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New algorithm to enable 400+ TFlop/s sustained performance in simulations of disorder effects in high-Tc superconductors

Gonzalo Alvarez  (Oak Ridge National Laboratory)
Michael S. Summers  (Oak Ridge National Laboratory)
Don E. Maxwell  (Oak Ridge National Laboratory)
Markus Eisenbach  (Oak Ridge National Laboratory)
Jeremy S. Meredith  (Oak Ridge National Laboratory)
Jeffrey M. Larkin  (Cray Inc.)
John M. Levesque  (Cray Inc.)
Thomas A. Maier  (Oak Ridge National Laboratory)
Paul R. Kent  (Oak Ridge National Laboratory)
Eduardo D'Azevedo  (Oak Ridge National Laboratory)
Thomas C. Schulthess  (Oak Ridge National Laboratory)
ACM Gordon Bell Finalists Session
Wednesday,  04:00PM - 04:30PM
Room Ballroom G
Staggering computational and algorithmic advances in recent years now make possible systematic Quantum Monte Carlo simulations of high temperature superconductivity in a microscopic model, the two dimensional Hubbard model, with parameters relevant to the cuprate materials. Here we report the algorithmic and computational advances that enable us to study the effect of disorder and nano-scale inhomogeneities on the pair-formation and the superconducting transition temperature. Significant algorithmic improvements have been made to make effective use of current supercomputing architectures. By implementing delayed Monte Carlo updates and a mixed single/double precision method, we are able to dramatically accelerate the time to solution. On the Cray XT4 systems of the Oak Ridge National Laboratory, for example, we currently reach a sustained performance of 409 TFlop/s on 49 thousand cores. We present here a study of how random disorder in the effective Coulomb interaction strength affects the superconducting transition temperature in the Hubbard model.
The full paper can be found in the IEEE Xplore Digital Library and ACM Digital Library
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