February 16, 2021
With the one-year mark of the pandemic in the U.S. rapidly approaching and vaccinations ramping up, decision-makers and stakeholders are beginning to look back Read more…
June 10, 2020
The rise of AI – machine and deep learning – in life sciences has stirred the same excitement and same ‘fits-and-starts’ reality as elsewhere. Today, AI is mostly used in two areas: 1) embedded in life science instruments such as cryo-electron microscopes where it assists in feature recognition and lies largely hidden from users, and... Read more…
May 20, 2020
Given the disruption caused by the COVID-19 pandemic and the massive enlistment of major HPC resources to fight the pandemic, it is especially appropriate to re Read more…
April 30, 2020
Not surprisingly the scramble to find treatments for COVID-19 is making productive use of AI. This week Fernanda Foertter, formerly of Nvidia and now a consult Read more…
March 25, 2020
Supercomputing, big data and artificial intelligence are crucial tools in the fight against the coronavirus pandemic. Around the world, researchers, corporation Read more…
February 25, 2019
Weary of the constant din of AI hype. So is Ari Berman, vice president and general manager of consulting services for BioTeam, a research computing consultancy specializing in life sciences. “Every vendor is selling AI. I think it has become the gluten-free tag of life sciences because it is everywhere.... Read more…
February 21, 2019
For the past few years HPCwire and leaders of BioTeam, a research computing consultancy specializing in life sciences, have convened to examine the state of HPC (and now AI) use in life sciences. Without HPC writ large, modern life sciences research would quickly grind to a halt. It’s true most life sciences research computing... Read more…
October 5, 2015
Sifting through the vast treasure trove of data spilling from modern life science instruments is perhaps the defining challenge for biomedical research today. N 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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