This cutting-edge AI supercomputer stands as the fastest of its kind in the UK today. It marks a groundbreaking fusion of AI and high-performance computing (HPC) technologies, showcasing the potential to tackle some of the world’s most pressing challenges.

Dawn Phase 1 is the cornerstone of the recently launched UK AI Research Resource (AIRR), demonstrating the nation’s commitment to exploring innovative systems and architectures.

This supercomputer brings the UK closer to achieving the exascale; a computing threshold of a quintillion (10^18) floating point operations per second. To put this into perspective, the processing power of an exascale system equals what every person on Earth would calculate in over four years if they were working non-stop, 24 hours a day.

Operational at the Cambridge Open Zettascale Lab, Dawn utilises Dell PowerEdge XE9640 servers, providing an unparalleled platform for the Intel Data Center GPU Max Series accelerator. This collaboration ensures a diverse ecosystem through oneAPI, fostering an environment of choice.

The system’s capabilities extend across various domains, including healthcare, engineering, green fusion energy, climate modelling, cosmology, and high-energy physics.

Adam Roe, EMEA HPC technical director at Intel, said:

“Dawn considerably strengthens the scientific and AI compute capability available in the UK and it’s on the ground and operational today at the Cambridge Open Zettascale Lab.

Dell PowerEdge XE9640 servers offer a no-compromises platform to host the Intel Data Center GPU Max Series accelerator, which opens up the ecosystem to choice through oneAPI.

I’m very excited to see the sorts of early science this machine can deliver and continue to strengthen the Open Zettascale Lab partnership between Dell Technologies, Intel, and the University of Cambridge, and further broaden that to the UK scientific and AI community.”

Glimpse into the future

Dawn Phase 1 is not just a standalone achievement; it’s part of a broader strategy.

The collaborative endeavour aims to deliver a Phase 2 supercomputer in 2024, promising tenfold performance levels. This progression would propel the UK’s AI capability, strengthening the successful industry partnership.

The supercomputer’s technical foundation lies in Dell PowerEdge XE9640 servers, renowned for their versatile configurations and efficient liquid cooling technology. This innovation ensures optimal handling of AI and HPC workloads, offering a more effective solution than traditional air-cooled systems.

Tariq Hussain, Head of UK Public Sector at Dell, commented:

“Collaborations like the one between the University of Cambridge, Dell Technologies and Intel, alongside strong inward investment, are vital if we want the compute to unlock the high-growth AI potential of the UK. It is paramount that the government invests in the right technologies and infrastructure to ensure the UK leads in AI and exascale-class simulation capability.

It’s also important to embrace the full spectrum of the technology ecosystem, including GPU diversity, to ensure customers can tackle the growing demands of generative AI, industrial simulation modelling and ground-breaking scientific research.”

As the world awaits the full technical details and performance numbers of Dawn Phase 1 – slated for release in mid-November during the Supercomputing 23 (SC23) conference in Denver, Colorado – the UK stands at the precipice of a transformative era in scientific and AI research.

This collaboration between industry giants and academia not only accelerates research discovery but also propels the UK’s knowledge economy to new heights.

(Image Credit: Joe Bishop for Cambridge Open Zettascale Lab)

See also: UK paper highlights AI risks ahead of global Safety Summit

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Dell, Intel and University of Cambridge deploy the UK’s fastest AI supercomputer appeared first on AI News.

Dojo was unveiled at Tesla’s annual AI Day last year but the project was still in its infancy. At AI Day 2022, Tesla unveiled the progress it has made with Dojo over the course of the year.

The supercomputer has transitioned from just a chip and training tiles into a full cabinet. Tesla claims that it can replace six GPU boxes with a single Dojo tile, which it says is cheaper than one GPU box.

Per tray, there are six Dojo tiles. Tesla claims that each tray is equivalent to “three to four full-loaded supercomputer racks”. Two trays can fit in a single Dojo cabinet with a host assembly.

Such a supercomputer naturally has a large power draw. Dojo requires so much power that it managed to trip the grid in Palo Alto.

“Earlier this year, we started load testing our power and cooling infrastructure. We were able to push it over 2 MW before we tripped our substation and got a call from the city,” said Bill Chang, Tesla’s Principal System Engineer for Dojo.

In order to function, Tesla had to build custom infrastructure for Dojo with its own high-powered cooling and power system.

An ‘ExaPOD’ (consisting of a few Dojo cabinets) has the following specs:

• 1.1 EFLOP
• 1.3TB SRAM
• 13TB DRAM

Seven ExaPODs are currently planned to be housed in Palo Alto.

Dojo is purpose-built for AI and will greatly improve Tesla’s ability to train neural nets using video data from its vehicles. These neural nets will be critical for Tesla’s self-driving efforts and its humanoid robot ‘Optimus’, which also made an appearance during this year’s event.

Optimus

Optimus was also first unveiled last year and was even more in its infancy than Dojo. In fact, all it was at the time was a person in a spandex suit and some PowerPoint slides.

While it’s clear that Optimus still has a long way to go before it can do the shopping and carry out dangerous manual labour tasks, as Tesla envisions, we at least saw a working prototype of the robot at AI Day 2022.

“I do want to set some expectations with respect to our Optimus robot,” said Tesla CEO Elon Musk. “As you know, last year it was just a person in a robot suit. But, we’ve come a long way, and compared to that it’s going to be very impressive.”

Optimus can now walk around and, if attached to apparatus from the ceiling, do some basic tasks like watering plants:

The prototype of Optimus was reportedly developed in the past six months and Tesla is hoping to get a working design within the “next few months… or years”. The price tag is “probably less than $20,000”.

All the details of Optimus are still vague at the moment, but at least there’s more certainty around the Dojo supercomputer.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Tesla’s AI supercomputer tripped the power grid appeared first on AI News.

The supercomputer is called the AI Research SuperCluster (RSC) and is yet to be fully complete. However, Meta’s researchers have already begun using it for training large natural language processing (NLP) and computer vision models.

RSC is set to be fully built in mid-2022. Meta says that it will be the fastest in the world once complete and the aim is for it to be capable of training models with trillions of parameters.

“We hope RSC will help us build entirely new AI systems that can, for example, power real-time voice translations to large groups of people, each speaking a different language, so they can seamlessly collaborate on a research project or play an AR game together,” wrote Meta in a blog post.

“Ultimately, the work done with RSC will pave the way toward building technologies for the next major computing platform — the metaverse, where AI-driven applications and products will play an important role.”

For production, Meta expects RSC will be 20x faster than Meta’s current V100-based clusters. RSC is also estimated to be 9x faster at running the NVIDIA Collective Communication Library (NCCL) and 3x faster at training large-scale NLP workflows.

A model with tens of billions of parameters can finish training in three weeks compared with nine weeks prior to RSC.

Meta says that its previous AI research infrastructure only leveraged open source and other publicly-available datasets. RSC was designed with the security and privacy controls in mind to allow Meta to use real-world examples from its production systems in production training.

What this means in practice is that Meta can use RSC to advance research for vital tasks such as identifying harmful content on its platforms—using real data from them.

“We believe this is the first time performance, reliability, security, and privacy have been tackled at such a scale,” says Meta.

(Image Credit: Meta)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Meta claims its new AI supercomputer will set records appeared first on AI News.

The UK’s most powerful supercomputer and among the world’s top fifty, Cambridge-1 was announced by the technology company in October last year and cost $100 million (£72m) to build.

Its first projects with AstraZeneca, GSK, Guy’s and St Thomas’ NHS Foundation Trust, King’s College London, and Oxford Nanopore Technologies include developing a deeper understanding of brain diseases like dementia, using AI to design new drugs, and improving the accuracy of finding disease-causing variations in human genomes.

“Cambridge-1 will empower world-leading researchers in business and academia with the ability to perform their life’s work on the U.K.’s most powerful supercomputer, unlocking clues to disease and treatments at a scale and speed previously impossible in the U.K.,” said Jensen Huang, founder and CEO of NVIDIA.

“The discoveries developed on Cambridge-1 will take shape in the U.K., but the impact will be global, driving groundbreaking research that has the potential to benefit millions around the world,” he added.

AI for healthcare is growing rapidly in the UK, with a range of start-ups and larger pharmaceutical companies turning to mining the vast quantities of data available to discover potential drugs, further understand certain diseases, and improve and personalise patient care.

“It’s great to see it in the UK ecosystem,” said Roel Bulthuis, head of the healthcare team at Inkef Capital. “Many European healthcare systems are not as advanced in their thinking about using data and integrating that into the healthcare system.”

According to Frontier Economics, an economics consulting firm, Cambridge-1 has the potential to create an estimated value of £600 million over the next 10 years.

Find out more about Digital Transformation Week North America, taking place on November 9-10 2021, a virtual event and conference exploring advanced DTX strategies for a ‘digital everything’ world.

The post NVIDIA launches UK supercomputer to search for healthcare solutions appeared first on AI News.

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.

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

The post NVIDIA DGX Station A100 is an ‘AI data-centre-in-a-box’ appeared first on AI News.

AI is helping with COVID-19 research around the world and much of it is being powered by NVIDIA GPUs. It’s a daunting task, new drugs often cost over $2.5 billion in research and development — doubling every nine years — and 90 percent of efforts fail.

Nvidia wants to help speed up discoveries of vital medicines while reducing costs

“COVID-19 hits home this urgency [for new tools],” Huang says.

Huang announced NVIDIA Clara Discovery—a suite of tools for assisting scientists in discovering lifesaving new drugs.

NVIDIA Clara combines imaging, radiology, and genomics to help develop healthcare AI applications. Pre-trained AI models and application-specific frameworks help researchers to find targets, build compounds, and develop responses.

Dr Hal Barron, Chief Scientific Officer and President of R&D at GSK, commented:

“AI and machine learning are like a new microscope that will help scientists to see things that they couldn’t see otherwise.

NVIDIA’s investment in computing, combined with the power of deep learning, will enable solutions to some of the life sciences industry’s greatest challenges and help us continue to deliver transformational medicines and vaccines to patients.

Together with GSK’s new AI lab in London, I am delighted that these advanced technologies will now be available to help the UK’s outstanding scientists.”

Researchers can now use biomedical-specific language models for their work, thanks to a breakthrough in natural language processing. This means researchers can organise and activate large datasets, research literature, and sort through papers or patents on existing treatments and other vital real-world data.

“Where there are popular industry tools, our computer scientists accelerate them,” Huang said. “Where no tools exist, we develop them—like NVIDIA Parabricks, Clara Imaging, BioMegatron, BioBERT, NVIDIA RAPIDS.”

We’re all hoping COVID-19 research – using such powerful new tools available to scientists – can lead to a vaccine within a year or two, when they have often taken a decade or longer to create.

“The use of big data, supercomputing, and artificial intelligence has the potential to transform research and development; from target identification through clinical research and all the way to the launch of new medicines,” commented James Weatherall, Ph.D., Head of Data Science and AI at AstraZeneca.

During his keynote, Huang provided more details about NVIDIA’s effort to build the UK’s fastest supercomputer – which will be used to further healthcare research – the Cambridge-1.

NVIDIA has established partnerships with companies leading the fight against COVID-19 and other viruses including AstraZeneca, GSK, King’s College London, the Guy’s and St Thomas’ NHS Foundation Trust, and startup Oxford Nanopore. These partners can harness Cambridge-1 for their vital research.

“Tackling the world’s most pressing challenges in healthcare requires massively powerful computing resources to harness the capabilities of AI,” said Huang. “The Cambridge-1 supercomputer will serve as a hub of innovation for the UK and further the groundbreaking work being done by the nation’s researchers in critical healthcare and drug discovery.”

And, for organisations wanting to set up their own AI supercomputers, NVIDIA has announced DGX SuperPODs as the world’s first turnkey AI infrastructure. The solution was developed from years of research for NVIDIA’s own work in healthcare, automotive, healthcare, conversational AI, recommender systems, data science and computer graphics.

While Huang has a nice kitchen, I’m sure he’d like to be back on the big stage for his GTC 2021 keynote. We’d certainly all love COVID-19 to be well and truly in the rear-view mirror.

(Photo by Elwin de Witte on Unsplash)

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

The post GTC 2020: Using AI to help put COVID-19 in the rear-view mirror appeared first on AI News.

NVIDIA is investing heavily in the UK’s accelerating AI sector. The company announced its acquisition of legendary semiconductor giant Arm for $40 billion back in September along with the promise to open a new AI centre in Cambridge.

“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 at the time. “We want to propel Arm – and the UK – to global AI leadership.”

NVIDIA promises to advance Arm’s platform in three major ways:

• NVIDIA will complement Arm partners with GPU, networking, storage and security technologies to create complete accelerated platforms.
• NVIDIA will work with Arm partners to create platforms for HPC, cloud, edge and PC — this requires chips, systems, and system software.
• NVIDIA will port the NVIDIA AI and NVIDIA RTX engines to Arm.

“Today, these capabilities are available only on x86,” Huang said, “With this initiative, Arm platforms will also be leading-edge at accelerated and AI computing.”

Huang also provided more details about NVIDIA’s effort to build the UK’s fastest supercomputer, the Cambridge-1.

Cambridge-1 will boast 400 petaflops of AI performance and will be used by NVIDIA for its vast AI and healthcare collaborations in the UK across academia, industry, and startups.

“Tackling the world’s most pressing challenges in healthcare requires massively powerful computing resources to harness the capabilities of AI,” said Huang. “The Cambridge-1 supercomputer will serve as a hub of innovation for the UK and further the groundbreaking work being done by the nation’s researchers in critical healthcare and drug discovery.”

The company’s first partners are AstraZeneca, GSK, King’s College London, the Guy’s and St Thomas’ NHS Foundation Trust, and startup Oxford Nanopore. A partnership with GSK will also see the world’s first AI drug discovery lab built in London.

“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,” commented Dr Kim Branson, senior vice president and global head of AI and ML at GSK.

“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.”

While there were some natural concerns that Arm’s acquisition would see operations move from the UK to the US, NVIDIA clearly wants to build up its operations in what’s quickly becoming Europe’s AI epicentre.

(Photo by A Perry on Unsplash)

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

The post GTC 2020: Nvidia doubles-down on its UK AI investments appeared first on AI News.

The supercomputer will deliver eye-watering amounts of power from its 285,000 CPU cores and 10,000 GPUs (yes, it can probably even run Crysis.)

OpenAI is a non-profit that was founded by one Elon Musk to promote the ethical development of artificial intelligence technologies. Musk, however, departed OpenAI following disagreements over the company’s direction.

Back in February, Musk responded to an MIT Technology Review profile of OpenAI saying that it “should be more open,” and that all organisations “developing advanced AI should be regulated, including Tesla.”

Microsoft invested $1 billion in OpenAI last year and it seems we’re just beginning to see the fruits of that relationship. While most AIs today focus on doing single tasks well, the next wave of research is focusing on performing multiple at once.

“The exciting thing about these models is the breadth of things they’re going to enable,” said Microsoft Chief Technical Officer Kevin Scott.

“This is about being able to do a hundred exciting things in natural language processing at once and a hundred exciting things in computer vision, and when you start to see combinations of these perceptual domains, you’re going to have new applications that are hard to even imagine right now.”

So-called Artificial General Intelligence (AGI) is the ultimate goal for AI research; the point when a machine can understand or learn any task just like the human brain.

“The creation of AGI will be the most important technological development in human history, with the potential to shape the trajectory of humanity,” said Sam Altman, CEO, OpenAI. “Our mission is to ensure that AGI technology benefits all of humanity, and we’re working with Microsoft to build the supercomputing foundation on which we’ll build AGI.”

“We believe it’s crucial that AGI is deployed safely and securely and that its economic benefits are widely distributed. We are excited about how deeply Microsoft shares this vision.”

AGI will, of course, require tremendous amounts of processing power.

Microsoft and OpenAI claim their new supercomputer would rank in the top five but do not give any specific power measurements. To rank in the top five, a supercomputer would currently require more than 23,000 teraflops of performance. The current leader, the IBM Summit, reaches over 148,000 teraflops.

“As we’ve learned more and more about what we need and the different limits of all the components that make up a supercomputer, we were really able to say, ‘If we could design our dream system, what would it look like?’” said Altman. “And then Microsoft was able to build it.”

Unfortunately, for now at least, the supercomputer is built exclusively for OpenAI.

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

The post Microsoft partners with OpenAI to build Azure supercomputer appeared first on AI News.