Toward high‐performance computational chemistry: II. A scalable self‐consistent field program (original) (raw)
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An account is given of experience gained in implementing computational chemistry application software, including quantum chemistry and macromolecular refinement codes, on distributed memory parallel processors. In quantum chemistry we consider the coarse-grained implementation of Gaussian integral and derivative integral evaluation, the direct-SCF computation of an uncorrelated wavefunction,~the 4-index transformation of two-electron integrals and the direct-CI calculation of correlated wavefunctions. In the refinement of macromolecular conformations, we describe domain decomposition techniques used in implementing general purpose molecular mechanics, molecular dynamics and free energy perturbation calculations. Attention is focused on performance figures obtained on the Intel iPSC/2 and iPSC/860 hypercubes, which are compared with those obtained on a Cray Y-MP/464 and Convex C-220 minisupercomputer. From this data we deduce the cost effectiveness of parallel processors in the field of computational chemistry.
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1990
The algorithms employed are computationally intensive and, as a result, increased performance (both algorithmic and architectural) is required to improve accuracy and to treat larger molecular systems. Several benchmark quantum chemistry codes are examined on a variety of architectures. While these codes are only a small portion of a typical quantum chemistry library, they illustrate many of the computationally intensive kernels and data manipulation requirements of some applications. Furthermore, understanding the performance of the existing algorithm on present and proposed supercomputers serves as a guide for future programs and algorithm development. The algorithms investigated are: (1) a sparse symmetric matrix vector product; (2) a four index integral transformation; and (3) the calculation of diatomic two electron Slater integrals. The vectorization strategies are examined for these algorithms for both the Cyber 205 and Cray XMP. In addition, multiprocessor implementations of...
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The goal of our project is the development of a program synthesis system to facilitate the development of high-performance parallel programs for a class of computations encountered in computational chemistry and computational physics. These computations are expressible as a set of tensor contractions and arise in electronic structure calculations. This paper provides an overview of a planned synthesis system that will take as input a high-level specification of the computation and generate high-performance parallel code for a number of target architectures. We focus on an approach to performing data locality optimization in this context. Preliminary experimental results on an SGI Origin 2000 are encouraging and demonstrate that the approach is effective.
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Density function theory (DFT) is the most widely employed electronic structure method due to its favorable scaling with system size and accuracy for a broad range of molecular and condensed-phase systems. The advent of massively parallel supercomputers has enhanced the scientific community's ability to study larger system sizes. Ground-state DFT calculations on ∼10 3 valence electrons using traditional O(N 3 ) algorithms can be routinely performed on present-day supercomputers. The performance characteristics of these massively parallel DFT codes on >10 4 computer cores are not well understood. The GPAW code was ported an optimized for the Blue Gene/P architecture. We present our algorithmic parallelization strategy and interpret the results for a number of benchmark test cases.
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The Journal of Chemical Physics, 2004
Linear scaling quantum chemical methods for Density Functional Theory are extended to the condensed phase at the Γ-point. For the two-electron Coulomb matrix, this is achieved with a tree-code algorithm for fast Coulomb summation [J. Chem. Phys. 106, 5526 (1997)], together with multipole representation of the crystal field [J. Chem. Phys. 107, 10131 (1997)]. A periodic version of the hierarchical cubature algorithm [J. Chem. Phys. 113, 10037 (2000)], which builds a telescoping adaptive grid for numerical integration of the exchange-correlation matrix, is shown to be efficient when the problem is posed as integration over the unit cell. Commonalities between the Coulomb and exchange-correlation algorithms are discussed, with an emphasis on achieving linear scaling through the use of modern data structures. With these developments, convergence of the Γ-point supercell approximation to the k-space integration limit is demonstrated for MgO and NaCl. Linear scaling construction of the Fockian and control of error is demonstrated for RBLYP/6-21G* diamond up to 512 atoms.