AMD Boosts Credentials with HPC Wins
Authors: Michael Feldman, Earl Joseph
Publication Date: April 2020
Length: 4 pages
Over the last three years, AMD has become a force to be reckoned with in the high performance computing market. The recent announcements by AMD that its future AMD EPYC™ CPUs and AMD Radeon Instinct™ GPUs will be used to power the El Capitan supercomputer at Lawrence Livermore National Laboratory (LLNL) in 2023 and the Frontier supercomputer at Oak Ridge National Laboratory (ORNL) in 2021, demonstrate that the chipmaker may be able to effectively compete against other vendors for inclusion in some of the most powerful HPC systems in the world.
ORNL Announces Newest Leadership HPC: It’s More Than Just Exaflops
The Department of Energy at Oak Ridge National Laboratory (ORNL) recently announced plans for the development of a 1.5 exaflops system called Frontier to be delivered in 2021. US HPC maker Cray and chip maker AMD are the two key US commercial partners in this effort. Despite numerous press articles centered on the 1.5 exaflops peak performance of Frontier, ORNL's original RFP released in April of 2018 clearly called out the diverse workload requirements that Frontier would have to successfully handle that span the traditional modeling and simulation sector, big data analysis, and AI applications, while demonstrating a 50X improvement in solving key DOE science problems that today run at the 20 petaflops level. To meet those ambitious goals, strong support from DOE's companion $1.7 billion Exascale Computing Project (ECP) will be critical.
May 2019 | Quick Take
The AI Hardware Summit: A Recap
Alex Norton, Bob Sorensen, Steve Conway and Earl Joseph
This inaugural, two-day AI Hardware summit held in Mountain View, California, at the Computer History Museum, brought together researchers, vendors, and users to explore the development of the AI ecosystem from a hardware perspective. Large companies, startups, and analysts joined to hear over 30 speakers and roundtables. Although the overall theme was all AI hardware, many of the presentations focused on AI processors and the work that is being done to design hardware to further the development of the evolving and growing field of AI, machine learning, and deep learning.
November 2018 | Quick Take