ROOT - A C++ Framework for Petabyte Data Storage, Statistical Analysis and Visualization

ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, designed for storing and analyzing petabytes of data in an efficient way. Any instance of a C++ class can be stored into a ROOT file in a machine-independent compressed binary format. In ROOT the TTree object container is optimized for statistical data analysis over very large data sets by using vertical data storage techniques. These containers can span a large number of files on local disks, the web, or a number of different shared file systems. In order to analyze this data, the user can chose out of a wide set of mathematical and statistical functions, including linear algebra classes, numerical algorithms such as integration and minimization, and various methods for performing regression analysis (fitting). In particular, ROOT offers packages for complex data modeling and fitting, as well as multivariate classification based on machine learning techniques. A central piece in these analysis tools are the histogram classes which provide binning of one- and multi-dimensional data. Results can be saved in high-quality graphical formats like Postscript and PDF or in bitmap formats like JPG or GIF. The result can also be stored into ROOT macros that allow a full recreation and rework of the graphics. Users typically create their analysis macros step by step, making use of the interactive C++ interpreter CINT, while running over small data samples. Once the development is finished, they can run these macros at full compiled speed over large data sets, using on-the-fly compilation, or by creating a stand-alone batch program. Finally, if processing farms are available, the user can reduce the execution time of intrinsically parallel tasks - e.g. data mining in HEP - by using PROOF, which will take care of optimally distributing the work over the available resources in a transparent way

Surface-based flow visualization

This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/computers-and-graphics/.With increasing computing power, it is possible to process more complex fluid simulations. However, a gap between increasing\ud data size and our ability to visualize them still remains. Despite the great amount of progress that has been made in the field of\ud flow visualization over the last two decades, a number of challenges remain. Whilst the visualization of 2D flow has many good\ud solutions, the visualization of 3D flow still poses many problems. Challenges such as domain coverage, speed of computation, and\ud perception remain key directions for further research. Flow visualization with a focus on surface-based techniques forms the basis\ud of this literature survey, including surface construction techniques and visualization methods applied to surfaces. We detail our\ud investigation into these algorithms with discussions of their applicability and their relative strengths and drawbacks. We review the\ud most important challenges when considering such visualizations. The result is an up-to-date overview of the current state-of-the-art\ud that highlights both solved and unsolved problems in this rapidly evolving branch of research

Interactive interrogation of computational mixing data in a virtual environment

Mixing processes are essential in the chemical process industries, including food processors, consumer products corporations, and pharmaceutical manufacturers. The increased use of computational fluid dynamics (CFD) during the design and analysis of static and stirred mixers has provided increased insight into mixing processes. However, the velocities, temperatures, and pressures are insufficient to completely quantify a mixing process. A more complete understanding of mixing processes is given by the material spatial distribution of massless particles as they move through the flow field. This research seeks to combine surround-screen virtual reality and particle tracing of massless particles into an interactive virtual environment to explore the benefits these tools bring to engineers seeking to understand the behavior of fluids in mixing processes. Surround-screen virtual reality (VR) provides a means to immerse users into the mixing data where they can collaboratively investigate the flow features as displayed on a large scale stereo-projection system. This work integrates the particle tracing computation power of the HyperTrace[Superscript TM] commercial software application with new data interrogation techniques made possible by the use of virtual reality technology. Parallel processing to facilitate interactive placement of particles in the flow, volume data selection using a convex hull approach, cutting plane generation, and the integration of voice control and a tablet PC will be presented. Both a stirred mixing vessel and flow through a duct will be used as examples. Finally, the benefits of VR applied to mixing analysis are presented, along with some suggestions for future work in this area

Computer Simulations of Cosmic Reionization

The cosmic reionization of hydrogen was the last major phase transition in the evolution of the universe, which drastically changed the ionization and thermal conditions in the cosmic gas. To the best of our knowledge today, this process was driven by the ultra-violet radiation from young, star-forming galaxies and from first quasars. We review the current observational constraints on cosmic reionization, as well as the dominant physical effects that control the ionization of intergalactic gas. We then focus on numerical modeling of this process with computer simulations. Over the past decade, significant progress has been made in solving the radiative transfer of ionizing photons from many sources through the highly inhomogeneous distribution of cosmic gas in the expanding universe. With modern simulations, we have finally converged on a general picture for the reionization process, but many unsolved problems still remain in this young and exciting field of numerical cosmology.Comment: Invited Review to appear on Advanced Science Letters (ASL), Special Issue on Computational Astrophysics, edited by Lucio Maye

VisIVO - Integrated Tools and Services for Large-Scale Astrophysical Visualization

VisIVO is an integrated suite of tools and services specifically designed for the Virtual Observatory. This suite constitutes a software framework for effective visual discovery in currently available (and next-generation) very large-scale astrophysical datasets. VisIVO consists of VisiVO Desktop - a stand alone application for interactive visualization on standard PCs, VisIVO Server - a grid-enabled platform for high performance visualization and VisIVO Web - a custom designed web portal supporting services based on the VisIVO Server functionality. The main characteristic of VisIVO is support for high-performance, multidimensional visualization of very large-scale astrophysical datasets. Users can obtain meaningful visualizations rapidly while preserving full and intuitive control of the relevant visualization parameters. This paper focuses on newly developed integrated tools in VisIVO Server allowing intuitive visual discovery with 3D views being created from data tables. VisIVO Server can be installed easily on any web server with a database repository. We discuss briefly aspects of our implementation of VisiVO Server on a computational grid and also outline the functionality of the services offered by VisIVO Web. Finally we conclude with a summary of our work and pointers to future developments

Efficient Lagrangian particle tracking algorithms for distributed-memory architectures

This paper focuses on the solution of the dispersed phase of Eulerian–Lagrangian one-way coupled particle laden flows. An efficient two-constraint domain partitioning for 2D and 3D unstructured hybrid meshes is proposed and implemented in distributed memory architectures. A preliminary simulation, using a set of representative particles, is performed first to suitably tag the cells with a weight proportional to the probability of being crossed by a particle. In addition, an innovative parallel ray-tracing location algorithm is presented. A global identifier is assigned to each particle resulting in a significant reduction of the overall communication among processes. The proposed approaches are verified against two steady reference cases for ice accretion simulation: a NACA 0012 profile and a NACA 64A008 swept horizontal tail. Furthermore, a cloud droplet impact test case starting from an unsteady flow around a 3D cylinder is performed to evaluate the code performances on unsteady problems