HPC 2017-18 | High-performance computing


AI drives new computing technologies


Robert Roe looks at advances in AI computing technology


As the market for artificial intelligence matures, it is helping to drive accelerated growth in computing technologies to support highly parallel workloads, artificial intelligence (AI) and machine learning (ML). Tis new paradigm in computing is opening up the benefits of GPU or accelerated computing to a broader audience – far beyond the traditional users of supercomputing. Te growth in AI and machine learning has


been dramatic. In April this year, market research firm IDC predicted that western European revenues for cognitive and AI systems would reach $1.5 billion in 2017. IDC predicts this rise will continue in the


coming years as the company forecasts a growth rate of 42.5 per cent through 2020 when revenues will exceed $4.3 billion. Much of this growth comes from comes from


three key industries which were early adopters of AI and cognitive systems – banking, retail, and discrete manufacturing, although the IDC report does note that cross-industry applications have the largest share across all industries. Te report states that by 2020 these industries


– including cross-industry applications – will account for almost half of all IT spending on cognitive and artificial intelligence systems. ‘IDC is seeing huge interest in cognitive


applications and AI across Europe right now, from different industry sectors, healthcare, and government,’ said Philip Carnelley, research director for Enterprise Soſtware at IDC Europe, and leader of IDC’s European AI Practice. ‘Although only a minority of European


organisations have deployed AI solutions today, a large majority are either planning to deploy


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Loihi – Intel’s first self-learning neuromorphic computer chip


or evaluating its potential. Tey are looking at use cases with clear ROI, such as predictive maintenance, fraud prevention, customer service, and sales recommendation,’ Carnelley added. Te report also notes that from a technology


perspective, the most significant area of spending in western Europe in 2017 will be cognitive applications at approximately $516 million. Tis includes ‘cognitively-enabled’ process and industry applications that automatically learn, discover, and make recommendations or predictions. ‘Cognitive Computing is coming, and we


expect it to embed itself across all industries. However, early adopters are those tightly regulated industries that need robust decision support: finance, specifically banking and securities investment services, is one of these early adopters,’ said Mike Glennon, associate vice president for customer insights and analysis at IDC. ‘However, the cost savings to be found in automating decision support in a structured


environment, together with the enhanced ability to identify previously hidden aspects of behaviour, ensure the distribution and services and public sectors embrace cognitive computing and artificial intelligence systems – where it can offer the dual benefits of lowering cost, and growing new business. We also expect strong growth in adoption in manufacturing in w estern Europe, at the core of industry across the region,’ Glennon added. Tis interest is reflected in a large growth


in the number of applications that are finding their way into use in both industry, academia. AI applications are being run on appliances and small servers all the way up to the largest supercomputers. Tis is true of even the leadership class HPC facilities which are pursuing research into machine learning and AI research applications.


Developing AI technology Nvidia and Intel are the primary hardware providers for AI and ML applications, but several other companies have released processing technologies aimed at AI and ML applications.


Although only a minority of European organisations have deployed AI solutions today, a large majority are either planning to deploy or evaluating its potential


Tese range from Nvidia GPU’s to Intel coprocessors or even FPGA technology. While the DL and ML algorithms have


been around for some time, it is the arrival of incredibly parallel computing technologies such as GPUs that have enabled the explosion in ML/ DL applications. Te falling price of these GPUs and other accelerators alongside increasing computational performance that is well suited to highly parallel applications have enabled accelerator technology to flourish in this new market. Tis year has seen Intel and Nvidia release


new hardware, tools and investment aimed at capturing the AI market. Nvidia initially launched is DGX-1 appliance in 2016. Tis was updated in 2017 to deliver higher performance by adding the Volta GPUs to the DGX-1 system. Te first the first examples of the Volta-based DGX-1 were delivered to supercomputing sites in September 2017. But it is not just hardware that is the ground for stiff competition. Te development of


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