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A supercomputer with low-power embedded microprocessors, has been proposed by three researchers from the U.S. Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) to improve global climate change predictions.

This is an innovative approach that would overcome limitations posed by today’s conventional supercomputers.
In the May issue of International Journal of High Performance Computing Applications, Michael Wehner and Lenny Oliker of Berkeley Lab’s Computational Research Division, and John Shalf of the National Energy Research Scientific Computing Center (NERSC) laid out the benefit of a new class of supercomputers for modeling climate conditions and understanding climate change.

They have proposed designing a cost-effective machine by using embedded microprocessor technology. Embedded microprocessors are used in cell phones, iPods, toaster ovens and most other modern day electronic conveniences.

From left to right: Michael Wehner, Lenny Oliker and John Shalf
(Image courtesy of Berkeley National Laboratory)

In April, Berkeley Lab signed a collaboration agreement with Tensilica, Inc. to explore such new design concepts for energy-efficient high-performance scientific computer systems.

Although cloud systems have been included in climate models in the past, they lack the details that could improve the accuracy of climate predictions. Wehner, Oliker and Shalf set out to establish a practical estimate for building a supercomputer capable of creating climate models at 1-kilometer (km) scale. A cloud system model at the 1-km scale would provide rich details that are not available from existing models.

To develop a 1-km cloud model, scientists would need a supercomputer that is 1,000 times more powerful than what is available today, the researchers say. But building a supercomputer powerful enough to tackle this problem is a huge challenge.

Historically, supercomputer makers build larger and more powerful systems by increasing the number of conventional microprocessors — usually the same kinds of microprocessors used to build personal computers. Although feasible for building computers large enough to solve many scientific problems, using this approach to build a system capable of modeling clouds at a 1-km scale would cost about $1 billion. The system also would require 200 megawatts of electricity to operate, enough energy to power a small city of 100,000 residents.

In their paper, “Towards Ultra-High Resolution models of Climate and Weather,” the researchers present a radical alternative that would cost less to build and require less electricity to operate. They conclude that a supercomputer using about 20 million embedded microprocessors would deliver the results and cost $75 million to construct. This “climate computer” would consume less than 4 megawatts of power and achieve a peak performance of 200 petaflops.

According to Shalf:

Without such a paradigm shift, power will ultimately limit the scale and performance of future supercomputing systems, and therefore fail to meet the demanding computational needs of important scientific challenges like the climate modeling.

Under the agreement with Tensilica, the team will use Tensilica’s Xtensa LX extensible processor cores as the basic building blocks in a massively parallel system design.