March 13, 2023
CSC, Finland’s scientific IT center, is now most famous for its LUMI system, launched in partnership with EuroHPC and landing third on the most recent Top500 Read more…
February 14, 2023
Each November, HPCwire’s readers and editors recognize dozens of individuals and organizations across more than 20 very serious award categories, celebrating Read more…
February 1, 2023
“LUMI is officially here!” proclaimed the headline of a blog post written by Pekka Manninen, director of science and technology for CSC, Finland’s state-o Read more…
January 25, 2023
The immediate impacts of climate change and land-use change are severe enough, but increasingly, researchers are warning that large enough changes can then snow Read more…
October 19, 2022
In late 2020, the European Union announced plans for its Destination Earth (“DestinE”) moonshot project to create multiple digital twins of Earth, including Read more…
June 8, 2022
Back in 2008, the U.S. Defense Advanced Research Projects Agency (DARPA) set an ambitious target: an exascale supercomputer in a 20-megawatt envelope. That targ Read more…
December 17, 2021
2021 marked the 18th annual HPCwire Readers’ and Editors’ Choice Awards. Coming off a tumultuous 2020, this year marked something of a return to normalcy: m Read more…
May 20, 2021
About two years ago, the EuroHPC Joint Undertaking (JU) selected eight host countries for its first eight systems. Now, those trees are bearing fruit – Sloven Read more…
Five Recommendations to Optimize Data Pipelines
When building AI systems at scale, managing the flow of data can make or break a business. The various stages of the AI data pipeline pose unique challenges that can disrupt or misdirect the flow of data, ultimately impacting the effectiveness of AI storage and systems.
With so many applications and diverse requirements for data types, management systems, workloads, and compliance regulations, these challenges are only amplified. Without a clear, continuous flow of data throughout the AI data lifecycle, AI models can perform poorly or even dangerously.
To ensure your AI systems are optimized, follow these five essential steps to eliminate bottlenecks and maximize efficiency.
Karlsruhe Institute of Technology (KIT) is an elite public research university located in Karlsruhe, Germany and is engaged in a broad range of disciplines in natural sciences, engineering, economics, humanities, and social sciences. For institutions like KIT, HPC has become indispensable to cutting-edge research in these areas.
KIT’s HoreKa supercomputer supports hundreds of research initiatives including a project aimed at predicting when the Earth’s ozone layer will be fully healed. With HoreKa, projects like these can process larger amounts of data enabling researchers to deepen their understanding of highly complex natural processes.
Read this case study to learn how KIT implemented their supercomputer powered by Lenovo ThinkSystem servers, featuring Lenovo Neptune™ liquid cooling technology, to attain higher performance while reducing power consumption.
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