HPC Day Agenda
We are looking forward to seeing you at our Annual HPC Day event March 24 from 10am-5pm!
The event includes: a keynote by James Ahrens from LANL, a machine learning workshop, and faculty and student presentations.
Keynote: “Supercharging the Scientific Process Via Data Science at Scale”
Dr. James Ahrens is a senior research scientist at the Los Alamos National Laboratory (LANL). He is the founder and design lead of ParaView, a widely adopted visualization and data analysis package for large-scale scientific simulation data ( http://paraview.org). ParaView has had an extremely positive impact on the large-scale data analytic capabilities available to simulation scientists around the world. Dr. Ahrens graduated in 1989 with a B.S. in computer science from the University of Massachusetts and in 1996 with a Ph.D. in computer science from the University of Washington. At LANL, he is part of a data science team of twenty staff, postdocs and students. He is also a national leader of programmatic initiatives important to the United States Department of Energy’s National Nuclear Security Administration and Office of Science. Dr. Ahrens is the Data Analysis and Visualization lead for the U.S. Exascale Computing Project and the general chair for this year’s IEEE Scientific Visualization conference to be held in Phoenix, AZ in early October.
ARC member Nicholas Polys helped organize a session at the CHCI Workshop Technology on the Trail on March 2-3. The session “From Experience to Abstraction and Back Again” discussed the challenges and opportunities for citizen science, especially the use of uncertain data to build new scientific models. The event was covered with an article in the Roanoke Times!
Featuring sessions on big data workflows, data visualization, data publishing, and reproducible research practices, the 2017 Big Data Science Workshop will also incorporate a brainstorming/strategy session aimed at improving research workflows, a networking breakfast, and lightning talks.
ARC’s Nicholas Polys and Brian Marshall each presented. The event flyer is here:
Big Data Science Workshop
The Visionarium got a spot in the 2016 Hokie Halftime commercial, which aired during Virginia Tech’s first football game against Liberty University. Check us out and see what we have been up to recently! Go Hokies! https://www.youtube.com/watch?v=Jl9iL2a-pmw
Our last halftime spot was 2012 : https://www.youtube.com/watch?v=p8nER5wb6cA
ARC is happy to announce the release of a new cluster, named DragonsTooth, available at
dragonstooth1.arc.vt.edu. DragonsTooth is made up of 48 nodes, each equipped with:
- 2 x Intel Xeon E5-2680v3 (Haswell) 2.5 GHz 12-core CPU (same CPU as NewRiver)
- 256 GB 2133 MHz DDR4 memory for large-memory problems
- 4 x 480 GB SSD Hard Drives for fast local I/O ($TMPDIR)
- 806 GFlops/s theoretical double-precision peak
Continue reading New ARC Cluster: DragonsTooth
Dr. Srijith Rajamohan presented an Introduction to Python Pandas for Data Analytics tutorial. Pandas is a high-level open-source library that provides data analysis tools for Python. The audience was also introduced to relevant packages such as Numpy for fast numeric computation and Matplotlib/Bokeh for visualization to supplement the data analysis process. The slides for this tutorial can be found here.
Visualization GRA and Doctoral Candidate Ayat Mohammed presented a visualization showcase titled ‘Insights into Alzheimer’s Disease: Molecular Dynamics (MD) Simulations of Peptide-Membrane Interactions’ at XSEDE16, Miami. Also from ARC, Alana Romanella chaired the session on Workforce Development and Diversity.
Dr. Srijith Rajamohan and Dr. Nicholas Polys in ARC and Assistant Professor, Vivica Kraak in the Department of Human Nutrition, Foods, and Exercise are collaborating on a research project to map the world of celebrity endorsement of food and beverage brands, products and groups in the United States. HNFE doctoral student, Mi Zhou, is part of the research team with ARC MS student, Faiz Abidi, to build, analyze and visually display in 2D and 3D a database of more than 550 unique celebrities used to market food, beverage and restaurant products to children, teens and adults. The ARC team had helped build an open-source analytics and visualization engine to help address these needs.
The results of this project will be used to inform the policies and actions of diverse stakeholders including industry, government and public health groups to use celebrity endorsement, along with other integrated marketing communications, to promote healthy nutrient-profile products and behaviors that support healthy food environments for American children, adolescents and their parents. Prof. Kraak and her work was recently featured on the VT news which can be found here:
Researchers at Virginia Tech have modeled and mapped grape production across an area spanning 19 states along the eastern US. Supercomputers helped crunch the numbers and stomp the grapes in an effort to speed wine development in the region. What these scientists have learned could aid farmers across the world and protect crops as our climate changes. Read more here.
Thank you to all the students who participated in the HPC Day Poster Session. Our 2016 top three finishers include:
First Place: Bobby Hollingsworth
Computational Insights into Binding of a Repeat Unit of an Antiviral Copolymer to Glycoprotein 120 in Four Strains of HIV
Second Place: Mariam Umar, Sand L. Correa, Kirk W. Cameron
Energy and Performance Modeling and Estimation for ASPEN Domain Specific Language
Third Place: Megan Richardson
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.