Energy-Efficient Computational Chemistry: Comparison of x86 and ARM Systems.

The computational efficiency and energy-to-solution of several applications using the GAMESS quantum chemistry suite of codes is evaluated for 32-bit and 64-bit ARM-based computers, and compared to an x86 machine. The x86 system completes all benchmark computations more quickly than either ARM system and is the best choice to minimize time to solution. The ARM64 and ARM32 computational performances are similar to each other for Hartree-Fock and density functional theory energy calculations. However, for memory-intensive second-order perturbation theory energy and gradient computations the lower ARM32 read/write memory bandwidth results in computation times as much as 86% longer than on the ARM64 system. The ARM32 system is more energy efficient than the x86 and ARM64 CPUs for all benchmarked methods, while the ARM64 CPU is more energy efficient than the x86 CPU for some core counts and molecular sizes.

[1]  Rahul Khanna,et al.  RAPL: Memory power estimation and capping , 2010, 2010 ACM/IEEE International Symposium on Low-Power Electronics and Design (ISLPED).

[2]  M. Plesset,et al.  Note on an Approximation Treatment for Many-Electron Systems , 1934 .

[3]  Yuefan Deng,et al.  New trends in high performance computing , 2001, Parallel Computing.

[4]  Mark S. Gordon,et al.  The Distributed Data Interface in GAMESS , 2000 .

[5]  T. H. Dunning.  Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen , 1989 .

[6]  R.H. Dennard,et al.  Design Of Ion-implanted MOSFET's with Very Small Physical Dimensions , 1974, Proceedings of the IEEE.

[7]  Thom H. Dunning,et al.  Gaussian basis sets for use in correlated molecular calculations. V. Core-valence basis sets for boron through neon , 1995 .

[8]  J. Pople,et al.  Self‐Consistent Molecular‐Orbital Methods. IX. An Extended Gaussian‐Type Basis for Molecular‐Orbital Studies of Organic Molecules , 1971 .

[9]  R. Parr.  Density-functional theory of atoms and molecules , 1989 .

[10]  W. Kohn,et al.  Self-Consistent Equations Including Exchange and Correlation Effects , 1965 .

[11]  R. Bartlett.  Many-Body Perturbation Theory and Coupled Cluster Theory for Electron Correlation in Molecules , 1981 .

[12]  Mark A. Ratner,et al.  6‐31G* basis set for third‐row atoms , 2001, J. Comput. Chem..

[13]  Mark Bohr,et al.  A 30 Year Retrospective on Dennard's MOSFET Scaling Paper , 2007, IEEE Solid-State Circuits Newsletter.

[14]  P. Wormer,et al.  Theory and Applications of Computational Chemistry The First Forty Years , 2005 .

[15]  Carl Staelin,et al.  lmbench: Portable Tools for Performance Analysis , 1996, USENIX Annual Technical Conference.

[16]  Burke,et al.  Generalized Gradient Approximation Made Simple. , 1996, Physical review letters.

[17]  V. Barone,et al.  Toward reliable density functional methods without adjustable parameters: The PBE0 model , 1999 .

[18]  A.P. Rendell,et al.  Enabling the Efficient Use of SMP Clusters: The GAMESS/DDI Model , 2003, ACM/IEEE SC 2003 Conference (SC'03).

[19]  K. Burke,et al.  Generalized Gradient Approximation Made Simple [Phys. Rev. Lett. 77, 3865 (1996)] , 1997 .

[20]  J. Pople,et al.  Self—Consistent Molecular Orbital Methods. XII. Further Extensions of Gaussian—Type Basis Sets for Use in Molecular Orbital Studies of Organic Molecules , 1972 .

[21]  P. Hohenberg,et al.  Inhomogeneous Electron Gas , 1964 .

[22]  A. Szabó,et al.  Modern quantum chemistry : introduction to advanced electronic structure theory , 1982 .

[23]  Mark S. Gordon,et al.  A new hierarchical parallelization scheme: Generalized distributed data interface (GDDI), and an application to the fragment molecular orbital method (FMO) , 2004, J. Comput. Chem..