SC23 HPC Student Cluster Smackdown

November 21, 2023

Since 2007, the Student Cluster Competition (SCC) has provided an international multi-day contest for the best and brightest university HPC teams. This year, th Read more…

ExaFLOPS; You Keep Using That Word

October 12, 2023

A recent article on Tom's Hardware began with the headline "China Wants 300 ExaFLOPS of Compute Power by 2025." Intrigued, further reading finds the following l Read more…

Another Top 5 Trends in HPC for the First Six Months of 2023

July 17, 2023

Editor’s note; Recently Google's Bard provided what it considered the Top 5 Trends in HPC for the First 6 Months of 2023 to HPCwire. While the answers Read more…

Top 5 Trends in HPC for the First Six Months of 2023  

July 12, 2023

Editor’s note; In light of recent updates to  Google’s Privacy Policy, “For example, we use publicly available information to help train Google’s AI mo Read more…

MLPerf Training 3.0 Showcases LLM; Nvidia Dominates, Intel/Habana Also Impress

June 28, 2023

As promised, MLCommons added a large language model (based on GPT-3) to its MLPerf training suite (v3.0) and released the latest round of results yesterday. Onl Read more…

HPC Market will Reach $33B in 2023 and Pass $50B by 2026 – Hyperion Research

June 7, 2023

Perhaps the most interesting slide at Hyperion Research’s annual ISC breakfast HPC market update was one without numbers, presented by research director Mark Read more…

HPC Career Notes: May 2023 Edition

May 1, 2023

In this monthly feature, we’ll keep you up-to-date on the latest career developments for individuals in the high-performance computing community. Whether it� Read more…

MLPerf Inference 3.0 Highlights – Nvidia, Intel, Qualcomm and…ChatGPT

April 5, 2023

MLCommons today released the latest MLPerf Inferencing (v3.0) results for the datacenter and edge. While Nvidia continues to dominate the results – topping al 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.

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