Argonne Leadership Computing Facility

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Leadership Computing Resources

The ALCF provides users with access to supercomputing resources that are significantly more powerful than systems typically used for open scientific research.

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The ALCF is accelerating scientific discoveries in many disciplines, ranging from chemistry and engineering to physics and materials science.

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Growing the HPC Community

The ALCF is committed to providing training and outreach opportunities that prepare researchers to efficiently use its leadership computing systems, while also cultivating a diverse and skilled HPC workforce for the future.
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Accelerating Science

The Argonne Leadership Computing Facility enables breakthroughs in science and engineering by providing supercomputing resources and expertise to the research community.
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Support Center

The ALCF Support Center assists users with support requests related to their ALCF projects.

Help Desk
Hours: 9:00am-5:00pm CT M-F
Email: support@alcf.anl.gov
Guides
Featured: Get Started

Featured: MyALCF

Magnetic Field Amplification by a Nonlinear Electron Streaming Instability

Publications Physical Review Letters

Optimization of High-Entropy Alloy Catalyst for Ammonia Decomposition and Ammonia Synthesis

Publications The Journal of Physical Chemistry Letters

A Vorticity-Based Criterion to Characterise Leading Edge Dynamic Stall Onset

Publications Journal of Fluid Mechanics

Comparing Distributed Termination Detection Algorithms for Modern HPC Platforms

Publications International Journal of Networking and Computing

Fixed-angle conjectures for the quantum approximate optimization algorithm on regular MaxCut graphs

Publications Physical Review A

Task-Level Resilience: Checkpointing vs. Supervision

Publications International Journal of Networking and Computing

Electron Scattering on A=3 Nuclei from Quantum Monte Carlo Based Approaches

Publications Physical Review C

Data Parallel Python: Bringing oneAPI to Python

Events

Argonne-Illinois Tech Research Seminar Series

Events

Promoting computing research collaborations between Argonne and Illinois Tech

Using deep Learning to Parameterize Radiative Transfer in Numerical Weather Prediction

Events

A plot of precipitable water (gray scale) and precipitation rate (colors) in a simulation of the Earth's atmosphere.

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Leadership Computing Resources

Featured: Aurora

Computational Science

Featured: Engineering

Growing the HPC Community

Accelerating Science

Support Center

Featured: Get Started

Featured: MyALCF

Magnetic Field Amplification by a Nonlinear Electron Streaming Instability

Optimization of High-Entropy Alloy Catalyst for Ammonia Decomposition and Ammonia Synthesis

A Vorticity-Based Criterion to Characterise Leading Edge Dynamic Stall Onset

Comparing Distributed Termination Detection Algorithms for Modern HPC Platforms

Fixed-angle conjectures for the quantum approximate optimization algorithm on regular MaxCut graphs

Task-Level Resilience: Checkpointing vs. Supervision

Electron Scattering on A=3 Nuclei from Quantum Monte Carlo Based Approaches

Data Parallel Python: Bringing oneAPI to Python

Argonne-Illinois Tech Research Seminar Series

Using deep Learning to Parameterize Radiative Transfer in Numerical Weather Prediction