Scientific Visualization has proven to be an effective means for analyzing multivariate multidimensional data (MVMD). A variety of techniques combining statistical and visual analytic tools have been developed in the recent years to analyze MVMD. Visual differencing, or visual discrimination, is the ability to compare an attribute value between two or more objects in a visualization. In this research, we are examining humans’ predictable bias in interpreting visual-spatial information for comparison and inference. We will develop and evaluate new techniques of data representation that support multivariate multidimensional visual differencing. We will also address the trade-off between proximity and occlusion and evaluate users’ ability to explore MVMD across the immersive spectrum.
As the breadth, usage and impact of High Performance Computing and Visualization continues to grow, Virginia Tech and Advanced Research Computing are again proud to host HPC Day. The campus community is cordially invited to various featured speakers, research talks, student lightning talks, and a panel that will take place on Monday, April 11th, 2016.
POSTER, LUNCH, AND ATTENDANCE RSVP: I WANT TO ATTEND!
ARC HPC Systems will undergo maintenance beginning at midnight on the morning of Tuesday, March 29, 2016. The purpose of this maintenance will be to migrate to a new shared Home directory on the file system that currently provides Home to NewRiver. This will provide two key benefits to users:
- All files in your Home directory will be visible from all clusters. For example, you will see the same files in $HOME from both NewRiver and BlueRidge. This will make it easier to migrate work between clusters based on which hardware is best suited to the task or which resource is less busy.
- The maximum Home directory size will be increased from 100 GB to 500 GB per user.
Continue reading ARC Migrating to Shared Home Directories, 29 Mar 2016
Dr. Nicholas Polys recently returned from the Federal In-service Training day where he showcased his work for AEC. The presentation took place in the Hirshhorn Auditorium and was focused on infrastructure based standards for X3D.
Please click here to view his presentation.
Researchers supported by the Institute for Creativity, Arts, and Technology are developing an interactive 3-D environment that will bring together data from multiple research locations, including water quality data, to produce more comprehensive models and analytics for community ecosystem monitoring, targeting ongoing research activities at Stroubles Creek and the Catawba Sustainability Center.
Read more here.
An Evaluation of the Effects of Hyper-Natural Components of Interaction Fidelity on Locomotion Performance in Virtual Reality
Mahdi Nabiyouni and Doug A. Bowman
A short description:
Hyper-natural interaction techniques are intentionally designed to enhance users’ abilities beyond what is possible in the real world. We compared such hyper-natural techniques to their natural counterparts on a wide range of locomotion tasks for a variety of measures. The results show that the effects of the hyper-natural transfer function was mostly positive, however, hyper-natural techniques designed to provide biomechanical assistance had lower performance and user acceptance than those based on natural walking movements.
Here is the link to the conference website:
Please find ARC Road Show Materials (Presentation, Mission, RPF) located Here
And the MOU from the ARC Road Show located Here
Advanced Research Computing is pleased to announce the public release of its newest high-performance computing (HPC) system, NewRiver, to the academic and research community at Virginia Tech. The 134-node system has an aggregate peak computing capacity of 152 TFLOPs (trillions of floating point operations per second) and 33 terabytes (TB) of aggregate memory. In addition, NewRiver is one of the first computational clusters to use the latest generation of InfiniBand interconnect, EDR, which connects the compute and storages nodes at a peak speed of 100 Gigabits/second.
NewRiver is capable of tackling the full spectrum of computational workloads, from problems requiring hundreds of compute cores to data-intensive problems requiring large amount of memory and storage resources. NewRiver contains five compute engines designed for distinct workloads.
- General – Distributed, scalable workloads. With two of Intel’s latest-generation Haswell processors, 24 cores, and 128 GB memory on each node, this 100-node compute engine is suitable for traditional HPC jobs and large codes using MPI.
- Big Data – High performance data analytics. With 43.2 TB of direct-attached storage for each node, this system enables processing and analysis of massive datasets. The 16 nodes in this compute engine also have 512 gigabytes (GB) of memory, making them suitable for jobs requiring large memory.
- GPU – Data visualization and code acceleration. Nvidia K80 GPUs in this system can be used for code acceleration. They will also provide remote rendering, allowing large datasets to be viewed in place without lengthy data transfers to desktop PCs. The 8 nodes in this compute engine also have 512 GB of memory, making them suitable for jobs requiring large memory.
- Interactive – Rapid code development and interactive usage. Each of the eight nodes in this compute engine have 256 GB memory and a K1200 graphics card. A browser-based, client-server architecture allows for a responsive, desktop-like experience when interacting with graphics-intensive programs.
- Very Large Memory – Graph analytics and very large datasets. With 3TB (3072 gigabytes) of memory, four 15-core processors and 6 direct attached hard drives, each of the two servers in this system will enable analysis of large highly-connected datasets, in-memory database applications, and speedier solution of other large problems.
ARC’s computational scientists and support staff are available to help faculty and students choose the right compute engine for their research. ARC offers assistance with code development and optimization for high performance computing systems and installation of software.
Access to ARC’s resources is available to all Virginia Tech faculty, staff, and students. Higher priority access can be purchased through the Investment Computing Program, which provides a way for departments and individual faculty members to gain access to a larger share of resources than it is otherwise possible for ARC to provide. ARC’s BlueRidge cluster was partially funded by the investment program.
This tutorial, given by Srijith Rajamohan, and Peter Radics, covers the Python programming language including all the information needed to participate in the XSEDE15 Modeling Day event on Tuesday, July 27th, 2015. Topics covered are variables, input/output, control structures, math libraries, and plotting libraries. This tutorial uses Anaconda Python Package, that you can download here .
Click here to access the tutorial slides.
To check out pictures from the event, visit our Facebook Page.
BLACKSBURG, Va., May 15, 2015 – Vijay K. Agarwala has been named director of high performance computing for Advanced Research Computing, a unit of Information Technology at Virginia Tech. Agarwala will provide technical and strategic leadership for the group’s advanced computing infrastructure.
To read the full article featured on VT News, click here