Accelerating the Fourier split operator method via graphics processing units

Abstract Current generations of graphics processing units have turned into highly parallel devices with general computing capabilities. Thus, graphics processing units may be utilized, for example, to solve time dependent partial differential equations by the Fourier split operator method. In this contribution, we demonstrate that graphics processing units are capable to calculate fast Fourier transforms much more efficiently than traditional central processing units. Thus, graphics processing units render efficient implementations of the Fourier split operator method possible. Performance gains of more than an order of magnitude as compared to implementations for traditional central processing units are reached in the solution of the time dependent Schrodinger equation and the time dependent Dirac equation.

[1]  André D. Bandrauk,et al.  Exponential split operator methods for solving coupled time-dependent Schrödinger equations , 1993 .

[2]  M. Feit,et al.  Solution of the Schrödinger equation by a spectral method , 1982 .

[3]  Steven G. Johnson,et al.  The Design and Implementation of FFTW3 , 2005, Proceedings of the IEEE.

[4]  H. Reiss,et al.  Dipole-approximation magnetic fields in strong laser beams , 2000 .

[5]  Bernd Thaller,et al.  The Dirac Equation , 1992 .

[6]  Q. Su,et al.  Relativistic suppression of wave packet spreading. , 1998, Optics express.

[7]  Herb Sutter,et al.  A Fundamental Turn Toward Concurrency in Software , 2008 .

[8]  John D. Owens,et al.  GPU Computing , 2008, Proceedings of the IEEE.

[9]  Su,et al.  Numerical solution of the time-dependent Maxwell's equations for random dielectric media , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[10]  Bernd Thaller,et al.  Advanced visual quantum mechanics , 2005 .

[11]  Gulcin M. Muslu,et al.  Higher-order split-step Fourier schemes for the generalized nonlinear Schrödinger equation , 2005, Math. Comput. Simul..

[12]  Guido R. Mocken,et al.  Quantum dynamics of relativistic electrons , 2004 .

[13]  Adalbert Kerber,et al.  The Cauchy Problem , 1984 .

[14]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[15]  Robert J. Brunner,et al.  High-Performance Computing with Accelerators , 2010, Comput. Sci. Eng..

[16]  John C. Light,et al.  On the Exponential Form of Time‐Displacement Operators in Quantum Mechanics , 1966 .

[17]  Christoph H. Keitel,et al.  A real space split operator method for the Klein-Gordon equation , 2009, J. Comput. Phys..

[18]  Jie Cheng,et al.  Programming Massively Parallel Processors. A Hands-on Approach , 2010, Scalable Comput. Pract. Exp..

[19]  J. R. Morris,et al.  Time-dependent propagation of high energy laser beams through the atmosphere , 1976 .

[20]  H. Feshbach,et al.  Elementary Relativistic Wave Mechanics of Spin 0 and Spin 1/2 Particles , 1958 .

[21]  Rainer Grobe,et al.  Numerical approach to solve the time-dependent Dirac equation , 1999 .

[22]  Michael J. Flynn,et al.  Some Computer Organizations and Their Effectiveness , 1972, IEEE Transactions on Computers.

[23]  Guido R. Mocken,et al.  FFT-split-operator code for solving the Dirac equation in 2+1 dimensions , 2008, Comput. Phys. Commun..

[24]  C. H. Keitel,et al.  Dynamics of multiply charged ions in intense laser fields , 2001 .

[25]  G. Strang On the Construction and Comparison of Difference Schemes , 1968 .

[26]  S. Sharma,et al.  The Fokker-Planck Equation , 2010 .

[27]  Vladimir Surkov Parallel option pricing with Fourier Space Time-stepping method on Graphics Processing Units , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.