Finding regions of interest on toroidal meshes

Fusion promises to provide clean and safe energy, and a considerable amount of research effort is underway to turn this aspiration intoreality. This work focuses on a building block for analyzing data produced from the simulation of microturbulence in magnetic confinementfusion devices: the task of efficiently extracting regions of interest. Like many other simulations where a large amount of data are produced,the careful study of ``interesting'' parts of the data is critical to gain understanding. In this paper, we present an efficient approach forfinding these regions of interest. Our approach takes full advantage of the underlying mesh structure in magnetic coordinates to produce acompact representation of the mesh points inside the regions and an efficient connected component labeling algorithm for constructingregions from points. This approach scales linearly with the surface area of the regions of interest instead of the volume as shown with bothcomputational complexity analysis and experimental measurements. Furthermore, this new approach is 100s of times faster than a recentlypublished method based on Cartesian coordinates

Petascale Parallelization of the Gyrokinetic Toroidal Code

The Gyrokinetic Toroidal Code (GTC) is a global, three-dimensional particle-in-cell application developed to study microturbulence in tokamak fusion devices. The global capability of GTC is unique, allowing researchers to systematically analyze important dynamics such as turbulence spreading. In this work we examine a new radial domain decomposition approach to allow scalability onto the latest generation of petascale systems. Extensive performance evaluation is conducted on three high performance computing systems: the IBM BG/P, the Cray XT4, and an Intel Xeon Cluster. Overall results show that the radial decomposition approach dramatically increases scalability, while reducing the memory footprint - allowing for fusion device simulations at an unprecedented scale. After a decade where high-end computing (HEC) was dominated by the rapid pace of improvements to processor frequencies, the performance of next-generation supercomputers is increasingly differentiated by varying interconnect designs and levels of integration. Understanding the tradeoffs of these system designs is a key step towards making effective petascale computing a reality. In this work, we examine a new parallelization scheme for the Gyrokinetic Toroidal Code (GTC) [?] micro-turbulence fusion application. Extensive scalability results and analysis are presented on three HEC systems: the IBM BlueGene/P (BG/P) at Argonne National Laboratory, the Cray XT4 at Lawrence Berkeley National Laboratory, and an Intel Xeon cluster at Lawrence Livermore National Laboratory. Overall results indicate that the new radial decomposition approach successfully attains unprecedented scalability to 131,072 BG/P cores by overcoming the memory limitations of the previous approach. The new version is well suited to utilize emerging petascale resources to access new regimes of physical phenomena

Scientific Application Performance on Leading Scalar and Vector Supercomputing Platforms

The last decade has witnessed a rapid proliferation of superscalar cache-based microprocessors to build high-end computing (HEC) platforms, primarily because of their generality, scalability, and cost effectiveness. However, the growing gap between sustained and peak performance for full-scale scientific applications on conventional supercomputers has become a major concern in high performance computing, requiring significantly larger systems and application scalability than implied by peak performance in order to achieve desired performance. The latest generation of custom-built parallel vector systems have the potential to address this issue for numerical algorithms with sufficient regularity in their computational structure. In this work we explore applications drawn from four areas: magnetic fusion (GTC), plasma physics (LBMHD3D), astrophysics (Cactus), and material science (PARATEC). We compare performance of the vector-based Cray X1, X1E, Earth Simulator, NEC SX-8, with performance of three leading commodity-based superscalar platforms utilizing the IBM Power3, Intel Itanium2, and AMD Opteron processors. Our work makes several significant contributions: a new data-decomposition scheme for GTC that (for the first time) enables a breakthrough of the Teraflop barrier; the introduction of a new three-dimensional Lattice Boltzmann magneto-hydrodynamic implementation used to study the onset evolution of plasma turbulence that achieves over 26Tflop/s on 4800 ES processors; the highest per processor performance (by far) achieved by the full-production version of the Cactus ADM-BSSN; and the largest PARATEC cell size atomistic simulation to date. Overall, results show that the vector architectures attain unprecedented aggregate performance across our application suite, demonstrating the tremendous potential of modern parallel vector systems

An integrated visual exploration approach to particle data analysis

Abstract—Particle simulations are powerful tools for understanding the complex phenomena associated with many areas of physics research, including confined plasma and high energy particle beams. The simulations are conducted on massively parallel computers, and the results provide minimal deviation from kinetic equations. Analyzing the data, however, presents a challenge due to the large quantity of particles, variables, and time steps. In this paper we describe a data exploration system that visualizes time-varying, multivariate point-based data from gyrokinetic particle simulations. By utilizing two modes of interaction–physical space and variable space–the system allows scientists to explore collections of densely packed particles and discover interesting features within the data. While single variables can be easily explored through the use of a one dimensional transfer function, we turn to the information visualization approach of parallel coordinates for interactively selecting particles in multivariate space. In this manner, particles with deeper connections can be separated from the rest of the data and then rendered using sphere glyphs and pathlines. From the results of this system, scientists at Princeton Plasma Physics Laboratory have been able to easily identify features of interest, such as the location and motion of particles that become trapped in turbulent plasma flow. The combination of scientific and information visualization techniques provides an easy way to analyze complex collections of particles. Index Terms—Particle visualization, multivariate visualization, user interfaces, parallel coordinates.