Deploying Web-based Visual Exploration Tools on the Grid

We discuss a web-based portal for the exploration, encapsulation, and dissemination of visualization results over the Grid. This portal integrates three components: an interface client for structured visualization exploration, a visualization web application to manage the generation and capture of the visualization results, and a centralized portal application server to access and manage grid resources. Our approach uses standard web technologies to make the system accessible with minimal user setup. We demonstrate the usefulness of the developed system using an example for Adaptive Mesh Refinement (AMR) data visualization

Properties of the Volume Operator in Loop Quantum Gravity II: Detailed Presentation

The properties of the Volume operator in Loop Quantum Gravity, as constructed by Ashtekar and Lewandowski, are analyzed for the first time at generic vertices of valence greater than four. The present analysis benefits from the general simplified formula for matrix elements of the Volume operator derived in gr-qc/0405060, making it feasible to implement it on a computer as a matrix which is then diagonalized numerically. The resulting eigenvalues serve as a database to investigate the spectral properties of the volume operator. Analytical results on the spectrum at 4-valent vertices are included. This is a companion paper to arXiv:0706.0469, providing details of the analysis presented there.Comment: Companion to arXiv:0706.0469. Version as published in CQG in 2008. More compact presentation. Sign factor combinatorics now much better understood in context of oriented matroids, see arXiv:1003.2348, where also important remarks given regarding sigma configurations. Subsequent computations revealed some minor errors, which do not change qualitative results but modify some numbers presented her

Testing gravitational-wave searches with numerical relativity waveforms: Results from the first Numerical INJection Analysis (NINJA) project

The Numerical INJection Analysis (NINJA) project is a collaborative effort between members of the numerical relativity and gravitational-wave data analysis communities. The purpose of NINJA is to study the sensitivity of existing gravitational-wave search algorithms using numerically generated waveforms and to foster closer collaboration between the numerical relativity and data analysis communities. We describe the results of the first NINJA analysis which focused on gravitational waveforms from binary black hole coalescence. Ten numerical relativity groups contributed numerical data which were used to generate a set of gravitational-wave signals. These signals were injected into a simulated data set, designed to mimic the response of the Initial LIGO and Virgo gravitational-wave detectors. Nine groups analysed this data using search and parameter-estimation pipelines. Matched filter algorithms, un-modelled-burst searches and Bayesian parameter-estimation and model-selection algorithms were applied to the data. We report the efficiency of these search methods in detecting the numerical waveforms and measuring their parameters. We describe preliminary comparisons between the different search methods and suggest improvements for future NINJA analyses.Comment: 56 pages, 25 figures; various clarifications; accepted to CQ

HPC graphics and visualization, not games, drive advances in networked graphics technology

A mantra we've heard over the years is that the consumer gaming market drives graphics technology. Our thesis is that the same does not hold true for networking technology. Instead, it is applications from high performance graphics and visualization efforts, not games, that push networks past their breaking point, which in turn stimulates work that results in better networks. This case study shows how a high performance computational science and visualization application has set new levels of networking performance by winning the SC2001 Application Bandwidth Challenge by novel use of existing network protocols

Algorithms, Frameworks and Toolsets for High Performance, Remote and Distributed Visualization

This document is a two-page summary of research accomplishments in the LBNL Visualization program during FY 2005

ISC High Performance 2017 International Workshops, DRBSD, ExaComm, HCPM, HPC-IODC, IWOPH, IXPUG, P^3MA, VHPC, Visualization at Scale, WOPSSS

This book constitutes revised selected papers from 10 workshops that were held as the ISC High Performance 2017 conference in Frankfurt, Germany, in June 2017. The 59 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They stem from the following workshops: Workshop on Virtualization in High-Performance Cloud Computing (VHPC) Visualization at Scale: Deployment Case Studies and Experience Reports International Workshop on Performance Portable Programming Models for Accelerators (P^3MA) OpenPOWER for HPC (IWOPH) International Workshop on Data Reduction for Big Scientific Data (DRBSD) International Workshop on Communication Architectures for HPC, Big Data, Deep Learning and Clouds at Extreme Scale Workshop on HPC Computing in a Post Moore's Law World (HCPM) HPC I/O in the Data Center ( HPC-IODC) Workshop on Performance and Scalability of Storage Systems (WOPSSS) IXPUG: Experiences on Intel Knights Landing at the One Year Mark International Workshop on Communication Architectures for HPC, Big Data, Deep Learning and Clouds at Extreme Scale (ExaComm

H5Part: A Portable High Performance Parallel Data Interface for Particle Simulations

The very largest parallel particle simulations, for problems involving six dimensional phase space, generate vast quantities of data. It is desirable to store such enormous datasets efficiently and also to share data effortlessly between data analysis tools such as PartView~\cite FPAT082 and extensions to AVS/Express among other groups who are working on particle-based accelerator simulations. We define a very simple file schema built on top of HDF5~\cite hdf5hp (Hierarchical Data Format version 5) as well as an API that simplifies the reading/writing of the data to the HDF5 file format. HDF5 offers a self-describing machine-independent binary file format that supports scalable parallel I/O performance for MPI codes on computer systems ranging from laptops to supercomputers. The sample H5PartAPI is available for C, C++, and Fortran codes. The common file format will enable groups that using completely different simulation implementations to transparently share datasets and custom data analysis tools like Part View. We will show examples and benchmark data for various platforms

A Framework for Visualizing Hierarchical Computations

Researchers doing scientific computations are attempting to accurately model physical phenomena. When these physical phenomena take place at a variety of different spatial and temporal scales it can be more efficient and accurate to model them at different levels of detail in an adaptive, hierarchical manner. We present a framework for visualizing adaptive, hierarchical computations- a conceptual framework and an implementation framework. Given that researchers have already defined a hierarchical structure for their data and are performing their computations using this structure, it has become important to provide a visualization tool which accurately represents this data and visualizes it directly. The tool we have designed for this purpose was built using the Visualization Toolkit, VTK, and one of its interpretive interfaces, Tcl/Tk. In addition to creating a visualization tool, we are developing extensions of visualiztion techniques and algorithms to hierarchiacal data (i.e., seamless isosurface generation)

Remote interactive direct volume rendering of AMR data

We describe a framework for direct volume rendering of adaptive mesh refinement (AMR) data that operates directly on the hierarchical grid structure, without the need to resample data onto a single, uniform rectilinear grid. The framework can be used for a range of renderers optimized for particular hardware architectures: a hardware-assisted renderer for single-processor graphics workstations, and a massively parallel software-only renderer for supercomputers. It is also possible to use the framework for distributed rendering servers. By exploiting the multiresolution structure of AMR data, the hardware-assisted renderers can render large AMR data sets at interactive rates, even if the data is stored remotely