Public and Private Sectors Team up to Solve HPC Software Problem 

November 15, 2023

Software implementation in high-performance computing is getting more fragmented as organizations opt for tools in their walled garden environments.  Howeve Read more…

Lessons from LLVM: An SC21 Fireside Chat with Chris Lattner

December 27, 2021

Today, the LLVM compiler infrastructure world is essentially inescapable in HPC. But back in the 2000 timeframe, LLVM (low level virtual machine) was just getting its start as a new way of thinking about how to overcome shortcomings in the Java Virtual Machine. At the time, Chris Lattner was a graduate student of... Read more…

Intel Completes LLVM Adoption; Will End Updates to Classic C/C++ Compilers in Future

August 10, 2021

Intel reported in a blog this week that its adoption of the open source LLVM architecture for Intel’s C/C++ compiler is complete. The transition is part of In Read more…

LLVM Holds the Keys to Exascale Computing

July 13, 2021

The recent proliferation of new hardware technologies has galvanized the high-performance computing (HPC) community and created the ability to deliver the natio Read more…

ECP Leads the Way to Cross-Platform Tested and Verified Compilers for HPC and Exascale Architectures

June 9, 2021

The Exascale Computing Project (ECP) is working to combine two key technologies, LLVM and continuous integration (CI), to ensure that current and future compile Read more…

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Whitepaper

Penguin Computing Scyld Cloud Central™: A New Cloud-First Approach to HPC and AI Workloads

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.

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QCT POD- An Adaptive Converged Platform for HPC and AI

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.

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