HPC 2017-18 | High-performance computing


to meaningful solutions for UK industry.’ A recent success is its cognitive hospital


project the Alder Hey Children’s Hospital, which was awarded ‘Most Innovative Collaboration’ at the North West Coast Research and Innovation Awards 2017. Hospitals produce a huge amount of data, yet it is very difficult for clinicians and patients to use that data to improve their hospital experience. Using the power of the IBM Watson, an app has been designed so that children can engage and ask questions about the procedure that they’re about to undertake at the hospital. Answers are then given through a friendly avatar. Te IBM Watson cognitive computing


system can process the huge amounts of data it receives quickly, extracting the most relevant and important parts. It can then transform this mountain of information into useful and personal insights that can be used to improve services or treatments at Alder Hey. Tis information also helps the patient to


understand and prepare for a procedure at home. ‘Tis could help to reduce the number of no shows and will help hospital staff address any questions the patient has before they get to the hospital, which frees their time too,’ Kennedy added. Such large-scale data analysis has wider


implications across the HPC sector, as Daniel Reed, vice president for research and economic development at the University of Iowa and fellow of the Association for Computing Machinery, said: ‘Traditional HPC applications usually start with a question and want an answer. Now, we are starting with a set of equations and we want to compute the implications. Such large scale data analysis has turned that concept on its head.’


Machine learning Te work at Alder Hey is the tip of the iceberg as the advancement of scalable artificial intelligence (AI) and machine learning (ML) applications has really stood out for HPC in the 12 months. While the concepts of AI are not new, progress


has been facilitated by HPC in this area as the increased volumes of data we now collect allow us to train computers to make decisions based on past examples. Faster network speeds enable us to move greater amounts of data around, and better compute elements for parallel execution of data. Gilad Shainer, chairman of the HPC Advisory


Council, said: ‘As these three conditions are now met, we can actually leverage AI. AI will impact nearly all aspects of our lives – from making better financial decisions, improving our security, developing self-driving vehicles, forecasting health issues and many other areas.’ Lui said: ‘HPC has helped AI/ML by reducing


runtime to process a workload on a single workstation which may take months to complete;


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The Hartree Centre is home to some of the most advanced high performance computing in the UK


the people involved in AI/ML development learned to use HPC to deploy a scale-out approach which makes use of the hardware accelerators like Nvidia Volta GPUs, and EDR InfiniBand for high-speed, low-latency network in a HPC cluster environment to reduce the runtime of training a deep neural network. ‘Te applications that use AI/ML that have


really exploded in the last year include algorithms for detecting objects and lane in self-driving cars, object classification in image recognition, fraud detection in financial transactions and speech recognition in videos,’ Lui added.


High performance computing and artificial intelligence share similar hardware requirements. Important to both is the ability to move data, exchange messages and computed results from thousands of parallel processes fast enough to keep the compute resources running at peak efficiency


Te accelerated use of HPC for AI has been,


in part, facilitated by better hardware resources, as Scot Schultz, senior director of HPC/Artificial Intelligence and Technical Computing at Mellanox Technologies, explained: ‘High performance computing and artificial intelligence share similar hardware requirements and important to both is the ability to move data, exchange messages and computed results from thousands of parallel processes fast enough to keep the compute resources running at peak efficiency.’ For example, IBM Research just announced


unprecedented performance and close to ideal scaling with its new distributed deep learning


soſtware, which achieved a record communication overhead and 95 per cent scaling efficiency on the Caffe deep learning framework with Mellanox InfiniBand and over 256 Nvidia GPUs in 64 IBM Power systems. Schultz said: ‘With the IBM DDL (Distributed Deep Learning) library, it took just seven hours to train ImageNet-22K using ResNet-101. From 16 days down to just seven hours not only changes the workflow of data scientists, this changes the game entirely.’ In May 2017, Nvidia also introduced Volta,


the world’s most powerful GPU computing architecture, created to drive the next wave of advancement in artificial intelligence. Te world’s first Nvidia DGX systems with Volta AI were recently shipped to the Center for Clinical Data Science (CCDS). Paresh Kharya, group product marketing manager at Nvidia, said: ‘More specifically, with this technology, CCDS data scientists can develop a host of new training algorithms to help them see medical abnormalities and patterns within medical images.’ Te Tesla V100 GPU broke through the 100


Tflops barrier of deep learning performance. Kharya added: ‘Demand for accelerating AI has never been greater across every industry, including healthcare, pharma, financial services, auto, retail, and telecommunications. Developers, data scientists, and researchers increasingly rely on neural networks to power their next advances in fighting cancer, making transportation safer with self-driving vehicles, providing new intelligent customer experiences, and more.’ AI-based applications have certainly


dominated in the last 12 months, and this trend shows no signs of stopping. We can expect AI to become increasingly integrated in the HPC landscape, as Shainer explained: ‘We will see continuous development in this area, from hardware elements to soſtware elements and it will just keep progressing. Several years from now, we will probably be talking less about AI as it becomes mainstream and tightly integrated into more solutions.’l


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