SCELib3.0: The new revision of SCELib, the parallel computational library of molecular properties in the Single Center Approach

Abstract

SCELib is a computer program which implements the Single Center Expansion (SCE) method to describe molecular electronic densities and the interaction potentials between a charged projectile (electron or positron) and a target molecular system. The first version (CPC Catalog identifier ADMG_v1_0) was submitted to the CPC Program Library in 2000, and version 2.0 (ADMG_v2_0) was submitted in 2004. We here announce the new release 3.0 which presents additional features with respect to the previous versions aiming at a significative enhance of its capabilities to deal with larger molecular systems. SCELib 3.0 allows for ab initio effective core potential (ECP) calculations of the molecular wavefunctions to be used in the SCE method in addition to the standard all-electron description of the molecule. The list of supported architectures has been updated and the code has been ported to platforms based on accelerating coprocessors, such as the NVIDIA GPGPU and the new parallel model adopted is able to efficiently run on a mixed many-core computing system. Program summary Program title: SCELib3.0 Catalogue identifier: ADMG_v3_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADMG_v3_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 2 018 862 No. of bytes in distributed program, including test data, etc.: 4 955 014 Distribution format: tar.gz Programming language: C Compilers used: xlc V8.x, Intel C V10.x, Portland Group V7.x, nvcc V2.x Computer: All SMP platforms based on AIX, Linux and SUNOS operating systems over SPARC, POWER, Intel Itanium2, X86, em64t and Opteron processors Operating system: SUNOS, IBM AIX, Linux RedHat (Enterprise), Linux SuSE (SLES) Has the code been vectorized or parallelized?: Yes. 1 to 32 (CPU or GPU) used RAM: Up to 32 GB depending on the molecular system and runtime parameters Classification: 16.5 Catalogue identifier of previous version: ADMG_v2_0 Journal reference of previous version: Comput. Phys. Comm. 162 (2004) 51 External routines: CUDA libraries (SDK V2.x). Does the new version supersede the previous version?: Yes Nature of problem: In this set of codes an efficient procedure is implemented to describe the wavefunction and related molecular properties of a polyatomic molecular system within the Single Center of Expansion (SCE) approximation. The resulting SCE wavefunction, electron density, electrostatic and correlation/polarization potentials can then be used in a wide variety of applications, such as electron–molecule scattering calculations, quantum chemistry studies, biomodelling and drug design. Solution method: The polycentre Hartree–Fock solution for a molecule of arbitrary geometry, based on linear combination of Gaussian-Type Orbital (GTO), is expanded over a single center, typically the Center Of Mass (C.O.M.), by means of a Gauss Legendre/Chebyschev quadrature over the θ , φ angular coordinates. The resulting SCE numerical wavefunction is then used to calculate the one-particle electron density, the electrostatic potential and two different models for the correlation/polarization potentials induced by the impinging electron, which have the correct asymptotic behavior for the leading dipole molecular polarizabilities. Reasons for new version: The present release of SCELib allows the study of larger molecular systems with respect to the previous versions by means of theoretical and technological advances, with the first implementation of the code over a many-core computing system. Summary of revisions: The major features added with respect to SCELib Version 2.0 are 1. molecular wavefunctions obtained via the Los Alamos (Hay and Wadt) LAN ECP plus DZ description of the inner-shell electrons (on Na–La, Hf–Bi elements) [1] can now be single-center-expanded; the addition required modifications of: (i) the filtering code readgau, (ii) the main reading function setinp, (iii) the sphint code (including changes to the CalcMO code), (iv) the densty code, (v) the vst code; 2. the classes of platforms supported now include two more architectures based on accelerated coprocessors (Nvidia GSeries GPGPU and ClearSpeed e720 (ClearSpeed version, experimental; initial preliminary porting of the sphint() function not for production runs – see the code documentation for additional detail). A single-precision representation for real numbers in the SCE mapping of the GTOs ( sphint code), has been implemented into the new code; 3. the I h symmetry point group for the molecular systems has been added to those already allowed in the SCE procedure; 4. the orientation of the molecular axis system for the Cs (planar) symmetry has been changed in accord with the standard orientation adopted by the latest version of the quantum chemistry code (Gaussian C03 [2]), which is used to generate the input multi-centre molecular wavefunctions ( z-axis perpendicular to the symmetry plane); 5. the abelian subgroup for the Cs point group has been changed from C 1 to Cs; 6. atomic basis functions including g-type GTOs can now be single-center-expanded. Restrictions: Depending on the molecular system under study and on the operating conditions the program may or may not fit into available RAM memory. In this case a feature of the program is to memory map a disk file in order to efficiently access the memory data through a disk device. The parallel GP-GPU implementation limits the number of CPU threads to the number of GPU cores present. Running time: The execution time strongly depends on the molecular target description and on the hardware/OS chosen, it is directly proportional to the ( r , θ , φ ) grid size and to the number of angular basis functions used. Thus, from the program printout of the main arrays memory occupancy, the user can approximately derive the expected computer time needed for a given calculation executed in serial mode. For parallel executions the overall efficiency must be further taken into account, and this depends on the no. of processors used as well as on the parallel architecture chosen, so a simple general law is at present not determinable.