Quentin F. Stout

Space Weather Modeling Framework: A new tool for the space science community

[1] The Space Weather Modeling Framework (SWMF) provides a high-performance flexible framework for physics-based space weather simulations, as well as for various space physics applications. The SWMF integrates numerical models of the Solar Corona, Eruptive Event Generator, Inner Heliosphere, Solar Energetic Particles, Global Magnetosphere, Inner Magnetosphere, Radiation Belt, Ionosphere Electrodynamics, and Upper Atmosphere into a high-performance coupled model. The components can be represented with alternative physics models, and any physically meaningful subset of the components can be used. The components are coupled to the control module via standardized interfaces, and an efficient parallel coupling toolkit is used for the pairwise coupling of the components. The execution and parallel layout of the components is controlled by the SWMF. Both sequential and concurrent execution models are supported. The SWMF enables simulations that were not possible with the individual physics models. Using reasonably high spatial and temporal resolutions in all of the coupled components, the SWMF runs significantly faster than real time on massively parallel supercomputers. This paper presents the design and implementation of the SWMF and some demonstrative tests. Future papers will describe validation (comparison of model results with measurements) and applications to challenging space weather events. The SWMF is publicly available to the scientific community for doing geophysical research. We also intend to expand the SWMF in collaboration with other model developers.

Adaptive numerical algorithms in space weather modeling

Space weather describes the various processes in the Sun-Earth system that present danger to human health and technology. The goal of space weather forecasting is to provide an opportunity to mitigate these negative effects. Physics-based space weather modeling is characterized by disparate temporal and spatial scales as well as by different relevant physics in different domains. A multi-physics system can be modeled by a software framework comprising several components. Each component corresponds to a physics domain, and each component is represented by one or more numerical models. The publicly available Space Weather Modeling Framework (SWMF) can execute and couple together several components distributed over a parallel machine in a flexible and efficient manner. The framework also allows resolving disparate spatial and temporal scales with independent spatial and temporal discretizations in the various models. Several of the computationally most expensive domains of the framework are modeled by the Block-Adaptive Tree Solarwind Roe-type Upwind Scheme (BATS-R-US) code that can solve various forms of the magnetohydrodynamic (MHD) equations, including Hall, semi-relativistic, multi-species and multi-fluid MHD, anisotropic pressure, radiative transport and heat conduction. Modeling disparate scales within BATS-R-US is achieved by a block-adaptive mesh both in Cartesian and generalized coordinates. Most recently we have created a new core for BATS-R-US: the Block-Adaptive Tree Library (BATL) that provides a general toolkit for creating, load balancing and message passing in a 1, 2 or 3 dimensional block-adaptive grid. We describe the algorithms of BATL and demonstrate its efficiency and scaling properties for various problems. BATS-R-US uses several time-integration schemes to address multiple time-scales: explicit time stepping with fixed or local time steps, partially steady-state evolution, point-implicit, semi-implicit, explicit/implicit, and fully implicit numerical schemes. Depending on the application, we find that different time stepping methods are optimal. Several of the time integration schemes exploit the block-based granularity of the grid structure. The framework and the adaptive algorithms enable physics-based space weather modeling and even short-term forecasting.

Parallel computations on reconfigurable meshes

The mesh with reconfigurable bus is presented as a model of computation. The reconfigurable mesh captures salient features from a variety of sources, including the CAAPP, CHiP, polymorphic-torus network, and bus automation. It consists of an array of processors interconnected by a reconfigurable bus system that can be used to dynamically obtain various interconnection patterns between the processors. A variety of fundamental data-movement operations for the reconfigurable mesh are introduced. Based on these operations, algorithms that are efficient for solving a variety of problems involving graphs and digitized images are also introduced. The algorithms are asymptotically superior to those previously obtained for the aforementioned reconfigurable architectures, as well as to those previously obtained for the mesh, the mesh with multiple broadcasting, the mesh with multiple buses, the mesh-of-trees, and the pyramid computer. The power of reconfigurability is illustrated by solving some problems, such as the exclusive OR, more efficiently on the reconfigurable mesh than is possible on the programmable random-access memory (PRAM). >

A Microprocessor-based Hypercube Supercomputer

Each node in the NCUBE/ten parallel processor is organized around a custom, VAX-like, 32-bit CPU chip. With 1024 nodes, the NCUBE/ten provides a throughput of 500 MELOPS.

Data movement techniques for the pyramid computer

The pyramid computer was initially proposed for performing high-speed low-level image processing. However, its regular geometry can be adapted naturally to many other problems, providing effective solutions to problems more complex than those previously considered. We illustrate this by presenting pyramid computer solutions to problems involving component labeling, minimal spanning forests, nearest neighbors, transitive closure, articulation points, bridge edges, etc. Central to these algorithms is our collection of data movement techniques which exploit the pyramid’s mix of tree and mesh connections. Our pyramid algorithms are significantly faster than their mesh-connected computer counterparts. For example, given a black/white square picture with n pixels, we can label the connected components in

Mesh computer algorithms for computational geometry

\theta (n^{{1 / 4}} )

Efficient parallel convex hull algorithms

time, as compared with the

Geometric Algorithms for Digitized Pictures on a Mesh-Connected Computer

\Omega (n^{{1 / 2}} )

Intensive hypercube communication. Prearranged communication in link-bound machines

time required on the mesh-connected computer.

An adaptive MHD method for global space weather simulations

Asymptotically optimal parallel algorithms are presented for use on a mesh computer to determine several fundamental geometric properties of figures. For example, given multiple figures represented by the Cartesian coordinates of n or fewer planar vertices, distributed one point per processor on a two-dimensional mesh computer with n simple processing elements, Theta (n/sup 1/2/>or=-time algorithms are given for identifying the convex hull and smallest enclosing box of each figure. Given two such figures, a Theta (n/sup 1/2/>or=-time algorithm is given to decide if the two figures are linearly separable. Given n or fewer planar points, Theta (n/sup 1/2/>or=-time algorithms are given to solve the all-nearest neighbor problems for points and for sets of points. Given n or fewer circles, convex figures, hyperplanes, simple polygons, orthogonal polygons, or iso-oriented rectangles, Theta (n/sup 1/2/>or=-time algorithms are given to solve a variety of area and intersection problems. Since any serial computer has worst-case time of Omega (n) when processing n points, these algorithms show that the mesh computer provides significantly better solutions to these problems. >