Designed in collaboration with Intel and Cray, the Argonne Leadership Computing Facility's upcoming exascale system, Aurora, will enable researchers to pursue some of the farthest-reaching science and engineering breakthroughs ever achieved with supercomputing
Rick Stevens, Argonne Associate Laboratory Director, discusses how Aurora, the lab's upcoming Intel-Cray exascale system, will be used to dramatically advance scientific research and discovery.
Argonne National Laboratory’s next-generation supercomputer, Aurora, will be one of the world’s first exascale systems when it arrives in 2021.
As part of the Argonne Leadership Computing Facility's (ALCF's) Aurora Early Science Program (ESP), a research team led by University of Colorado Boulder's Kenneth Jansen is preparing to use Argonne's upcoming Intel-Cray exascale system to advance the design of more fuel-efficient aircraft. As principal investigator of two Aurora ESP projects, Jansen is leveraging simulation, data analytics, and machine learning methods to enable computational fluid dynamics computations of unprecedented scale and complexity.
As part of the Argonne Leadership Computing Facility's (ALCF's) Aurora Early Science Program, a research team led by Argonne National Laboratory's Nicola Ferrier is preparing to use Argonne's upcoming Intel-Cray exascale system to advance neuroscience research. The team's “Enabling Connectomics at Exascale to Facilitate Discoveries in Neuroscience” project is developing a computational pipeline that can extract brain-image-derived mappings of neurons and their connections from electron microscope datasets too large for pre-exascale systems.
As part of the Argonne Leadership Computing Facility's (ALCF's) Aurora Early Science Program, a research team led by Argonne's Rick Stevens is preparing to use Argonne's upcoming Intel-Cray exascale system to advance cancer research. Leveraging the CANDLE (CANcer Distributed Learning Environment) framework, the team's "Virtual Drug Response Prediction" project is building a software environment that will bring the combined power of exascale computing and deep learning to bear on the challenges of cancer research, diagnosis, and treatment.
As part of the Argonne Leadership Computing Facility's (ALCF's) Aurora Early Science Program, a research team led by Argonne National Laboratory physicist Jimmy Proudfoot is preparing to use the lab's upcoming Intel-Cray exascale system to advance physics research at CERN's Large Hadron Collider. The team's "Simulating and Learning in the ATLAS Detector at the Exascale" project is developing exascale workflows, algorithms, and machine learning capabilities to accelerate the search for new physics discoveries.
As part of the Argonne Leadership Computing Facility's (ALCF's) Aurora Early Science Program, a research team led by Princeton Plasma Physics Laboratory scientist William Tang is preparing to use Argonne's upcoming Intel-Cray exascale system to advance fusion energy research. The team's “Accelerated Deep Learning Discovery in Fusion Energy Science” project will leverage artificial intelligence to predict and tame disruptions that can halt fusion reactions and damage tokamak devices.
As part of the Argonne Leadership Computing Facility's (ALCF's) Aurora Early Science Program, a research team led by Argonne National Laboratory's David Bross is preparing to use Argonne's upcoming Intel-Cray exascale system to advance catalysis research. The team's “Exascale Computational Catalysis” project is developing software tools to facilitate and significantly speed up the quantitative description of crucial gas-phase and coupled heterogeneous catalyst/gas-phase chemical systems.
