David Geer recently spoke with Rick Stevens, Computing and Life Sciences Directorate Lead at the Argonne National Laboratory and an internationally recognized expert who helps drive the national agenda on computing. Following is an edited transcript of that conversation.

Listen to the original interview here, or visit our Podcast Center for more audio interviews.

David Geer: Briefly describe your current work on petaflops.

Rick Stevens: Well, let's see. At the sort of bottom of what we're doing, we're standing up very large supercomputers. We're in the process of acquiring a next-generation IBM Blue Gene system, which will be up and running later this year, and that has an architecture that will support petaflop-scale computing -- petascale computing. And then in addition to the hardware, our lab has been developing operating system software, system software, tools, file systems, that scale so we can run them on machines with hundreds of thousands of processors, which this machine will have. And then layering on top of that is a broad class of applications, ranging from basic physics to the modeling of nuclear reactors to the design of nano materials to the analysis of genomes to the screening of new drug compounds, and many other areas. So, it's probably good to think of this as sort of a stack -- not that different from the software stack that's on a laptop or a PC, except that in our case we're talking about hundreds of thousands of cores in the system.

Geer: What's most fascinating about your work as you've described it here?

Stevens: Well, the things that keep it interesting are that the computer architecture trajectory that we're on, or think of it as more of the general IT technology trajectory that we're on, (but of course we're trying to exploit that trajectory, driven by Moore's Law and associated things, to get ever more capable systems for doing science). So every day, in some sense, our job is to figure out what's next. What's the next class of architecture, what's the latest improvement in process technologies that will create the next turn of chips? And how do we harness that capability, that sort of underlying stuff, to do more interesting science?

And what's interesting about this thing is that if you go back 15, 20 years, the community has been on this track for a long time and we can plot out, just like we can plot out the increasing performance of desktop or laptop machines or servers in a conventional environment. We can do the same thing for supercomputers and they track that same trend because, at least nowadays, they're built on essentially the same technology, which wasn't true, say, 20 years ago.

But they're actually ahead of that track, and it's because on the scientific computing domain or the area at which we work, people have one more dimension in which they've been able to exploit performance, and that is scale, that is taking parallelism to new levels. So ten years ago,

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