Dr. Rommie E. Amaro from UC San Diego gave this talk at the Blue Waters Symposium. “In this talk I will discuss how the BlueWaters Petascale computing architecture forever altered the landscape and potential of computational biophysics. In particular, new and emerging capabilities for multiscale dynamic simulations that cross spatial scales from the molecular (angstrom) to cellular ultrastructure (near micron), and temporal scales from the picoseconds of macromolecular dynamics to the physiologically important time scales of organelles and cells (milliseconds to seconds) are now possible.”
Advances in the Fields of Atmospheric Science, Climate, and Weather
Susan Bates from NCAR gave this talk at the Blue Waters Summit. “For the past five years, the Blue Waters Project has provided an invaluable platform for research in the fields of atmospheric science, climate, and weather. The computationally intensive numerical models running on Blue Waters push the limits of model resolution and/or capability in first-of-their-kind simulations.”
Containers: Shifter and Singularity on Blue Waters
In this video from the Blue Waters 2018 Symposium, Maxim Belkin presents a tutorial on Containers: Shifter and Singularity on Blue Waters. “Container solutions are a great way to seamlessly execute code on a variety of platforms. Not only they are used to abstract away from the software stack of the underlying operating system, they also enable reproducible computational research. In this mini-tutorial, I will review the process of working with Shifter and Singularity on Blue Waters.”
Video: Massive Galaxies and Black Holes at the Cosmic Dawn
Tiziana DiMatteo from Carnegie Melon University gave this talk at the 2018 Blue Waters Symposium. “The first billion years is a pivotal time for cosmic structure formation. The galaxies and black holes that form then shape and influence all future generations of stars and black holes. Understanding and detecting the the first galaxies and black holes is therefore one of the main observational and theoretical challenges in galaxy formation.”
Machine Learning with Python: Distributed Training and Data Resources on Blue Waters
Aaron Saxton from NCSA gave this talk at the Blue Waters Symposium. “Blue Waters currently supports TensorFlow 1.3, PyTorch 0.3.0 and we hope to support CNTK and Horovod in the near future. This tutorial will go over the minimum ingredients needed to do distributed training on Blue Waters with these packages. What’s more, we also maintain an ImageNet data set to help researchers get started training CNN models. I will review the process by which a user can get access to this data set.”
Using Ai to detect Gravitational Waves with the Blue Waters Supercomputer
NASA researchers are using AI technologies to detect gravitational waves. The work is described in a new article in Physics Review D this month. “This article shows that we can automatically detect and group together noise anomalies in data from the LIGO detectors by using artificial intelligence algorithms based on neural networks that were already pre-trained to classify images of real-world objects,” said research scientist, Eliu Huerta.
Supercomputing Graphene Applications in Nanoscale Electronics
Researchers at North Carolina State University are using the Blue Waters Supercomputer to explore graphene’s applications, including its use in nanoscale electronics and electrical DNA sequencing. “We’re looking at what’s beyond Moore’s law, whether one can devise very small transistors based on only one atomic layer, using new methods of making materials,” said Professor Jerry Bernholc, from North Carolina University. “We are looking at potential transistor structures consisting of a single layer of graphene, etched into lines of nanoribbons, where the carbon atoms are arranged like a chicken wire pattern. We are looking at which structures will function well, at a few atoms of width.”
Supercomputing Better Tools for Long-Term Crop Prediction
Researchers are using the Blue Waters supercomputer to create better tools for long-Term crop prediction. “We built this new tool to bridge these two types of crop models combining their strengths and eliminating the weaknesses. This work is an outstanding example of the convergence of simulation and data science that is a driving factor in the National Strategic Computing Initiative announced by the White House in 2015.”
Video: Deep Learning for Real-Time Gravitational Wave Discovery
Scientists at NCSA have pioneered the use of GPU-accelerated deep learning for rapid detection and characterization of gravitational waves. This new approach will enable astronomers to study gravitational waves using minimal computational resources, reducing time to discovery and increasing the scientific reach of gravitational wave astrophysics.
Advanced Protein Prediction Using Deep Learning on Blue Waters Supercomputer
Researchers at NCSA used the Blue Waters Supercomputer and Deep Learning to achieve a breakthrough in protein structure predictions. As published in the Cell Systems journal, the research was conducted by Jian Peng, NCSA Faculty Fellow and Assistant Professor in the Department of Computer Science at Illinois and Yang Liu, a graduate student in the Department of Electrical and Computer Engineering. “Peng’s research proposes to largely explore a more accurate function for evaluating predicted protein structures through his development of the deep learning tool, DeepContact. DeepContact automatically leverages local information and multiple features to discover patterns in contact map space and embeds this knowledge within the neural network. Furthermore, in subsequent prediction of new proteins, DeepContact uses what it has learned about structure and contact map space to impute missing contacts and remove spurious predictions, leading to significantly more accurate inference of residue-residue contacts.”