Each organization has donated time and expertise of committee members, he said.
While some computer makers and their architects may prefer to ignore a new test for fear their machine will not do well, the hope is that large-scale demand for a more complex test will be a natural outgrowth of the greater complexity of problems.
Studies show that moving data around (not simple computations) will be the dominant energy problem on exascale machines, the next frontier in supercomputing, and the subject of a nascent U.S. Department of Energy initiative to achieve this next level of operations within a decade, Leland said. (Petascale and exascale represent 10 to the 15th and 18th powers, respectively, operations per second.)
Part of the goal of the Graph500 list is to point out that in addition to more expense in data movement, any shift in application base from physics to large-scale data problems is likely to further increase the application requirements for data movement, because memory and computational capability increase proportionally. That is, an exascale computer requires an exascale memory.
"In short, we're going to have to rethink how we build computers to solve these problems, and the Graph500 is meant as an early stake in the ground for these application requirements," said Murphy.
How does it work?
Large data problems are very different from ordinary physics problems.
Unlike a typical computation-oriented application, large-data analysis often involves searching large, sparse data sets performing very simple computational operations.
To deal with this, the Graph 500 benchmark creates two computational kernels: a large graph that inscribes and links huge numbers of participants and a parallel search of that graph.
"We want to look at the results of ensembles of simulations, or the outputs of big simulations in an automated fashion," Murphy said. "The Graph500 is
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DOE/Sandia National Laboratories