…But chipmaker still does not have an integrated product strategy, which puts the company behind AMD and Nvidia. Intel finally has a full complement of server and PC chips it will release in th …
When the EuroHPC Summit was held last week in Gothenburg, there was a distinct shift in tone for the maturing supercomputing play. With LUMI and Leonardo – plus four other petascale systems – …
In what’s become a regular GPU Technology Conference feature, Bill Dally, Nvidia chief scientist and SVP of research, provides a glimpse into how Nvidia organizes R&D and what are a few of …
Researchers looking to create a foundation for a ChatGPT-style application now have an affordable way to do so. Cerebras is releasing open-source learning models for researchers with the ingredie …
Find out which 12 HPC luminaries are being recognized this year for driving innovation within their particular fields.
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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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