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HIGH PERFORMANCE COMPUTING


‘Those cases where a classical algorithm may be difficult to design... AI can come to the rescue and help you come up with a solution’


formulas and then tune the parameters, so their expectations of the training data match up with an existing understanding of real-world data that can then be used to verify the approximations made by the parametrised model,’ Angerer continued.


www.scientific-computing.com | @scwmagazine


In this case, the use of AI means that


you do not need to work manually on the models, Similarly writing feature detectors for image recognition is now a challenge that has been passed along to the neural network, rather than being coded by people. The increase in compute and training data has supplanted the hand-coded work of scientists trying to understand weather and climate. ‘You do not have to come up with these


surrogate models in detail, but this means you can focus on the physics to produce training data, but then in order to speed it up or make it usable in a production run, you can use AI models,’ added Angerer.


‘In this case, it is not about getting it done at all, because there are existing algorithms that can do what you want the AI to do. It is more about the speed-up, depending on the example you pick, but there is anecdotal evidence of not only 10x speed-up but 10,000x speed-up which represents an improvement of many orders of magnitude.


Making gains While this may seem like a ridiculous number, there are a number of factors that can help to drive this number up by orders of magnitude. Some of these codes were originally developed many


October/November 2019 Scientific Computing World


g 5


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