|Speaker: Kai Zhao
Date: Dec 1, 2023, 2:15 – 3:10 PM
Abstract: The rapid development of artificial intelligence technologies and the explosive growth of data in petascale and exascale systems have brought critical challenge s to high-performance computing (HPC) systems. Today’s scientific simulations ar e producing vast volumes of data that cannot be stored and transferred efficient ly because of limited storage capacity, parallel I/O bandwidth, and network band width. Error-bounded lossy compression is becoming one of the most critical tech niques for resolving the big scientific data issue, in that it can significantly reduce the scientific data volume while guaranteeing that the reconstructed dat a is valid for users because of its compression-error-bounding feature. In this talk, I will present the SZ lossy compressor which reduces the data volume effic iently for various scientific applications. SZ is in the Exascale Computing Proj ect (ECP) and has been widely recognized and used by scientists and researchers from world-wide research institutions and universities, such as Argonne National Laboratory, Lawrence Livermore National Laboratory, Lawrence Berkeley National Laboratory, Los Alamos National Laboratory, Oak Ridge National Laboratory, Total S.A, Saudi Aramco, etc.
Location and Zoom link: 307 Love, or https://fsu.zoom.us/j/95555814225