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DCA++: A case for science driven application development for leadership computing platforms

Michael S Summers1, Gonzalo Alvarez1, Jeremy Meredith1, Thomas A Maier1 and Thomas C Schulthess2

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The DCA++ code was one of the early science applications that ran on jaguar at the National Center for Computational Sciences, and the first application code to sustain a petaflop/s under production conditions on a general-purpose supercomputer. The code implements a quantum cluster method with a Quantum Monte Carlo kernel to solve the 2D Hubbard model for high-temperature superconductivity. It is implemented in C++, making heavy use of the generic programming model. In this paper, we discuss how this code was developed, reaching scalability and high efficiency on the world's fastest supercomputer in only a few years. We show how the use of generic concepts combined with systematic refactoring of codes is a better strategy for computational sciences than a comprehensive upfront design.


PACS

74.20.-z Theories and models of superconducting state

07.05.Bx Computer systems: hardware, operating systems, computer languages, and utilities

02.70.Ss Quantum Monte Carlo methods

74.72.-h Cuprate superconductors (high-Tc and insulating parent compounds)

Subjects

Superconductivity

Computational physics

Instrumentation and measurement

Dates

Issue 1 (2009)



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