Exascale now a global race for tech

Top scientist Peter Beckman details U.S. steps, international rivalry

By , Computerworld |  Data Center, supercomputers

Did they ask you to adjust the 20MW requirements? All the responders said it would be a difficult target to reach without a strong investment. If we allowed them twice as much power, 40MW or 50MW, then it is much simpler. They also said that the system software and the whole software stack required an integrated approach. Most of the responses, I would say, were light on the data challenges. People know that data is a challenge, but they really focused, in the responses, on the computing.

What is the exascale data challenge? If we imagine that we have a machine that is an exascale, exaflop machine, generating petabytes and petabytes of data, it becomes its own, in some sense, computation problem. We can't solve the bandwidth storage problem by just buying more disks. A multi-level plan is what will have to evolve, including NVRAM and even novel technologies such as phase change memory . But there has to be a comprehensive data solution that includes analysis. It can't be, 'Oh, we just need to be able to store the data.' We need to look up the architecture necessary to analyze the data. If you look at Google and the other web-based technologies, they have come up with ways to store and analyze data -- a way in which you have a programming model where the storage and analysis are very close.

In computing we haven't done that yet. We've always had the model where the data is over here, the computing is over [there]; you ask for the data, you get a copy of it, you put it in the computer, you work on it a lot, and then you put it back. And so as we move to exascale, where this computing becomes really more powerful and the data sets become bigger, sloshing this back and forth is way too costly in terms of power and performance -- power, especially. It's movement that cost a lot of electrical power. We need to find to ways to compute and then analyze and do the storage and analysis closer together.

Is there anything out there like that today? Some types of data lend themselves to spreading out the computation though the data -- satellite images and other things. People have had this sort of capability for certain types of data sets. But we really need to think broadly about the problem. What you want to do is figure out ways to slice and dice the data, and do analysis on the data in an integrated architecture. And that's something that will become more important at exascale that we haven't addressed very well, yet.


Originally published on Computerworld |  Click here to read the original story.
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