Nicholas J. Wright

Direct calculation of anharmonic vibrational states of polyatomic molecules using potential energy surfaces calculated from density functional theory

Potential energy surface points computed from variants of density functional theory (DFT) are used to calculate directly the anharmonic vibrational frequencies of H2O, Cl−H2O, and (H2O)2. The method is an adaptation to DFT of a recent algorithm for direct calculations of anharmonic vibrational frequencies using ab initio electronic structure codes. The DFT calculations are performed using the BLYP and the B3LYP functionals and the results are compared with experiment, and also with those calculated directly from a potential energy surface obtained using ab initio Moller-Plesset second–order perturbation theory (MP2). The direct calculation of the vibrational states from the potential energy points is performed using the correlation-corrected vibrational self-consistent field (CC-VSCF) method. This method includes anharmonicity and correlations between different vibrational modes. The accuracy of this method is examined and it is shown that for the experimentally measured transitions the errors in the CC-V...

Forward-backward semiclassical dynamics for condensed phase time correlation functions

The forward–backward semiclassical dynamics (FBSD) scheme for obtaining time correlation functions shows much promise as a method for including quantum mechanical effects into the calculation of dynamical properties of condensed phase systems. By combining this scheme with a discretized path integral representation of the Boltzmann operator one is able to calculate correlation functions at finite temperature. In this work we develop constant temperature molecular dynamics techniques for sampling the phase space and path integral variables. The resulting methodology is applied to the calculation of the velocity autocorrelation function of liquid argon. At the chosen state point the FBSD results are in good agreement with classical trajectory predictions, but the existence of a non-negligible imaginary part of the correlation function illustrates the importance of proper density quantization even under nearly classical conditions.

WRF nature run

The Weather Research and Forecast (WRF) model is a limited-area model of the atmosphere for mesoscale research and operational numerical weather prediction (NWP). A petascale problem is a WRF nature run that provides very high-resolution truth against which more coarse simulations or perturbation runs may be compared for purposes of studying predictability, stochastic parameterization, and fundamental dynamics. We carried out a nature run involving an idealized high resolution rotating fluid on the hemisphere to investigate scales that span the k-3 to k-5/3 kinetic energy spectral transition of the observed atmosphere using 65,536 processors of the BG/L machine at LLNL. We worked through issues of parallel I/O and scalability. The primary result is not just the scalability and high Tflops number, but an important step towards understanding weather predictability at high resolution.

Forward-backward semiclassical simulation of dynamical processes in liquids

Forward-backward semiclassical dynamics (FBSD) provides a practical methodology for including quantum mechanical effects in classical trajectory simulations of polyatomic systems. FBSD expressions for time-dependent expectation values or correlation functions take the form of phase space integrals with respect to trajectory initial conditions, weighted by the coherent state transform of a corrected density operator. Quantization through a discretized path integral representation of the Boltzmann operator ensures a proper treatment of zero point energy effects and of imaginary components in finite-temperature correlation functions, and extension to systems obeying Bose statistics is possible. Accelerated convergence is achieved via Monte Carlo or molecular dynamics sampling techniques and through the construction of improved imaginary time propagators. The accuracy of the methodology is demonstrated on several model systems, including models of Bose and Fermi particles. Applications to liquid argon, neon and para-hydrogen are presented.

Modeling and predicting application performance on parallel computers using HPC challenge benchmarks

A method is presented for modeling application performance on parallel computers in terms of the performance of microkernels from the HPC Challenge benchmarks. Specifically, the application run time is expressed as a linear combination of inverse speeds and latencies from microkernels or system characteristics. The model parameters are obtained by an automated series of least squares fits using backward elimination to ensure statistical significance. If necessary, outliers are deleted to ensure that the final fit is robust. Typically three or four terms appear in each model: at most one each for floating-point speed, memory bandwidth, interconnect bandwidth, and interconnect latency. Such models allow prediction of application performance on future computers from easier-to-make predictions of microkernel performance. The method was used to build models for four benchmark problems involving the PARATEC and MILC scientific applications. These models not only describe performance well on the ten computers used to build the models, but also do a good job of predicting performance on three additional computers with newer design features. For the four application benchmark problems with six predictions each, the relative root mean squared error in the predicted run times varies between 13 and 16%. The method was also used to build models for the HPL and G-FFTE benchmarks in HPCC, including functional dependences on problem size and core count from complexity analysis. The model for HPL predicts performance even better than the application models do, while the model for G-FFTE systematically underpredicts run times.

Extending the vibrational self-consistent method: Using a partially separable wave function for calculating anharmonic vibrational states of polyatomic molecules

A new method for the treatment of correlation effects between modes in vibrational self-consistent-field (VSCF) calculations is introduced. It is based upon using a partially separable form for the wave function. As a result, some of the modes are treated as mutually fully correlated, while the rest are separable. The modes which are explicitly coupled together in the calculation are chosen on physical grounds. Trial calculations are performed upon H2O, H3O+, and CH3NH2 and indicate that the method performs well. The agreement with experiment for the explicitly coupled modes is improved when compared to both the vibrational self-consistent-field method and its correlation-corrected extension. When interfaced with an electronic structure code this method opens the way for the accurate first-principles prediction of vibrational frequencies of strongly coupled modes. If only a few modes are mutually strongly coupled, the method has a very favorable scaling with system size, as does VSCF itself.

Measuring and Understanding Variation in Benchmark Performance

Runtime irreproducibility complicates application performance evaluation on today’s high performance computers. Performance can vary significantly between seemingly identical runs; this presents a challenge to benchmarking as well as a user, who is trying to determine whether the change they made to their code is an actual improvement. In order to gain a better understanding of this phenomenon, we measure the runtime variation of two applications, PARAllel Total Energy Code (PARATEC) and Weather Research and Forecasting (WRF), on three different machines. Key associated metrics are also recorded. The data is then used to 1) quantify the magnitude and distribution of the variations and 2) gain an understanding as why the variations occur. Using our lightweight framework, Integrated Performance Monitoring (IPM), to understand the performance characteristics of individual runs, and the Inca framework to automate the procedure measurements were collected over a month’s time. The results indicate that performance can vary up to 25% and is almost always due to contention for network resources. We also found that the variation differs between machines and is almost always greater on machines with lower performing networks.

Modeling the Office of Science ten year facilities plan: The PERI Architecture Tiger Team

The Performance Engineering Institute (PERI) originally proposed a tiger team activity as a mechanism to target significant effort optimizing key Office of Science applications, a model that was successfully realized with the assistance of two JOULE metric teams. However, the Office of Science requested a new focus beginning in 2008: assistance in forming its ten year facilities plan. To meet this request, PERI formed the Architecture Tiger Team, which is modeling the performance of key science applications on future architectures, with S3D, FLASH and GTC chosen as the first application targets. In this activity, we have measured the performance of these applications on current systems in order to understand their baseline performance and to ensure that our modeling activity focuses on the right versions and inputs of the applications. We have applied a variety of modeling techniques to anticipate the performance of these applications on a range of anticipated systems. While our initial findings predict that Office of Science applications will continue to perform well on future machines from major hardware vendors, we have also encountered several areas in which we must extend our modeling techniques in order to fulfill our mission accurately and completely. In addition, we anticipate that models of a wider range of applications will reveal critical differences between expected future systems, thus providing guidance for future Office of Science procurement decisions, and will enable DOE applications to exploit machines in future facilities fully.