Jan. 6, 2022 — As part of a series aimed at sharing best practices in preparing applications for Aurora, Argonne National Laboratory is highlighting researchers’ efforts to optimize codes to run efficiently on graphics processing units. As part of the Argonne Leadership Computing Facility’s (ALCF) Aurora Early Science Program, William Tang of the U.S Department … Continue reading Preparing for Aurora: Ensuring the Portability of Deep Learning Software to Explore Fusion Energy