IBM Research High Performance Computing
IBM scientists (left to right)
Alessandro Curioni, Yves Ineichen and Costas Bekas
We spoke to Yves about his research at IBM and the potential impact of his work.
Yves, congratulations on winning the PRACE Award 2012 for building a general-purpose framework for multi-objective optimizations. What interested you in pursuing this research?
Thank you so much. The Paul Scherrer Institute has a project called SwissFel to build a free-electron laser accelerator. It's an extremely complex machine with numerous parameters or quality objectives that are mutually conflicting. My work approaches this machine as a mathematical, multi-objective optimization model.
Can you explain that in layperson's terms?
Sure. We encounter multi-objective optimization issues all the time in our daily lives. Let's say you want to buy a car, and you want to get the most value for the least amount of money. This is an everyday situation where we make decisions based on several — often conflicting — parameters. That's what we call a multi-objective optimization issue. If money were no object, you could buy the most expensive car with all the features anyone could want. But most of us want to maximize what we call optimality. That's not so simple because, if you improve one objective, it's likely that the others will deteriorate. In a nutshell, our work is to develop computational simulations to model this optimization process. The problem is computationally intensive; to tackle it we developed massively parallel implementations, in which we can utilize hundreds of thousands of cores.
What potential applications does this modeling tool have?
Simply put, the modeling tool accommodates change. Instead of remodeling, say, a factory floor, which can involve a costly "what if" search for an optimal solution, a computer simulation allows for many solutions to be tested at a negligible fraction of the cost. You see, it really makes a difference not to treat individual issues separately but all together. This not only yields a more optimal solution, it helps us to understand better the problem at hand.
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