MODELLING AND SIMULATION


Pushing the envelope


GEMMA CHURCH WONDERS IF DRIVERLESS CARS AND BIG DATA ARE QUIETLY OVERHAULING MODELLING AND SIMULATION


High performance computing and big data analytics are natural bedfellows. It is important that


both can move data, exchange messages and analyse computed results from thousands of parallel processes fast enough to keep those computing resources running at peak efficiency. But for the true power of big data


analytics to be harnessed inside a powerful HPC architecture, we need to integrate machine learning techniques into our approach. And this will open the floodgates to a range of new applications, as Fatma Kocer, vice president design exploration at Altair, explained: ‘We are now looking into using machine learning in applications where we do not have viable physics-based solutions. These applications could be ones for which no physics-based solutions exist. They can also be ones that have a physics-based solution, however, they are resource intensive, especially compared to the data-based solutions.’ But big data is so different to traditional


data sets that it forces us to change our simulation and modelling strategies. Kocer said: ‘Now, we have big data whereas, in the past, we had to work with barely enough data. Now, the data is coming from the field, instead of coming from controlled design of experiments. As a result, there is noise that needs to be filtered; there are errors that need to be cleaned; there are attributes that need to be ignored for successful applications of machine learning techniques.’ ‘So, in short, the challenge is not


working in the machine learning space as a simulation company; the challenge is to move from a controlled, small data set to big real-time field data,’ she added.


20 Scientific Computing World December 2017/January 2018


Working with big data Until recently, the majority of big data applications have been based on conventional modelling and simulation techniques. However, we are just starting to see small breakthroughs in this area. For example, IBM Research recently overcame a key technical limitation where many deep learning frameworks do not run efficiently across multiple servers and can struggle to scale beyond a single node. Using a new distributed deep learning software, it achieved a record communication overhead and 95 per cent scaling efficiency. Altair has also used machine learning


techniques to create predictive models for design optimisation. ‘We have users that have no simulation models but they have data from testing. They use our products to create predictive models from these data sets to optimise their designs. We also have users that have resource intensive simulations, such as CFD simulations, that are prohibitive


for many optimisation studies. They use our products to create training and testing data. They then continue creating predictive models using this data. Finally, they use these predictive models for design optimisation,’ Kocer explained. ‘These mathematical techniques can


be successfully leveraged for design improvements without needing a statistician or an optimisation expert,’ Kocer added.


This is the tip of the iceberg. Big data will drive the development of new forms of HPC configurations to unlock the insights held in these huge swathes of unstructured data. This convergence of big data analytics and HPC, also known as high-performance data analytics (HPDA), will accelerate the development of many tantalising applications – including driverless cars.


Autonomous rides We’re seeing great strides in the development of driverless cars through


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