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LBNL’s Evaluation of Earth Simulator Performance Nominated for Best Paper Award at SC2004

September 1, 2004

With the re-emergence of viable vector computing systems such as the Earth Simulator and the Cray X1, there is renewed debate about which architecture is best suited for running large-scale scientific applications. In order to cut through the conflicting claims of fastest, biggest, etc., a team led by Lenny Oliker of CRD’s Future Technologies Group put five different systems through their paces, running four different scientific applications key to DOE research programs. As part of the effort, the group became the first international team to conduct a performance evaluation study of the 5,120-processor Earth Simulator. The team also assessed the performance of:

  •  the 6,080-processor Power3 IBM supercomputer running AIX 5.1 at the NERSC Center at Lawrence Berkeley National Laboratory 
  •  the 864-processor Power4 IBM supercomputer running AIX 5.2 at Oak Ridge National Laboratory
  •  the 256-processor SGI Altix 3000 system running 64-bit Linux at ORNL
  •  the 512-processor Cray X1 supercomputer running UNICOS at ORNL.                            

The results of the comparison are of great interest to the HPC community. The team’s paper was accepted for the SC2004 conference, then nominated for Best Paper. The winning paper will be announced at the conference in November.

Earth Simulator

In addition to Oliker, the team includes Andrew Canning, Jonathan Carter and John Shalf, all of LBNL, and Stephane Ethier of Princeton Plasma Physics Laboratory.

“This effort relates to the fact that the gap between peak and actual performance for scientific codes keeps growing,” said Oliker, who won the Best Paper Award at SC99. “Because these systems are so expensive, it’s important to know which applications are best suited to which architecture.”

In their abstract, the group members write, “Computational scientists have seen a frustrating trend of stagnating application performance despite dramatic increases in the claimed peak capability of high performance computing systems. This trend has been widely attributed to the use of superscalar-based commodity components whose architectural designs offer a balance between memory performance, network capability, and execution rate that is poorly matched to the requirements of large-scale numerical computations.”

ORNL’s Altix

The four applications and research areas selected by the team for the evaluation are:

  • Cactus, an astrophysics code that evolves Einstein’s equations from the Theory of Relativity using the Arnowitt-Deser-

    ORNL's Cray X1

    Misner method
  • GTC, a magnetic fusion application that uses the particle-in-cell approach to solve non-linear gyrophase-averaged Vlasov-Poisson equations
  • LBMHD, a plasma physics application that uses the Lattice-Boltzmann method to study magnetohydrodynamics
  • PARATEC, a first principles materials science code that solves the Kohn-Sham equations of density-functional theory to obtain electronic wave functions.

So, what are the team’s conclusions?

“The four applications successfully ran on the Earth Simulator with high scalability,” Oliker said. “And they ran faster than as measured on any other architecture.”

However, Oliker added, only codes that scale well and are suited to the vector architecture may be run on the Earth Simulator.

“Vector architectures are extremely powerful for the set of applications that map well to those architectures,” Oliker said. “But if even a small part of the code is not vectorized, overall performance degrades rapidly.”

ORNL’s IBM Power 4

As with most scientific inquiries, the ultimate solution to the problem is neither simple nor straightforward.

“We’re at a point where no single architecture is well suited to the full spectrum of scientific applications,” Oliker said. “One size does not fit all, so we need a range of systems. It’s conceivable that future supercomputers would have heterogeneous architectures within a single system, with different sections of a code running on different components.”

The team’s full paper can be found at: <http://crd.lbl.gov/~oliker/papers/SC04.pdf>.

About Computing Sciences at Berkeley Lab

The Computing Sciences Area at Lawrence Berkeley National Laboratory provides the computing and networking resources and expertise critical to advancing Department of Energy Office of Science research missions: developing new energy sources, improving energy efficiency, developing new materials, and increasing our understanding of ourselves, our world, and our universe.

Founded in 1931 on the belief that the biggest scientific challenges are best addressed by teams, Lawrence Berkeley National Laboratory and its scientists have been recognized with 13 Nobel Prizes. Today, Berkeley Lab researchers develop sustainable energy and environmental solutions, create useful new materials, advance the frontiers of computing, and probe the mysteries of life, matter, and the universe. Scientists from around the world rely on the Lab’s facilities for their own discovery science. Berkeley Lab is a multiprogram national laboratory, managed by the University of California for the U.S. Department of Energy’s Office of Science.

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