Multiphysics Simulations and Petascale Computing
3.6 Looking Ahead
The convergence of multiscale, multiphysics applications with rapidly in- creasing parallelism requires computational scientists to rethink their ap- proach to application development and execution. The largest current ma- chine, Blue Gene/L, has challenged developers to utilize effectively more than 100,000 cores. Several teams have achieved notable successes, but accomplish- ments that now lead the field will need to become commonplace if the scientific computing community is to fully exploit the power of the next generation of supercomputers. Heroic efforts in application development and performance tuning must give way to standardized scalable algorithms and programming methodologies that allow computational scientists to focus on their science rather than underlying computer science issues.
Work is now underway at LLNL and other research institutions to find the best ways to harness petascale computers, but these efforts will not yield near- term results. When large-scale distributed memory computers began to domi- nate scientific computing in the early 1990s, there was a period of uncertainty as developers of hardware, systems software, middleware, and applications worked to determine which architectures and programming models offered the best combination of performance, development efficiency, and portability. Eventually, a more-or-less standard model emerged that embraced data par- allel programming on commodity processors using MPI communication and high-speed interconnection networks.
The next generation of applications and hardware will bring about another period of uncertainty. Multiphysics simulations need programming models that more naturally lend themselves to MPMD applications. In light of this, the authors believe that the dominant paradigm for computational science ap- plications running on petascale computers will be task parallel programming that combines multiple scalable components into higher-level programs.
3.7
Acknowledgments
We thank the following colleagues for generously contributing to this chap- ter: Bob Anderson, Nathan Barton, Erik Draeger, Rob Falgout, Rich Hor- nung, David Jefferson, David Keyes, Gary Kumfert, James Leek, Fred Streitz, and John Tannahill. We also acknowledge the U.S. National Nuclear Security Administration’s Advanced Simulation and Computing program, the Depart- ment of Energy Office of Science’s Scientific Discovery through Advanced Computing program, and LLNL’s Laboratory Directed Research and Devel- opment program for their support.
This work was performed under the auspices of the U.S. Department of Energy by University of California, Lawrence Livermore National Laboratory under Contract W-7405-Eng-48. UCRL-BOOK-227348.
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