Earlier this year, Microsoft announced its supercomputer hosted in Azure Cloud which it developed in collaboration with OpenAI. The company said that the supercomputer could train various artificial intelligence models and comes with more than 2,85,000 CPU cores, 10,000 GPUs, and 400Gbps of network connectivity for each GPU server. Not just this, Hewlett Packard Enterprise recently acquired the supercomputing leader Cray, following which it has introduced the HPE Cray supercomputing line that can perform data-centric AI workloads with exceptionally high speed. It also built the new TX-GAIA (Green AI Accelerator) computing system at the Lincoln Laboratory Supercomputing Center which has been ranked as the most powerful AI supercomputer at any university in the world. With a performance of 100 AI petaflops, it can perform complex deep neural network operations with much ease.

And it will require a totally different approach powered by a new supercomputer architecture. While early adopters of AI mostly took the same approach out of necessity ― small-scale AI integration at certain points within a traditional workflow ― the next wave will be marked by end-to-end AI workflows as part of companies' core business practices. Supercomputing will be the line of demarcation between organizations with the competitive advantage in AI technology, and those left behind. We began developing our new artificial intelligence system years ago using insights from our customers' widely varied use cases. The result is not just a new supercomputer, but a new way of supercomputing, ready to scale with the demands of the next wave of AI.

HPE has agreed to buy supercomputer-maker Cray for $1.3 billion, a deal that the companies say will bring their corporate customers high-performance computing as a service to help with analytics needed for artificial intelligence and machine learning, but also products supporting high-performance storage, compute and software. In addition to bringing HPC capabilities that can blend with and expand HPE's current products, Cray brings with it customers in government and academia that might be interested in HPE's existing portfolio as well. The companies say they expect to close the cash deal by the end of next April. Such a service could address periodic enterprise need for fast computing that might otherwise be too expensive, says Tim Zimmerman, an analyst with Gartner. Businesses could use the service, for example, to create digital twins of their entire networks and use them to test new code to see how it will impact the network before deploying it live, Zimmerman says.

Episode Summary: Some businesses are going to require a sea change in the way that their computation works and the kinds of computing power that they're leveraging to do what they need to do with artificial intelligence. Others might not need an upgrade in hardware in the near term to do what they want to do with AI. That's the question that we decided to ask today of Per Nyberg, Vice President of Market Development, Artificial Intelligence and Cloud at Cray. Cray is known for the Cray-1 supercomputer, built back in 1975. Cray continues to work on hardware and has an entire division now dedicated to artificial intelligence hardware.

The explosion of AI and machine learning is changing the very nature of computing, so says one of the biggest practitioners of AI, Google. Google software engineer Cliff Young gave the opening keynote on Thursday morning at the Linley Group Fall Processor Conference, a popular computer-chip symposium put on by venerable semiconductor analysis firm The Linley Group, in Santa Clara, California. Said Young, the use of AI has reached an "exponential phase" at the very same time that Moore's Law, the decades-old rule of thumb about semiconductor progress, has ground to a standstill. "The times are slightly neurotic," he mused. "Digital CMOS is slowing down, we see that in Intel's woes in 10-nanometer [chip production], we see it in GlobalFoundries getting out of 7-nanometer, at the same time that there is this deep learning thing happening, there is economic demand."

There was much celebrating in America last month when the US Department of Energy unveiled Summit, the world's fastest supercomputer. Now the race is on to achieve the next significant milestone in processing power: exascale computing. This involves building a machine within the next few years that's capable of a billion billion calculations per second, or one exaflop, which would make it five times faster than Summit (see chart). Every person on Earth would have to do a calculation every second of every day for just over four years to match what an exascale machine will be able to do in a flash. This phenomenal power will enable researchers to run massively complex simulations that spark advances in many fields, from climate science to genomics, renewable energy, and artificial intelligence.

Digital Catapult is partnering with high-performance computing (HPC) firm Cray to give startups in its Machine Intelligence Garage accelerator access to supercomputing resources. In this e-guide we've pulled together some of the latest thinking on good datacentre management practice and shone a light on the tools and tech that can help enterprises run their facilities with greater ease and agility. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.

Today Cray announced that it has partnered with Digital Catapult and its Machine Intelligence Garage in the UK to help organizations of all sizes speed the development of machine intelligence systems. This partnership enables the Machine Intelligence Garage to offer companies the supercomputing power and expertise required to develop and build machine learning and AI solutions. Our partnership with Cray opens a new door for UK machine intelligence businesses," said Anat Elhalal, Head of Technology and AI/ML Lead Tech at Digital Catapult. "Cray's expertise in high-performance computing will give startups much-needed resources to continue the amazing work already underway in the UK for AI and machine learning solutions." The Machine Intelligence Garage is a program delivered by Digital Catapult, which supports and promotes advanced digital technology and transformation to drive growth in the UK economy. The effort takes its name from the mythical place garages have in the history of technology. The Machine Intelligence Garage holds an open call every six to 12 weeks to select startups that can benefit most from the computational resources the Machine Intelligence Garage can offer from within the organization or through its partners. It then selects about 30 startups and develops for each a tailored program offering supervision, feedback and a case study of each startup's corresponding results. Based on research conducted by the Machine Intelligence Garage, startups in the UK have identified that one of their biggest obstacles to progress in AI is access to exceptionally powerful and fast computational systems," said Per Nyberg, vice president of market development, artificial intelligence and cloud at Cray. "We're seeing a convergence between AI and supercomputing as organizations work toward taking AI projects out of beta and into production. The number of AI models that must be trained will only increase and organizations are coming to realize the performance and I/O challenges that will arise. Cray's innovative architectures and robust compute resources, as well as access to our Cray Accel AI Lab, can help address these challenges and give startups the fundamental tools they need to develop successful machine learning systems and initiatives."

From tin-can robots to sophisticated, sentient virtual environments, artificial intelligence (AI) is a dominant theme in science fiction. With real-world advances in machine learning and deep learning, the gap between fact and fiction is narrowing. From Siri, search engines and motion-sensing video games to medical imaging and diagnostics, artificial intelligence is an increasingly significant part of our lives. Cray systems are used every day to solve artificial intelligence problems through machine learning and deep learning approaches. In this three-part blog series, we'll look at a few examples of AI in sci-fi and see how they match up with reality. Robots -- especially humanoid robots -- are often the first thing that comes to mind when we think about artificial intelligence.

Deep Learning Invades HPC While many algorithms are commonly referred to as "machine learning" (ML) or "artificial intelligence" (AI), deep learning with neural networks (NNs) has dominated the attention of the ML industry in recent years. Though numerous alternatives exist – including support vector machines, Bayesian classifiers, genetic algorithms, clustering techniques, and even decision trees – NNs have experienced a rapid increase in real-world effectiveness during recent years. Continued improvements in computing hardware help propel the ongoing expansion in the use of NNs by many industries. In fact, the demand for larger and more-powerful neural networks motivates many to leverage the unique scaling advantages provided by high-performance computing (HPC), including Cray's high-end clusters and supercomputers. Specifically, current scaling to small node counts is no longer sufficient for today's larger NN workloads, let alone the workloads of the future.