Coming in 2021

Argonne National Laboratory’s first exascale computer is coming soon, and will exclusively serve the research community. Scientists will use the new machine, named Aurora, to pursue some of the farthest-reaching science and engineering breakthroughs ever achieved with supercomputing.

Aurora’s revolutionary architecture, designed in collaboration with Intel and Cray, will support machine learning and data science workloads alongside traditional modeling and simulation workloads.

In terms of opening new frontiers in science, the sky’s the limit. Literally. The forthcoming machine will process data from the latest sky surveys, to help answer some of the biggest questions in physics about the nature of the universe.

The science teams using it will also develop alternative energy sources, design safer vehicles, invent new materials, understand how our brains work, and find ways to keep us healthier and safer.

Aurora will be housed at the Argonne Leadership Computing Facility, a U.S. Department of Energy (DOE) Office of Science User Facility and a premier source of world-class computing resources for open science research since 2006.

For decades, the DOE has been building an aggressive scientific computing research program to give our nation a strategic competitive advantage in the advancement of science and technology. Today, the U.S. program is unrivaled in the world and provides groundbreaking discoveries in all fields of inquiry.

The massive machines at the core of the DOE supercomputing program are technological wonders in themselves—each a unique design, and each one ten to a hundred times more powerful than other systems used for scientific research. For the research community that relies on these machines to push the frontiers of science and engineering, big is never big enough and fast is never fast enough.

A brand-new class of system

Each machine generation provides a fresh challenge to U.S. computer manufacturers—from the racks to the processors to the networking to the I/O system. Similarly, fulfilling the science potential of each new computing architecture requires significant changes to today’s software. The initiative is, and will continue to be, guided by pioneering visionaries in the mathematics and computational science community, stewarded by the DOE’s Office of Science, and operated at the cutting edge.

But while people have been using supercomputers to solve big problems for years, the capabilities of the machines that will soon begin rolling out in national labs around the country willbe brand new.

Researchers will be able to run a greater diversity of workloads, including machine learning and data intensive tasks, in addition to traditional simulations. Providing the data science software “stack”—the high-level programming languages, frameworks, and I/O middleware that are conventional toolkits—at exascale, is a major effort in deploying Aurora.

Revolutionary architecture

Aurora will feature several technological innovations, including uniform high-performance memory and a revolutionary I/O system to support new types of workloads. Programming techniques already in use on current systems will apply directly to Aurora. The system will be highly optimized across multiple dimensions that are key to success in simulation, data, and learning applications:


  • FLOPS (>1 exaFLOPS)
  • Concurrency
  • Memory performance
  • System Interconnect
  • ML/DL operations


  • Speed
  • Capacity
  • Flexibility
    — Conventional I/O
    — Database
    — Analytics middleware

Programming Environment

  • Optimizing compilers
  • Latest OpenMP
  • Key Big Data stack components
  • Productive languages
  • ML/DL frameworks
  • Optimized libraries
    — Math, Statistics, ML/NN

Aurora Early Science Program

The Aurora Early Science Program will prepare key applications for Aurora’s scale and architecture, and will solidify libraries and infrastructure to pave the way for other production applications to run on the system.

The program has selected 15 projects, proposed by investigator-led teams from universities and national labs and covering a wide range of scientific areas and numerical methods.

In collaboration with experts from Intel and Cray, ALCF staff will train the teams on the Aurora hardware design and how to program it. This includes not only code migration and optimization, but also mapping the complex workflows of data-focused, deep learning, and crosscutting applications. The facility will publish technical reports that detail the techniques used to prepare the applications for the new system.

In addition to fostering application readiness for the future supercomputer, the Early Science Program allows researchers to pursue innovative computational science campaigns not possible on today’s leadership-class supercomputers.

Aurora ESP Projects

The combination of simulation, data science, and machine learning will transform how supercomputers are used for scientific discovery and innovation.


Extending Moore’s Law Computing with Quantum Monte Carlo

PI: Anouar Benali, Argonne National Laboratory
DOMAIN: Materials Science

High-Fidelity Simulation of Fusion Reactor Boundary Plasmas

PI: C.S. Chang, Princeton Plasma Physics Laboratory
DOMAIN: Physics

NWChemEx: Tackling Chemical, Materials, and Biochemical Challenges in the Exascale Era

PI: Thomas Dunning, Pacific Northwest National Laboratory
DOMAIN: Chemistry

Extreme-Scale Cosmological Hydrodynamics

PI: Katrin Heitmann, Argonne National Laboratory
DOMAIN: Physics

Extreme-Scale Unstructured Adaptive CFD

PI: Kenneth Jansen, University of Colorado at Boulder
DOMAIN: Engineering


Exascale Computational Catalysis

PI: David Bross, Argonne National Laboratory
DOMAIN: Chemistry

Dark Sky Mining

PI: Salman Habib, Argonne National Laboratory
DOMAIN: Physics

Data Analytics and Machine Learning for Exascale Computational Fluid Dynamics

PI: Ken Jansen, University of Colorado Boulder
DOMAIN: Engineering

Simulating and Learning in the ATLAS Detector at the Exascale

PI: James Proudfoot, Argonne National Laboratory
DOMAIN: Physics

Extreme-Scale In-Situ Visualization and Analysis of Fluid-Structure-Interaction Simulations

PI: Amanda Randles, Duke University and Oak Ridge National Laboratory
DOMAIN: Biological Sciences


Machine Learning for Lattice Quantum Chromodynamics

PI: William Detmold, Massachusetts Institute of Technology
DOMAIN: Physics

Enabling Connectomics at Exascale to Facilitate Discoveries in Neuroscience

PI: Nicola Ferrier, Argonne National Laboratory
DOMAIN: Biological Sciences

Many-Body Perturbation Theory Meets Machine Learning to Discover Singlet Fission Materials

PI: Noa Marom, Carnegie Mellon University
DOMAIN: Materials Science

Virtual Drug Response Prediction

PI: Rick Stevens, Argonne National Laboratory
DOMAIN: Biological Science

Accelerated Deep Learning Discovery in Fusion Energy Science

PI: William Tang, Princeton Plasma Physics Laboratory
DOMAIN: Physics