We have put up a channel in Vimeo that showcases the Broader Impacts of our HPC and Visualization work :
VT Biochemistry had a strong showing, presenting their results on using immersive visualization in VT's Visionarium Hypercube to engage and teach students (paper & presentation here).
The Technical Paper was presented and awarded Best Student Paper!
Dr. Srijith Rajamohan (email@example.com) presented a workshop on ‘Introduction to Machine Learning with TensorFlow and Keras’. The purpose of this workshop was to provide a formal introduction to the mathematical concepts underlying Machine Learning. This was augmented by hands-on examples in the Machine Learning framework TensorFlow and the Deep Learning framework Keras. The slides for this workshop can be found at https://srijithr.gitlab.io/pos
Alana Romanella (firstname.lastname@example.org) served on the executive committee as Diversity and Workforce Development Chair and Chair Emeritus for the Student Program. She focused on promoting inclusivity through increasing individual diversity awareness skills and effective organizational systems that allowed for a more diverse conference.
Interested in joining us next year?
PEARC19, will be located in Chicago from July 28 – August 1, 2019, and will explore the current practice and experience in advanced research computing including modeling, simulation, and data-intensive computing. A primary focus next year will be on Machine Learning and Artificial Intelligence which are proving to be disruptive technologies in a diverse range of scientific fields from materials science to medicine. https://www.pearc19.pearc.org/
ARC released a new cluster named Huckleberry in late 2017. The Huckleberry system, accessed at huckleberry1.arc.vt.edu, was installed with deep learning applications in mind. To this end, it consists of 14 IBM “Minsky” S822LC nodes and NVIDIA's proprietary NVLink interconnect network. This system enables highly parallel and highly distributed workloads. IBM unveiled its deep learning AI toolkit called PowerAI alongside the launch of Minsky nodes that leverage CPUs linked to Power CPUs with NVLink making it possible to have high speed high performance computing. PowerAI is available under
/opt/DL in Huckleberry.
Each compute node on Huckleberry (i.e. IBM “Minsky” nodes) consists of :
- Two IBM Power8 with 10 cores, 8 threads per core and memory bandwidth 115gb/s per socket
- Four NVIDIA P100 GPUs advertised to have 21 teraFLOPS of 16-bit floating-point performance ideal for deep learning applications deliver high performance, massive parallelism
- NVIDIA's NVLink technology which provides high bandwidth data transfers between CPUs and GPUs; an improvement over PCI-Express
- Mellanox EDR Infiniband (100 GB/s) interconnect used to connect compute nodes
The PowerAI toolkit contains Caffe, TensorFlow etc. which are optimized for the Power servers. IBM provides support for it as well.
While the rest of the clusters make use of the PBS batch systems, Huckleberry makes use of the Slurm batch system using the command
From any ARC login node, you can explore how to use ARC software by checking the directory /opt/examples
Details on the examples directory are in
The “Wing It” exhibit (both actual and virtual) just won a 2018 SEGD Global Design Merit award!!
Congratulations to Dr. Polys and his team!
ARC is happy to announce the addition of 39 new GPU nodes to the NewRiver cluster. Each of these nodes is equipped with two Intel Xeon E5-2680v4 (Broadwell) 2.4GHz GPU (28 cores/node in all), 512 GB memory, and two NVIDIA P100 GPUs. Each GPU is capable of up to 4.7 TeraFLOPS of double-precision performance, so including CPU and GPU these nodes add over 400 TFLOPS of peak double-precision throughput to ARC's resources.
Continue reading P100 GPU Nodes added to NewRiver
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: