March 10, 2023
The U.S. government is dedicating a record amount of $25 billion as part of the 2024 budget to emerging technologies as the country looks to counter the technology threat from China. The budget includes billions of dollars earmarked to boost the supercomputing infrastructure, semiconductors, and cutting-edge technologies such as artificial intelligence and quantum computing. The technology... Read more…
August 27, 2020
Among a lengthy list of U.S. technology initiatives included in pending budget legislation is a proposed National Artificial Intelligence Initiative that would Read more…
January 16, 2020
The National Science Foundation (NSF) has been spared a President Trump-proposed budget cut that would have rolled back its funding to 2012 levels. Congress pas Read more…
February 23, 2018
On February 12, 2018, the Trump administration submitted its Fiscal Year 2019 (FY-19) budget to Congress. The good news for the U.S. exascale program is that th Read more…
July 27, 2017
In the federal budgeting world, “regular order” is a meaningful term that is fondly remembered by members of both the Congress and the Executive Branch. Reg Read more…
May 30, 2017
“No money shall be drawn from the Treasury, but in Consequence of Appropriations made by Law.” These words are from the U.S. Constitution (Article 1, Sectio Read more…
May 4, 2017
Bipartisan congressional negotiators reached an agreement this week on the 2017 fiscal year budget, which funds the government through September 30. The bill is, as Computing Research Association Policy Analyst Brian Mosley put it, "not great, but not terrible for science." Read more…
February 12, 2016
A drill down into the FY2017 budget released by the Obama administration on Tuesday brings to light important information about the United States' exascale pro 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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