October were met with more than 2 billion out-of-stock messages — double the rate reported in October 2020.

With consumers more likely than not to go — and possibly stay with — a competitor, retailers are investing heavily in predictive analytics to gain real-time insights for forecasting and ordering from point of embarkation through to individual store shelves and distribution centers.

Dematic, a global materials handling company, and startups Kinetic Vision and Osaro are other key companies that use the NVIDIA AI platform to develop edge AI applications that add intelligence to automated warehouse systems. From computer vision AI applications to autonomous forklifts to pick-and-place robots, these AI applications improve distribution center throughput and reduce equipment downtime. And with NVIDIA Fleet Command, these solutions can be remotely deployed and managed securely and at scale in hundreds of distribution centers.

Improving Logistics

To help the $9 trillion logistics industry efficiently route goods from distribution centers to stores and from stores to homes, NVIDIA in November announced its NVIDIA ReOpt AI software.

NVIDIA ReOpt is an accelerated solver for machine learning that optimizes vehicle route planning and logistics in real time. Working with the NVIDIA ReOpt team, Domino’s Pizza implemented a real-time predictive system that helps it meet important delivery standards for customers eager for dinner.

Retail Goes AI 

The NVIDIA AI Enterprise platform is helping retailers weather challenges expected to continue well beyond 2022. With consumers increasingly demanding what the industry calls an omnichannel experience, one that lets them order online and pick up at curbside or have items delivered speedily to their homes, balancing supply with demand has increased the need for fast, actionable insights.

As consumers move from seeking goods and services to experiences, the depth and breadth of interaction between customers and retailers is requiring AI to complement human interaction. It’s a shift that has moved from wishlist to deployment.

Addressing a growing labor crisis among quick-service restaurants, startup Vistry is harnessing AI to automate the process of taking orders.

The company will share its story at the NRF Big Show, the annual industry gathering of the National Retail Federation in New York, starting Jan. 16.

“They’re closing restaurants because there is not enough labor,” said Atif Kureishy, CEO of Vistry, which is a member of the NVIDIA Inception startup accelerator program.

At the same time, customers are placing orders in more ways than ever: for pickup, in drive-throughs and via delivery services, as well as in dining rooms.

“There are new store formats, new configurations, new digital capabilities,” Kureishy said.

To help restaurants keep up, Kureishy, a veteran of both NASA and Booz Allen Hamilton, assembled a team that includes veterans of the semiconductor industry and Ivy League neuroscience programs.

While restaurant labor shortages are grabbing headlines, Vistry is tackling an opportunity driven by a labor shortage demographers have been predicting for decades.

As a result, the quick-service dining industry, which does $300 billion in sales each year in the United States alone, is just one of the industries that will need to find ways to get more done with fewer people over the long term.

To address this, Vistry is working to build an AI-enabled automated order-taking solution. It’s harnessing the latest natural language processing for menu understanding and speech and recommendation systems to deliver faster, more accurate order-taking and more relevant, personalized offers.

The system relies on NVIDIA Riva, a collection of technologies for building speech AI applications. It includes natural language understanding and speech recognition and synthesis capabilities. It also uses computer vision technology optimized with the NVIDIA Metropolis application framework.

Vistry’s platform, powered by the NVIDIA Jetson edge AI platform and NVIDIA A2 Tensor Core GPUs, goes beyond just an automated order-taking kiosk.

Vistry’s computer vision applications also help restaurants automate curbside check-ins. It can speed up drive-throughs and better predict how long it will take for customer orders to be ready. And it will track and trace orders for customers relying on food delivery services, Kureishy explains.

“Buyer behaviors are changing — the guest experience is not solely in the dining room anymore,” Kureishy said.

“Pandemic uncertainty continues to impact dining, along with consumer expectations of a seamless, operationally excellent experience on every platform and touchpoint,” said Susan Beardslee, principal analyst with ABI Research. “Behind the scenes, providers must enable integrated, near real-time digital solutions to address everything from supplies to staffing to delivery optimization.”

Vistry promises its solutions will be easy to deploy, fully integrated with existing restaurant systems, secure and private. They’ll also provide sophisticated real-time dashboards, so restaurant operators can better understand a growing number of sales channels — from drive-through lines to dining rooms to delivery services.

“All the expectations have changed, all of us want food faster, and we want to make sure the quality is preserved,” Kureishy said.

Vistry is helping quick-service restaurants reduce drive-through lines, predict when customers’ orders are ready, ensure food freshness, deliver curbside orders faster and optimize restaurant performance using AI and its analytics dashboard.

Who’s hungry?

Learn more about NVIDIA’s AI solutions for quick-service restaurants. 

Starting next week, Fortnite on GeForce NOW will launch in a limited-time closed beta for mobile, all streamed through the Safari web browser on iOS and the GeForce NOW Android app.

The beta is open for registration for all GeForce NOW members, and will help test our server capacity, graphics delivery and new touch controls performance. Members will be admitted to the beta in batches over the coming weeks.

‘Fortnite’ Streaming Gameplay Comes to Mobile Through iOS Safari and Android With Touch Inputs

Alongside the amazing team at Epic Games, we’ve been working to enable a touch-friendly version of Fortnite for mobile delivered through the cloud. While PC games in the GeForce NOW library are best experienced on mobile with a gamepad, the introduction of touch controls built by the GeForce NOW team offers more options for players, starting with Fortnite.

Beginning today, GeForce NOW members can sign up for a chance to join the Fortnite limited-time closed beta for mobile devices. Not an existing member? No worries. Register for a GeForce NOW membership and sign up to become eligible for the closed beta once the experience starts rolling out next week. Upgrade to a Priority or RTX 3080 membership to receive priority access to gaming servers. A paid GeForce NOW membership is not required to participate.

For tips on gameplay mechanics or a refresher on playing Fortnite with touch controls, check out Fortnite’s Getting Started page.

More Touch Games

And we’re just getting started. Cloud-to-mobile gaming is a great opportunity for publishers to get their games into more gamers’ hands with touch-friendly versions of their games. PC games or game engines, like Unreal Engine 4, which support Windows touch events can easily enable mobile touch support on GeForce NOW.

We’re working with additional publishers to add more touch-enabled games to GeForce NOW. And look forward to more publishers streaming full PC versions of their games to mobile devices with built-in touch support — reaching millions through the Android app and iOS Safari devices.

GFN Thursday Releases

GFN Thursday always means more games. Members can find these and more streaming on the cloud this week:

• The Anacrusis (New release on Steam and Epic Games Store, Jan. 13)
• Supraland Six Inches Under (New release on Steam, Jan. 14)
• Galactic Civilizations 3 (Free on Epic Games Store, Jan. 13 – 20)
• Ready or Not (Steam)

We make every effort to launch games on GeForce NOW as close to their release as possible, but, in some instances, games may not be available immediately.

What are you planning to play this weekend? Let us know on Twitter or in the comments below.

Cutting down the time needed to sequence and analyze a patient’s whole genome from days to hours isn’t just about clinical efficiency — it can save lives.

By accelerating every step of this process — from collecting a blood sample to sequencing the whole genome to identifying variants linked to diseases — a research team led by Stanford University took just hours to find a pathogenic variant and make a definitive diagnosis in a three-month-old infant with a rare seizure-causing genetic disorder. A traditional gene panel analysis ordered at the same time took two weeks to return results.

This ultra-rapid sequencing method, detailed today in the New England Journal of Medicine, helped clinicians manage the epilepsy case by providing insight about the infant’s seizure types and treatment response to anti-seizure medications.

The method set the first Guinness World Record for fastest DNA sequencing technique: five hours and 2 minutes. It was developed by researchers from Stanford University, NVIDIA, Oxford Nanopore Technologies, Google, Baylor College of Medicine and the University of California at Santa Cruz.

The researchers accelerated both base calling and variant calling using NVIDIA GPUs on Google Cloud. Variant calling, the process of identifying the millions of variants in a genome, was also sped up with NVIDIA Clara Parabricks, a computational genomics application framework.

Euan Ashley, MB ChB, DPhil, the paper’s corresponding author and a professor of medicine, of genetics and of biomedical data science at Stanford University School of Medicine, will be speaking at NVIDIA GTC, which runs online March 21-24.

Racing Against Time, Making Clinical Impact

Identifying genetic variants associated with a specific disease is a classic needle-in-the-haystack problem, often requiring researchers to comb through a person’s genome of 3 billion base pairs to find a single change that causes the disease.

It’s a lengthy process: A typical whole human genome sequencing diagnostic test takes six to eight weeks. Even rapid turnaround tests take two or three days. In many cases, this can be too slow to make a difference in treatment of a critically ill patient.

By optimizing the diagnosis pipeline to take only 7-10 hours, clinicians can more quickly identify genetic clues that inform patient care plans. In this pilot project, genomes were sequenced for a dozen patients, most of them children, at Stanford Health Care and Lucile Packard Children’s Hospital Stanford.

In five of the cases, the team found diagnostic variants that were reviewed by physicians and used to inform clinical decisions including heart transplant and drug prescription.

“Genomic information can provide rich insights and enable a clearer picture to be built,” said Gordon Sanghera, CEO of Oxford Nanopore Technologies. “A workflow which could deliver this information in near real time has the potential to provide meaningful benefits in a variety of settings in which rapid access to information is critical.”

AI Calls It: Identifying Variants with Clara Parabricks

The researchers found ways to optimize every step of the pipeline, including speeding up sample preparation and using nanopore sequencing on Oxford Nanopore’s PromethION Flow Cells to generate more than 100 gigabases of data per hour.

This sequencing data was sent to NVIDIA Tensor Core GPUs in a Google Cloud computing environment for base calling — the process of turning raw signals from the device into a string of A, T, G and C nucleotides —  and alignment in near real time. Distributing the data across cloud GPU instances helped minimize latency.

Next, the scientists had to find tiny variations within the DNA sequence that could cause a genetic disorder. Known as variant calling, this stage was sped up with Clara Parabricks using a GPU-accelerated version of PEPPER-Margin-DeepVariant, a pipeline developed in a collaboration between Google and UC Santa Cruz’s Computational Genomics Laboratory.

DeepVariant uses convolutional neural networks for highly accurate variant calling. The GPU-accelerated DeepVariant Germline Pipeline software in Clara Parabricks provides results at 10x the speed of native DeepVariant instances, decreasing the time to identify disease-causing variants.

“Together with our collaborators and some of the world’s leaders in genomics, we were able to develop a rapid sequencing analysis workflow that has already shown tangible clinical benefits,” said NVIDIA’s Mehrzad Samadi, who co-led the creation of Parabricks and co-authored the New England Journal of Medicine article. “These are the kinds of high-impact problems we live to solve.”

Read the full publication in the New England Journal of Medicine and get started with a 90-day trial of NVIDIA Clara Parabricks, which can help analyze a whole human genome in under 30 minutes.

Subscribe to NVIDIA healthcare news here. 

SHIELD Software Experience Upgrade 9.0 is rolling out to all NVIDIA SHIELD TVs, delivering the Android 11 operating system and more.

An updated Gboard — the Google Keyboard — allows people to use their voices and the Google Assistant to discover content in all search boxes.

Additional permissions let users customize privacy across apps, including a new “only this time” option to grant temporary, one-time permissions.

SHIELD also adds support for aptX-compatible Bluetooth headsets. This, in addition to existing support for LDAC headsets, gives customers more options for higher quality listening options.

Browse the release notes for a complete list of upgrades and look for the upgrade notification, which will hit SHIELD TV homescreens starting today.

Stream On

Exciting new app releases and updates bring new and improved content to SHIELD TV-powered home theaters.

Google Play Movies & TV adds stunning Dolby Vision HDR for unparalleled cinematic experiences on SHIELD TV.

SHIELD owners can connect digital movie catalogs from Amazon, Apple TV and VUDU using Movies Anywhere and watch from a single place on Google Play Movies & TV.

Stream limitless entertainment from popular apps like IMDb TV and Apple TV on SHIELD TV, at up to 4K HDR.

IMDb TV delivers thousands of movies, binge-worthy TV shows and IMDb Originals like Leverage: Redemption and Alex Rider. Best of all, they’re always free.

With Apple TV, stream a robust library of Apple Originals in 4K Dolby Vision and Dolby Atmos. Buy or rent over 100,000 movies and shows — including Ted Lasso and The Morning Show — from the largest selection of 4K HDR titles.

Browse the Google Play Store app for the latest updates from Disney+, Paramount+, YouTube TV and Peloton for thousands of live and on-demand workouts.

The Next Generation of GeForce NOW

Gamers around the globe are upgrading their SHIELD TVs into a powerful GeForce RTX 3080-class gaming rig, unlocking extraordinary 4K HDR graphics exclusively on SHIELD, as well as immersive 7.1 surround sound, with the new GeForce NOW RTX 3080 membership.

GeForce NOW Founders members are eligible for an exclusive discount on the RTX 3080 membership. They can retain their Founders benefits — including “Founders for Life” pricing — if they decide to revert back to their original membership. Find more information and sign up at geforcenow.com.

The SHIELD update provides all GeForce NOW members with new benefits. Twitch has been updated to enable simultaneous gaming and streaming in high quality. Support for additional Bluetooth keyboards and mice has been added as well.

SHIELD TV pairs with Xbox One and Series X, Sony PlayStation DualSense and DualShock, and Scuf controllers, allowing for a bring-your-own-controller cloud gaming experience on GeForce NOW.

Bonus Streaming

Google is offering new, U.S.-based SHIELD TV owners six months of Peacock Premium at no additional cost. Unlock everything Premium has to offer. Watch movies and shows like The Office and Parks and Recreation, exclusive originals such as Yellowstone and AP Bio, and live sports including WWE and English Premier League soccer.

To redeem this offer, new SHIELD owners must set up a new Google account or log into a preexisting one, subscribe through the Peacock Premium banner on the For You or Apps tab, and provide a valid form of payment.

It’s an exciting time for SHIELD owners. What are you looking forward to most? Let us know in the comments or on Twitter.

NVIDIA is America’s best place to work, according to Glassdoor’s just-issued list of best employers for 2022.

Amid a global pandemic that has affected every workplace, NVIDIA was ranked No. 1 on Glassdoor’s 14th annual Best Places to Work list for large US companies. The award is based on anonymous employee feedback covering thousands of companies.

Other companies ranked highly by current and former employees include tech companies Google and Salesforce, and fitness wear retailer Lululemon. NVIDIA ranked second on the list last year.

Half of the top ten large US company winners are tech firms and 40 percent of the entire list of top 100 employers are tech companies.

“The world of work is rapidly evolving, fueled by the pandemic and now millions of workers re-evaluating their expectations from employers,” Glassdoor CEO Christian Sutherland-Wong said. “This year’s Best Places to Work winners are leading the way by listening and responding to employee feedback and reimagining the employee experience to truly put their people first.”

NVIDIA employees consistently give the company high marks on Glassdoor’s survey.

95 percent would recommend NVIDIA to a friend. The same proportion say the company has a positive business outlook. And 98 percent approve of the CEO, Jensen Huang.

“NVIDIA’s a place where folks who love working hard and enjoy tough challenges will feel at home,” one anonymous employee wrote on the site, which gets 67 million views each month.

“Leaders walk the talk with culture and values,” another wrote. “Everyone is genuinely helpful to train and ramp new employees, decision making is fast, title and hierarchy is never in the way of doing what is right.”

“The best workplace I have witnessed to date,” another noted.

The reviews from current and former employees capture an authentic look at companies.

When sharing a company review on Glassdoor, employees are encouraged to rate their satisfaction with the company overall and key workplace factors such as career opportunities, compensation and benefits, culture and values, diversity and inclusion, senior management, and work-life balance.

In addition, employees are asked to describe the best reasons to work at their companies and any downsides.

Glassdoor’s Best Places to Work were determined using company reviews from U.S.-based employees from Oct. 20, 2020, to Oct. 18, 2021.

Want to leave a review of your own? First, you’ll need to join our team. Check out our careers hub at https://www.nvidia.com/en-us/about-nvidia/careers/.

For the complete list of the Glassdoor Best Places to Work winners, visit https://www.glassdoor.com/Award/Best-Places-to-Work-LST_KQ0,19.htm

Bright Computing, a leader in software for managing high performance computing systems used by more than 700 organizations worldwide, is now part of NVIDIA.

Companies in healthcare, financial services, manufacturing and other markets use its tool to set up and run HPC clusters, groups of servers linked by high-speed networks into a single unit. Its product, Bright Cluster Manager, becomes the latest addition to NVIDIA’s software stack for accelerated computing.

Bright Computing, founded in 2009 and headquartered in Amsterdam, has customers that include household names such as Boeing, NASA, Tesla, Johns Hopkins University and Siemens.

We’ve been working with Bright for more than a decade as they integrated their software with our GPUs, networking, CUDA and most recently DGX systems.

Now we see an opportunity to combine our system software capabilities to make HPC data centers easier to buy, build and operate, creating a much larger future for HPC.

NVIDIA’s partners will take Bright’s software to more markets. And Bright’s software and expertise will enhance our growing NVIDIA DGX and data center businesses.

Bright’s flexible software can run at the edge, in the data center and across multiple public or hybrid clouds. It automates administration for clusters whether they’re made up of a handful or hundreds of thousands of servers. And it supports Arm and x86 CPUs, NVIDIA GPUs and Kubernetes containers.

We welcome Bright’s employees into NVIDIA. Together, we’ll continue to support Bright’s customers and invest in its product roadmap to grow the business.

“NVIDIA is changing the world as we know it, and we couldn’t be more excited for our team and software to play a part in that,” said Bill Wagner, CEO of Bright Computing.

Ready for the Industrial HPC Era

The combination of HPC, accelerated computing and AI has spawned what NVIDIA CEO Jensen Huang calls “an industrial HPC era.”

Clusters are at the heart of HPC’s scale-out style of computing, born in supercomputing centers and increasingly going mainstream to support AI.

Companies and developers in every field are adopting HPC systems to build physically accurate 3D simulations and digital twins for work as diverse as drug discovery, product design and factory automation — many of them using NVIDIA Omniverse.

Bolstered by Bright Computing’s team and software, NVIDIA will continue to democratize access to HPC and accelerated computing.

To make the best portfolio decisions, banks need to accurately calculate values of their trades, while factoring in uncertain external risks. This requires high-performance computing power to run complex derivatives models — which find fair prices for financial contracts — as close to real time as possible.

“You don’t want to trade today on yesterday’s data. You want to have up-to-the-moment portfolio values under many possible scenarios,” said Ryan Ferguson, CEO of Riskfuel, a Toronto-based startup that has built an AI-based accelerator technology for valuation and risk workloads.

Ferguson used to run securitization and credit derivatives for Scotiabank Global Banking and Markets. He noticed this industry-wide challenge and shifted his career to help address it, founding Riskfuel in 2019.

The company trains and develops its AI models using NVIDIA DGX systems, NVIDIA GPUs and the NVIDIA CUDA parallel computing platform.

Riskfuel is also a member of NVIDIA Inception, which is a free program for cutting-edge startups. The program provides access to training credits from the NVIDIA Deep Learning Institute, technology assistance, awareness support and opportunities to connect with investors. As a community member,

Speeding Up Sluggish Models

Riskfuel’s first customer happened to be Ferguson’s old employer, Scotiabank. When that work garnered the bank an industry award, the market noticed.

The company then partnered with Bank of Montreal (BMO), which was looking to improve the performance of its CPU-based structured notes models.

BMO employed industry-standard Monte Carlo simulations to process pricing requests, each of which could take several minutes to run on a single CPU core. But that’s too sluggish for the massive quantity of simulations required and the number of deals being run through many risk scenarios every day.

A pilot project showed that versions of BMO models accelerated by Riskfuel and deployed on NVIDIA DGX systems dramatically improved performance. Ultimately, it lets the bank expand its client base, drive higher trade flows, generate new risk insights and lead to better product design and selection.

According to Lucas Caliri, managing director and head of Cross Asset Solutions at BMO, “The partnership with Riskfuel and NVIDIA is enabling us to assist our clients to handle more complex hedging strategies and — with accelerated pricing and analysis — make faster, smarter investment decisions.”

Building a GPU-Powered ‘Rocket’

Riskfuel built its model by training it on 650 million data points, using NVIDIA DGX A100, which Ferguson calls an “AI workhorse.”

The startup works with banks’ code by using their CPU models to create training datasets for neural nets, which run on GPUs using PyTorch, TensorFlow or any other AI library. Then, it delivers its product as packaged neural nets, allowing customers to choose whether to run inference on NVIDIA A100, A30 or other NVIDIA Tensor Core GPUs.

Once the Riskfuel model is in, banks notice a huge speedup, while maintaining the same API for model access.

“We take their car into the garage, rip the engine out and put a rocket in,” said Ferguson. “It looks like the same car, but on the inside, it produces the results way faster.”

Riskfuel’s model sacrifices nothing in the way of accuracy — even with all that speed — no matter how far a bank might push it. Ferguson said that banks no longer have to trade speed for accuracy, or vice versa.

“Historically, there’s basically been a toggle that says faster or more accurate,” Ferguson said. “With Riskfuel, you can get fast and accurate.”

Just the Beginning

Looking forward, Riskfuel hopes to provide solutions for more areas in which banks can’t process scenarios fast enough. For example, previously, banks had to choose which risk scenarios to run, but that limitation is being removed.

“Now that their derivatives portfolio models can run in seconds, banks need real-time data and faster risk scenario generation,” said Ivan Sergienko, chief product officer at Riskfuel. “These are potential growth areas for us.”

Learn more about NVIDIA offerings for the financial services industry.