The A100 Tensor Core GPU set new MLPerf benchmark records last month—outperforming CPUs by up to 237x in data centre inference. In November, Amazon Web Services made eight A100 GPUs available in each of its P4d instances.

For those who prefer their hardware local, the DGX Station A100 is available in either four 80GB A100 GPUs or four 40GB configurations. The monstrous 80GB version of the A100 has twice the memory of when the GPU was originally unveiled just six months ago.

“We doubled everything in this system to make it more effective for customers,” said Paresh Kharya, senior director of product management for accelerated computing at NVIDIA.

NVIDIA says the two configurations provide options for data science and AI research teams to select a system according to their unique workloads and budgets.

Charlie Boyle, VP and GM of DGX systems at NVIDIA, commented:

“DGX Station A100 brings AI out of the data centre with a server-class system that can plug in anywhere.

Teams of data science and AI researchers can accelerate their work using the same software stack as NVIDIA DGX A100 systems, enabling them to easily scale from development to deployment.”

The memory capacity of the DGX Station A100 powered by the 80GB GPUs is now 640GB, enabling much larger datasets and models.

“To power complex conversational AI models like BERT Large inference, DGX Station A100 is more than 4x faster than the previous generation DGX Station. It delivers nearly a 3x performance boost for BERT Large AI training,” NVIDIA wrote in a release.

DGX A100 640GB configurations can be integrated into the DGX SuperPOD Solution for Enterprise for unparalleled performance. Such “turnkey AI supercomputers” are available in units consisting of 20 DGX A100 systems.

Since acquiring ARM, NVIDIA continues to double-down on its investment in the UK and its local talent.

“We will create an open centre of excellence in the area once home to giants like Isaac Newton and Alan Turing, for whom key NVIDIA technologies are named,” Huang said in September. “We want to propel ARM – and the UK – to global AI leadership.”

NVIDIA’s latest supercomputer, the Cambridge-1, is being installed in the UK and will be one of the first SuperPODs with DGX A100 640GB systems. Cambridge-1 will initially be used by local pioneering companies to supercharge healthcare research.

Dr Kim Branson, SVP and Global Head of AI and ML at GSK, commented:

“Because of the massive size of the datasets we use for drug discovery, we need to push the boundaries of hardware and develop new machine learning software.

We’re building new algorithms and approaches in addition to bringing together the best minds at the intersection of medicine, genetics, and artificial intelligence in the UK’s rich ecosystem.

This new partnership with NVIDIA will also contribute additional computational power and state-of-the-art AI technology.”

The use of AI for healthcare research has received extra attention due to the coronavirus pandemic. A recent simulation of the coronavirus, the largest molecular simulation ever, simulated 305 million atoms and was powered by 27,000 NVIDIA GPUs.

Several promising COVID-19 vaccines in late-stage trials have emerged in recent days which have raised hopes that life could be mostly back to normal by summer, but we never know when the next pandemic may strike and there are still many challenges we all face both in and out of healthcare.

Systems like the DGX Station A100 help to ensure that – whatever challenges we face now and in the future – researchers have the power they need for their vital work.

Both configurations of the DGX Station A100 are expected to begin shipping this quarter.

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Amazon Web Services (AWS) first launched a GPU instance 10 years ago with the NVIDIA M2050. It’s rather poetic that, a decade on, NVIDIA is now providing AWS with the hardware to power the next generation of groundbreaking innovations.

The A100 outperformed CPUs in this year’s MLPerf by up to 237x in data centre inference. A single NVIDIA DGX A100 system – with eight A100 GPUs – provides the same performance as nearly 1,000 dual-socket CPU servers on some AI applications.

“We’re at a tipping point as every industry seeks better ways to apply AI to offer new services and grow their business,” said Ian Buck, Vice President of Accelerated Computing at NVIDIA, following the benchmark results.

Businesses can access the A100 in AWS’ P4d instance. NVIDIA claims the instances reduce the time to train machine learning models by up to 3x with FP16 and up to 6x with TF32 compared to the default FP32 precision.

Each P4d instance features eight NVIDIA A100 GPUs. If even more performance is required, customers are able to access over 4,000 GPUs at a time using AWS’s Elastic Fabric Adaptor (EFA).

Dave Brown, Vice President of EC2 at AWS, said:

“The pace at which our customers have used AWS services to build, train, and deploy machine learning applications has been extraordinary. At the same time, we have heard from those customers that they want an even lower-cost way to train their massive machine learning models.

Now, with EC2 UltraClusters of P4d instances powered by NVIDIA’s latest A100 GPUs and petabit-scale networking, we’re making supercomputing-class performance available to virtually everyone, while reducing the time to train machine learning models by 3x, and lowering the cost to train by up to 60% compared to previous generation instances.”

P4d supports 400Gbps networking and makes use of NVIDIA’s technologies including NVLink, NVSwitch, NCCL, and GPUDirect RDMA to further accelerate deep learning training workloads.

Some of AWS’ customers across various industries have already begun exploring how the P4d instance can help their business.

Karley Yoder, VP & GM of Artificial Intelligence at GE Healthcare, commented:

“Our medical imaging devices generate massive amounts of data that need to be processed by our data scientists. With previous GPU clusters, it would take days to train complex AI models, such as Progressive GANs, for simulations and view the results.

Using the new P4d instances reduced processing time from days to hours. We saw two- to three-times greater speed on training models with various image sizes while achieving better performance with increased batch size and higher productivity with a faster model development cycle.”

For an example from a different industry, the research arm of Toyota is exploring how P4d can improve their existing work in developing self-driving vehicles and groundbreaking new robotics.

“The previous generation P3 instances helped us reduce our time to train machine learning models from days to hours,” explained Mike Garrison, Technical Lead of Infrastructure Engineering at Toyota Research Institute.

“We are looking forward to utilizing P4d instances, as the additional GPU memory and more efficient float formats will allow our machine learning team to train with more complex models at an even faster speed.”

P4d instances are currently available in the US East (N. Virginia) and US West (Oregon) regions. AWS says further availability is planned soon.

You can find out more about P4d instances and how to get started here.

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MLPerf consists of five inference benchmarks which cover the main three AI applications today: image classification, object detection, and translation.

“Industry-standard MLPerf benchmarks provide relevant performance data on widely used AI networks and help make informed AI platform buying decisions,” said Rangan Majumder, VP of Search and AI at Microsoft.

Last year, NVIDIA led all five benchmarks for both server and offline data centre scenarios with its Turing GPUs. A dozen companies participated.

23 companies participated in this year’s MLPerf but NVIDIA maintained its lead with the A100 outperforming CPUs by up to 237x in data centre inference.

For perspective, NVIDIA notes that a single NVIDIA DGX A100 system – with eight A100 GPUs – provides the same performance as nearly 1,000 dual-socket CPU servers on some AI applications.

“We’re at a tipping point as every industry seeks better ways to apply AI to offer new services and grow their business,” said Ian Buck, Vice President of Accelerated Computing at NVIDIA.

“The work we’ve done to achieve these results on MLPerf gives companies a new level of AI performance to improve our everyday lives.”

The widespread availability of NVIDIA’s AI platform through every major cloud and data centre infrastructure provider is unlocking huge potential for companies across various industries to improve their operations.

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The specific GPU added to Google Cloud is the NVIDIA A100 Tensor Core which was announced just last month. NVIDIA says the A100 “has come to the cloud faster than any NVIDIA GPU in history.”

NVIDIA claims the A100 boosts training and inference performance by up to 20x over its predecessors. Large AI models like BERT can be trained in just 37 minutes on a cluster of 1,024 A100s.

For those who enjoy their measurements in teraflops (TFLOPS), the A100 delivers around 19.5 TFLOPS in single-precision performance and 156 TFLOPS for Tensor Float 32 workloads.

Manish Sainani, Director of Product Management at Google Cloud, said:

“Google Cloud customers often look to us to provide the latest hardware and software services to help them drive innovation on AI and scientific computing workloads.

With our new A2 VM family, we are proud to be the first major cloud provider to market NVIDIA A100 GPUs, just as we were with NVIDIA T4 GPUs. We are excited to see what our customers will do with these new capabilities.”

The announcement couldn’t have arrived at a better time – with many looking to harness AI for solutions to the COVID-19 pandemic, in addition to other global challenges such as climate change.

Aside from AI training and inference, other things customers will be able to achieve with the new capabilities include data analytics, scientific computing, genomics, edge video analytics, and 5G services.

The new Ampere-based data center GPUs are now available in Alpha on Google Cloud. Users can access instances of up to 16 A100 GPUs, which provides a total of 640GB of GPU memory and 1.3TB of system memory.

You can register your interest for access here.

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In the keynote session, titled ‘What is the true adoption of AI’, Hogan provided real-world examples of how the technology is being used and enabled by Nvidia’s GPUs. But first, he highlighted the momentum we’re seeing in AI.

“Many governments have announced investments in AI and how they’re going to position themselves,” comments Hogan. “Countries around the world are starting to invest in very large infrastructures.”

The world’s most powerful supercomputers are powered by Nvidia GPUs. ORNL Summit, the current fastest, uses an incredible 27,648 GPUs to deliver over 144 petaflops of performance. Vast amounts of computational power is needed for AI which puts Nvidia in a great position to capitalise.

“The compute demands of AI are huge and beyond what anybody has seen within a standard enterprise environment before,” says Hogan. “You cannot train a neural network on a standard CPU cluster.”

Nvidia started off by creating graphics cards for gaming. While that’s still a big part of what the company does, Hogan says the company pivoted towards AI back in 2012.

A great deal of the presentation was spent on autonomous vehicles, which is unsurprising given the demand and Nvidia’s expertise in the field. Hogan highlights that you simply cannot train driverless cars using CPUs and provided a comparison in cost, size, and power consumption.

“A new type of computing is starting to evolve based around GPU architecture called ‘dense computing’ – the ability to build systems that are highly-powerful, huge amounts of computational scale, but actually contained within a very small configuration,” explains Hogan.

Autonomous car manufacturers need to train petabytes of data per day, reiterate their models, and deploy them again in order to get those vehicles to market.

Nvidia has a machine called the DGX-2 which delivers two petaflops of performance. “That is one server that’s equivalent to 800 traditional servers in one box.”

Nvidia has a total of 370 autonomous vehicles which Hogan says covers most of the world’s automotive brands. Many of these are investing heavily and rushing to deliver at least ‘Level 2’ driverless cars in the 2020-21 timeframe.

“We have a fleet of autonomous cars,” says Hogan. “It’s not our intention to compete with Uber, Daimler or BMW, but the best way of us helping our customers enable that is by trying it ourselves.”

“All the work our customers do we’ve also done ourselves so we understand the challenges and what it takes to do this.”

Real-world impact

Hogan notes how AI is a “horizontal capability that sits across organisations” and is “an enabler for many, many things”. It’s certainly a challenge to come up with examples of industries that cannot be improved to some degree through AI.

Following autonomous cars, Nvidia sees the next mass scaling of AI happening in healthcare (which our dear readers already know, of course.)

Hogan provides the natural example of the UK’s National Health Service (NHS) which has vast amounts of patient data. Bringing this data together and having an AI make sense of it can unlock valuable information to improve healthcare.

AIs which can make sense of medical imaging on a par with, or even better, than some doctors are starting to become available. However, they are still 2D images that are alien to most people.

Hogan showed how AI is able to turn 2D imagery into 3D models of the organs which are easier to understand. In the GIF below, we see a radiograph of a heart being turned into a 3D model:

We’ve also heard about how AI is helping with the field of genomics, assisting in finding cures for human diseases. Nvidia GPUs are used for Oxford Nanopore’s MinIT handheld which enables DNA sequencing of things such as plants to be conducted in-the-field.

In a blog post last year, Nvidia explained how MinIT uses AI for basecalling:

“Nanopore sequencing measures tiny ionic currents that pass through nanoscale holes called nanopores. It detects signal changes when DNA passes through these holes. This captured signal produces raw data that requires signal processing to determine the order of DNA bases – known as the ‘sequence.’ This is called basecalling.

This analysis problem is a perfect match for AI, specifically recurrent neural networks. Compared with previous methods, RNNs allow for more accuracy in time-series data, which Oxford Nanopore’s sequencers are known for.”

Hogan notes how, in many respects, eCommerce paved the way for AI. Data collected for things such as advertising helps to train neural networks. In addition, eCommerce firms have consistently aimed to improve and optimise their algorithms for things such as recommendations to attract customers.

“All that data, all that Facebook information that we’ve created, has enabled us to train networks,” notes Hogan.

Brick-and-mortar retailers are also being improved by AI. Hogan gives the example of Walmart which is using AI to improve their demand forecasting and keep supply chains running smoothly.

In real-time, Walmart is able to see where potential supply challenges are and take action to avoid or minimise. The company is even able to see where weather conditions may cause issues.

Hogan says this has saved Walmart tens of billions of dollars. “This is just one example of how AI is making an impact today not just on the bottom line but also the overall performance of the business”.

Accenture is now detecting around 200 million cyber threats per day, claims Hogan. He notes how protecting against such a vast number of evolving threats is simply not possible without AI.

“It’s impossible to address that, look at it, prioritise it, and action it in any other way than applying AI,” comments Hogan. “AI is based around patterns – things that are different – and when to act and when not to.”

While often we hear about what AI could one day be used for, Hogan’s presentation was a fascinating insight into how Nvidia is seeing it making an impact today or in the not-so-distant future.

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