November 16, 2022
Europe’s sovereign approach to exascale computing is complicating plans for U.S. chipmakers to breakthrough in the market, and in the process, empowering local chipmakers. For one, a European chip startup called SiPearl is emerging as an early... Read more…
November 12, 2022
Chipmakers regularly indulge in a game of brinkmanship, with an example being Intel and AMD trying to upstage one another with server chip launches this week. But each of those companies are in different positions, with AMD playing its traditional role of a scrappy underdog trying to unseat the behemoth Intel... Read more…
April 12, 2021
Today at Nvidia’s annual spring GPU Technology Conference (GTC), held virtually once more due to the pandemic, the company unveiled its first ever Arm-based CPU, called Grace in honor of the famous American programmer Grace Hopper. The announcement of the new... Read more…
November 27, 2020
As HPE’s chief technology officer for artificial intelligence, Dr. Eng Lim Goh devotes much of his time talking and consulting with enterprise customers about Read more…
July 31, 2020
A machine programming framework for heterogeneous computing championed by Intel Corp. and university partners is built around an automated engine that analyzes Read more…
December 1, 2015
Contrary to conventional thinking, GPUs are often not the best vehicles for big data visualization. In this commentary, I discuss several key technical reasons Read more…
Making the Most of Today’s Cloud-First Approach to Running HPC and AI Workloads With Penguin Scyld Cloud Central™
Bursting to cloud has long been used to complement on-premises HPC capacity to meet variable compute demands. But in today’s age of cloud, many workloads start on the cloud with little IT or corporate oversight. What is needed is a way to operationalize the use of these cloud resources so that users get the compute power they need when they need it, but with constraints that take costs and the efficient use of existing compute power into account. Download this special report to learn more about this topic.
Data center infrastructure running AI and HPC workloads requires powerful microprocessor chips and the use of CPUs, GPUs, and acceleration chips to carry out compute intensive tasks. AI and HPC processing generate excessive heat which results in higher data center power consumption and additional data center costs.
Data centers traditionally use air cooling solutions including heatsinks and fans that may not be able to reduce energy consumption while maintaining infrastructure performance for AI and HPC workloads. Liquid cooled systems will be increasingly replacing air cooled solutions for data centers running HPC and AI workloads to meet heat and performance needs.
QCT worked with Intel to develop the QCT QoolRack, a rack-level direct-to-chip cooling solution which meets data center needs with impressive cooling power savings per rack over air cooled solutions, and reduces data centers’ carbon footprint with QCT QoolRack smart management.
© 2023 HPCwire. All Rights Reserved. A Tabor Communications Publication
Reproduction in whole or in part in any form or medium without express written permission of Tabor Communications, Inc. is prohibited.