Cyber-Enabled Scientific DiscoveryChan, Tony; Jameson, Leland
doi: 10.1088/1742-6596/78/1/011003pmid: N/A
It is often said that numerical simulation is third in the group of three ways to explore modern science: theory, experiment and simulation. Carefully executed modern numerical simulations can, however, be considered at least as relevant as experiment and theory. In comparison to physical experimentation, with numerical simulation one has the numerically simulated values of every field variable at every grid point in space and time. In comparison to theory, with numerical simulation one can explore sets of very complex non-linear equations such as the Einstein equations that are very difficult to investigate theoretically. Cyber-enabled scientific discovery is not just about numerical simulation but about every possible issue related to scientific discovery by utilizing cyberinfrastructure such as the analysis and storage of large data sets, the creation of tools that can be used by broad classes of researchers and, above all, the education and training of a cyber-literate workforce.
Performance of particle in cell methods on highly concurrent computational architecturesAdams, M F; Ethier, S; Wichmann, N
doi: 10.1088/1742-6596/78/1/012001pmid: N/A
Particle in cell (PIC) methods are effective in computing Vlasov-Poisson system of equations used in simulations of magnetic fusion plasmas. PIC methods use grid based computations, for solving Poisson's equation or more generally Maxwell's equations, as well as Monte-Carlo type methods to sample the Vlasov equation. The presence of two types of discretizations, deterministic field solves and Monte-Carlo methods for the Vlasov equation, pose challenges in understanding and optimizing performance on today large scale computers which require high levels of concurrency. These challenges arises from the need to optimize two very different types of processes and the interactions between them. Modern cache based high-end computers have very deep memory hierarchies and high degrees of concurrency which must be utilized effectively to achieve good performance. The effective use of these machines requires maximizing concurrency by eliminating serial or redundant work and minimizing global communication. A related issue is minimizing the memory traffic between levels of the memory hierarchy because performance is often limited by the bandwidths and latencies of the memory system. This paper discusses some of the performance issues, particularly in regard to parallelism, of PIC methods. The gyrokinetic toroidal code (GTC) is used for these studies and a new radial grid decomposition is presented and evaluated. Scaling of the code is demonstrated on ITER sized plasmas with up to 16K Cray XT3/4 cores.
Petascale visual data analysis in a production computing environmentAhern, Sean
doi: 10.1088/1742-6596/78/1/012002pmid: N/A
Supporting the visualization and analysis needs of the users of the Department of Energy's premiere high-performance computing centers requires a careful engineering of software and hardware system architectures to provide maximum capability and algorithmic breadth. Data set growth follows an inverse power law that has implications for the platforms that are deployed for analysis and visualization; central storage and coupled analysis platforms are critical for petascale post-production. Software architectures like VisIt which exploit parallel platforms, as well as provide remote capability, extensibility, and optimization are fruitful ground for delivering new analysis capabilities for petascale applications. Finally, direct interaction with customers is key to deploying successful results.
Numerical simulation of low Mach number reacting flowsBell, J B; Aspden, A J; Day, M S; Lijewski, M J
doi: 10.1088/1742-6596/78/1/012004pmid: N/A
Using examples from active research areas in combustion and astrophysics, we demonstrate a computationally efficient numerical approach for simulating multiscale low Mach number reacting flows. The method enables simulations that incorporate an unprecedented range of temporal and spatial scales, while at the same time, allows an extremely high degree of reaction fidelity. Sample applications demonstrate the efficiency of the approach with respect to a traditional time-explicit integration method, and the utility of the methodology for studying the interaction of turbulence with terrestrial and astrophysical flame structures.
Building a universal nuclear energy density functionalBertsch, G F
doi: 10.1088/1742-6596/78/1/012005pmid: N/A
This talk describes a new project in SciDAC II in the area of low-energy nuclear physics. The motivation and goals of the SciDAC are presented as well as an outline of the theoretical and computational methodology that will be employed. An important motivation is to have more accurate and reliable predictions of nuclear properties including their binding energies and low-energy reaction rates. The theoretical basis is provided by density functional theory, which the only available theory that can be systematically applied to all nuclei. However, other methodologies based on wave function methods are needed to refine the functionals and to make applications to dynamic processes.
Topological feature extraction and trackingBremer, P-T; Bringa, E M; Duchaineau, M A; Gyulassy, A G; Laney, D; Mascarenhas, A; Pascucci, V
doi: 10.1088/1742-6596/78/1/012007pmid: N/A
Scientific datasets obtained by measurement or produced by computational simulations must be analyzed to understand the phenomenon under study. The analysis typically requires a mathematically sound definition of the features of interest and robust algorithms to identify these features, compute statistics about them, and often track them over time. Because scientific datasets often capture phenomena with multi-scale behaviour, and almost always contain noise the definitions and algorithms must be designed with sufficient flexibility and care to allow multi-scale analysis and noise-removal. In this paper, we present some recent work on topological feature extraction and tracking with applications in molecular analysis, combustion simulation, and structural analysis of porous materials.
Plasma microturbulence simulation of instabilities at highly disparate scalesCandy, J; Waltz, R E; Fahey, M R; Holland, C
doi: 10.1088/1742-6596/78/1/012008pmid: N/A
This work reports on studies of the multi-scale interaction between small-scale electron turbulence and large-scale ion turbulence in tokamak plasmas. Traditionally, the long-wavelength, low-frequency turbulence driven by ion-scale instabilities (ion-temperature-gradient and trapped-electron modes) is studied separately from the short-wavelength, high-frequency turbulence driven by electron-scale instabilities (electron-temperature-gradient modes). High-resolution, massively-parallel simulations have uncovered a number of physically-important discoveries. First, we find that a popular simplified model of ion physics previously used in studies of electron-scale turbulence can lead to nonphysical runaway of electron heat transport. We have shown that this nonphysical runaway is eliminated when correct long-wavelength ion physics is self-consistently included. We have also found that under normal conditions most of the electron heat transport arises from large-scale instabilities. However, when these large-scale instabilities are suppressed by plasma rotation or other processes, the electron instabilities survive and may dominate the loss of electron heat from the plasma. A further remarkable finding is that strong turbulence at long scales can act to reduce the intensity of turbulence at short scales. Simulations were carried out on the Cray X1E computer at ORNL, with the largest runs taking about a week on 720 multi-streaming processors.