Supercomputers allow for more realistic earthquake simulations

NIVIDIA’s Tesla K20X GPU accelerators.

The release notes that the benchmarks, run on Titan, showed a five-fold speedup over the heavily optimized CPU code on the same system, and a sustained performance of one petaflop per second (one quadrillion calculations per second) on the tested system. A previous benchmark of the AWP-ODC code reached only 200 teraflops (trillions of calculations per second) of sustained performance.

By delivering a significantly higher level of computational power, researchers can provide more accurate earthquake predictions with increased physical reality and resolution, with the potential of saving lives and minimizing property damage.

“This is an impressive achievement that has made petascale-level computing a reality for us, opening up some new and really interesting possibilities for earthquake research,” said Thomas Jordan, director of SCEC, which has been collaborating with UC San Diego and SDSU researchers on this and other seismic research projects, such as the simulation of a magnitude 8.0 earthquake, the largest ever simulation to-date.

“Substantially faster and more energy-efficient earthquake codes are urgently needed for improved seismic hazard evaluation,” said Cui, citing the recent destructive earthquakes in China, Haiti, Chile, New Zealand, and Japan.

While the GPU-based AWP-ODC code is already in research use, further enhancements are being planned for use on hybrid heterogeneous architectures such as Titan and Blue Waters.

“One goal going forward is to use this code to calculate an improved probabilistic seismic hazard forecast for the California region under a collaborative effort coordinated by SCEC,” said Cui. “Our ultimate goal is to support development of a CyberShake model that can assimilate information during earthquake cascades so we can improve our operational forecasting and early warning systems.”

CyberShake is a SCEC project focused on developing new approaches to performing seismic hazard analyses using 3D waveform modeling. The GPU-based code has potential to save hundreds of millions of CPU-hours required to complete statewide seismic hazard map calculations in planning.

Compute resources used for this research are supported by XSEDE under NSF grant number OCI-1053575, while additional funding for research was provided through XSEDE’s Extended Collaborative Support Service (ECSS) program.

“ECSS exists for exactly this reason, to help a research team make significant performance gains and take their simulations to the next level,” said Nancy Wilkins-Diehr, co-director of the ECSS program and SDSC’s associate director. “We’re very pleased with the results we were able to achieve for PI Thomas Jordan and his team. ECSS projects are typically conducted over several months to up to one year. This type of targeted support may be requested by anyone through the XSEDE allocations process.”