Moderator: Ignacio Laguna (Lawrence Livermore National Laboratory)
Panelists: David H. Bailey (University of California, Davis), Osman Unsal (Barcelona Supercomputing Center (BSC)), Harshitha Menon (Lawrence Livermore National Laboratory), Hartwig Anzt (Karlsruhe Institute of Technology), Radha Venkatagiri (Oregon State University)
Abstract: With the end of Dennard scaling and the slowdown of Moore's law, approximate computing (AC) has emerged as an attractive option to improve performance and energy efficiency by relaxing correctness and allowing errors. Several AC techniques have been proposed, from hardware-level techniques (e.g., voltage scaling) to software-level techniques (e.g., memorization and mixed-precision). These methods have proven to be helpful in workloads that are not traditional in HPC, such as image and video processing, which can naturally tolerate error. However, how feasible is it to apply AC methods to HPC scientific applications? The panel gathers experts in different AC fields to address this question. Driven by their vast experience, panelists will express views on the most crucial problems of adopting AC in HPC. Attendees will benefit from discussions with the panelists and will provide feedback.