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Atmospheric Radiation Measurement (ARM) Program standard data format is NetCDF 3 (Network Common Data Form). The object of this tutorial is to provide a basic introduction to NetCDF with an emphasis on aspects of the ARM application of NetCDF. The goal is to provide basic instructions for reading and visualizing ARM NetCDF data with the expectation that these examples can then be applied to more complex applications.
The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.
The dragon y topology is becoming a popular choice for build- ing high-radix, low-diameter networks with high-bandwidth links. Even with a powerful network, preliminary experi- ments on Edison at NERSC have shown that for communica- tion heavy applications, job interference and thus presumably job placement remains an important factor. In this paper, we explore the e ects of job placement, job sizes, parallel workloads and network con gurations on network through- put to better understand inter-job interference. We use a simulation tool called Damsel y to model the network be- havior of Edison and study the impact of various system parameters on network throughput. Parallel workloads based on ve representative communication patters are used and the simulation studies on up to 131,072 cores are aided by a new visualization of the dragon y network.
The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.
A graphical user interface (GUI) tool has been developed that facilitates the visualization and analysis of the Chapman-Jouguet state for high explosives gaseous products using the Jones- Wilkins-Lee equation of state.
The purpose of this white paper is to demonstrate the importance of color palette choice in scientific visualizations and to promote an effort to convene an interdisciplinary team of researchers to study and recommend color palettes based on intended application(s) and audience(s).
The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.
The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.
The goal of this collaborative effort was to develop a data-flow graphics tool (DFG) to visualize the execution of macro-dataflow graphs. LSI incorporated DFG into their graphic user interface to support parallel code development_ and text handling for natural language applications. The CRADA applied LLNL' s expertise in high-performance parallel programming systems based on the dataflow paradigm.
Defense Waste Processing Facility (DWPF) Melter 2 reached its end of service life after nearly14 years. A visual inspection of the interior of the melter was performed using a remotely operated camera to provide insight into melter performance and potential failure mechanisms. The condition of the interior of the melter was generally good considering its relatively long service life. The plenum refractory appeared to be in good condition. Spalling and wear of the glass contact refractory was evident at the melt line. There appeared to be some degree of degradation and deposition of material at the dome heater penetrations. Deformation, or sagging of the dome heaters was visible. Only the top surfaces of the upper electrodes were visible due to the full inventory of glass remaining after melter shutdown. The electrodes exhibited small cracks as well as minor deformation (creep or slumping). The lower electrode failure mechanism could not be determined since it was not visible.
The placement and movement of data is becoming the key limiting factor on both performance and energy efficiency of high performance computations. As systems generate more data, it is becoming increasingly difficult to actually move that data elsewhere for post-processing, as the rate of improvements in supporting I/O infrastructure is not keeping pace. Together, these trends are creating a shift in how we think about exascale computations, from a viewpoint that focuses on FLOPS to one that focuses on data and data-centric operations as fundamental to the reasoning about, and optimization of, scientific workflows on extreme-scale architectures. The overarching goal of our effort was the study of a unified data-driven approach for programming applications and in situ analysis and visualization. Our work was to understand the interplay between data-centric programming model requirements at extreme-scale and the overall impact of those requirements on the design, capabilities, flexibility, and implementation details for both applications and the supporting in situ infrastructure. In this context, we made many improvements to the Legion programming system (one of the leading data-centric models today) and demonstrated in situ analyses on real application codes using these improvements.
As we look ahead to next generation high performance computing platforms, the placement and movement of data is becoming the key-limiting factor on both performance and energy efficiency. Furthermore, the increased quantities of data the systems are capable of generating, in conjunction with the insufficient rate of improvements in the supporting I/0 infrastructure, is forcing applications away from the off-line post-processing of data towards techniques based on in ,situ analysis and visualization. Together, these challenges are shaping how we will both design and develop effective, performant and energy-efficient software. In particular, the challenges highlight the need for data and data-centric operations to be fundamental in the reasoning about, and optimization of, scientific workflows on extreme-scale architectures.
With funding provided from the Office of Fusion Energy Science’s program on Diagnostic Systems for Magnetic Fusion Energy Sciences, the PI and his team have pioneered the development of microwave imaging systems for magnetic fusion plasma diagnostics. Specifically, electron cyclotron emission (ECE) imaging [1], a passive radiometric technique, measures electron temperature fluctuations; and microwave imaging reflectometry (MIR) [2], an active radar imaging technique, measures electron density fluctuations. These microwave imaging diagnostic instruments developed by UC Davis have made important contributions to fusion science in a relatively short period of time from their original installation on DIII-D. The microwave imaging diagnostic technique has matured considerably since the beginning of a fruitful collaboration with DIII-D and PPPL, and has also been adopted at preeminent tokamak facilities worldwide. As discussed in detail in the following, this has continued with the development of revolutionary microwave diagnostic instruments employing monolithic millimeter wave integrated circuit (MMIC) system-on-chip (SoC) technology. Specifically, “chip” receivers mounted in liquid crystal polymer (LCP) modules have been demonstrated to dramatically improve the performance of the electron cyclotron emission imaging (ECEI) system including: 30x improvement in noise temperature thereby permitting absolute 2-D electron temperature images, outstanding shielding against out-of-band interference including microwave bursting which had plagued previous ELM studies using the old quasi-optical mini-lens ECEI system, and independent modules facilitating flexible repair and replacement. Under the “Visualization of multi-dimensional imaging data for the DIII-D National Fusion Facility” program, funding was provided for a research scientist postdoc and a Ph.D. student to support physics studies at DIII-D employing these instruments. This endeavor is synergistic with our current grant under the DOE Fusion Energy Sciences (FES) program on Diagnostic Systems for Magnetic Fusion Energy Sciences. This support considerably increased the scientific output of the diagnostics. The activities were strongly focused in the areas of pedestal and ELM stability, energetic particle confinement, and the early detection and avoidance of tokamak disruptions employing the microwave imaging diagnostic instruments developed under the advanced diagnostics program where imaging of the ELM crash was made possible by the elimination of the interference from the associated microwave “bursts”
Smuggling of nuclear and radiological material is a complex problem that will need to be analyzed from a variety of angles in order to effectively combat something so rare, yet so hazardous. One crucial tool in the effort to detect and deter smuggling of rad/nuc materials is the radiation portal monitor (RPM)—machines that scan passengers at airports, vehicles and trains at border crossings, and shipping containers at ports throughout the world. While data from RPMs are regularly analyzed at the individual lane level to ensure monitor health and good operation, aggregate analysis of the data has not gone far beyond summary statistics such as number of occupancies and alarm rates. In particular, there are opportunities to carry out interactive exploratory data analysis with the help of tools like the R package Shiny and methods that lend themselves to the visual display of spatiotemporal correlations such as spatial PCA and detrended cross-correlation analysis. By doing so, we discover interesting spatiotemporal patterns in the data that point to specific events or anomalous moments in time that warrant further analysis.
The goal of Kitware’s work has been the study of a unified data-driven approach for programming applications and in situ analysis and visualization. We have especially focused on understanding the interplay between data-centric programming model requirements at extreme-scale and the overall impact of those requirements on the design, capabilities, flexibility, and implementation details for both applications and the supporting in situ infrastructure. We have successfully demonstrated the use of pipeline abstractions within Legion and developed a number of algorithms that were integrated within the Legion simulation framework. This report focuses on describing milestones, mainly those focusing on exploring pipeline abstractions in the context of the Legion programming model.
Running visualization and analysis algorithms on ATS-1 platforms is a critical step for supporting ATDM apps at the exascale level. We are leveraging VTK-m to port our algorithms to the ATS-specific hardware and ensuring that it runs well.
Presentation covering technologies and methods in genomics, bioinformatics, microbiome research, data visualization and biosurveillance
The Department of Energy (DOE) Office of Science (SC) operates dozens of national science user facilities that span many different disciplines. These facilities include accelerators, colliders, supercomputers, light sources and neutron sources, as well as facilities for studying the nanoworld, the environment, and the atmosphere. In Fiscal Year 2014 over 33,000 researchers from academia, industry, and government laboratories, spanning all fifty states and the District of Columbia, utilized these unique facilities to perform new scientific research. Each of these facilities generates vast amounts of scientific data, and the rate, size, and complexity of this data is rapidly increasing, thanks to advances in technology. A growing concern, which motivates this workshop, is the likely significant adverse impact on science programs that will result without significant advances in the capabilities needed to manage and gain knowledge from these collections of data. The purpose of this workshop, held 29 September 2015 through 1 October 2015 in Bethesda, MD, is to help the Advanced Scientific Computing Research (ASCR) and research community better understand needs related to the management, analysis, and visualization of experimental and observational data (EOD) collected and generated by experimental and observational science projects (EOS) at Office of Science user facilities.