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Purdue University receives $10M for Anvil supercomputer

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Purdue University will soon house Anvil, a powerful supercomputer, thanks to a $10 million reward from the National Science Foundation, according to a press release on Friday. Anvil will be able to support a slew of computing capabilities and data-intensive research, from regular high-performance computations to advanced artificial intelligence (AI) applications. This supercomputer will boost the capacity available to the National Science Foundation's Extreme Science and Engineering Discovery Environment (XSEDE), which serves tens of thousands of researchers across the US and has been a partner to Purdue for nine years. Anvil will begin production in 2021, serving researchers for five years, enabling a wide range of research in areas such as fluid dynamics and bioinformatics, and also for data science, artificial intelligence, and machine learning applications. "The name'Anvil' reflects the Purdue Boilermakers' strength and workmanlike focus on producing results and the Anvil supercomputer will enable important discoveries across many different areas of science and engineering," said Preston Smith, executive director of research computing and a co-PI on the project, in the release.


West Big Data Hub at SDSC to Partner for Data Storage Network under New NSF Grant

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The West Big Data Innovation Hub (WBDIH) at the San Diego Supercomputer Center (SDSC) at UC San Diego is one of four regional big data hubs partner sites awarded a $1.8 million grant from the National Science Foundation (NSF) for the initial development of a data storage network during the next two years. Other partners include Johns Hopkins University and University of Chicago, awarded a $300K EAGER for Open Storage Network (OSN) software. The team will combine its expertise, facilities, and research challenges to develop the OSN. The demonstration project will result in the design of a larger, low-cost, scalable national system capable of being replicated across many universities. The OSN will enable national collaborations and allow academic researchers across the nation to share their data more efficiently than ever before, according to the NSF announcement.


SDSC, UC San Diego Awarded Two NSF Convergence Accelerator Grants

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Researchers at the San Diego Supercomputer Center at UC San Diego and UC San Diego School of Medicine have received two National Science Foundation (NSF) planning grants worth a combined $2 million under a new NSF initiative to invest in research collaborations between academia, industry, government and communities that enable capabilities beyond what is currently possible in either the private or public sectors. Called Convergence Accelerator awards, the first set of grants has been awarded to research teams, according to a recent NSF release. These projects will evaluate how employers can use sophisticated artificial intelligence tools to connect with the workers they need, while seeking ways to develop the future U.S. workforce with the universities that will educate people and the companies that will employ them. A total of 43 new awards totaling $39 million will support projects across the country. Both grants, which support one of NSF's'Big Ideas' called Harnessing the Data Revolution, are focused on the area of Open Knowledge Networks, which pool many types of information and ideas so they can be accessed and leveraged to create new understanding.


Supercomputers Pave the Way for New Machine Learning Approach

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Newswise -- According to a release issued earlier this month by the Los Alamos National Laboratory (LANL), researchers have developed a machine learning approach called transfer learning that lets them model novel materials by learning from data collected about millions of other compounds. The new approach can be applied to new molecules in milliseconds, enabling research into a far greater number of compounds over much longer timescales. The new technique, called ANI-1ccx potential, promises to advance the capabilities of researchers in many fields and improve the accuracy of machine learning-based potentials in future studies of metal alloys and detonation physics. "Our quantum mechanical calculations to create ANI-1ccx potential were conducted over two years with time split on the Comet supercomputer at the San Diego Supercomputer Center and the Badger supercomputer at LANL," said Olexandr Isayev, paper author and a pharmacy professor at the University of North Carolina at Chapel Hill. "We chose these two supercomputers to train our neural networks as there are few machines that can run these – due to the high memory and core requirements."


Supercomputers Pave the Way for New Machine Learning Approach

#artificialintelligence

According to a release issued earlier this month by the Los Alamos National Laboratory (LANL), researchers have developed a machine learning approach called transfer learning that lets them model novel materials by learning from data collected about millions of other compounds. The new approach can be applied to new molecules in milliseconds, enabling research into a far greater number of compounds over much longer timescales. The new technique, called ANI-1ccx potential, promises to advance the capabilities of researchers in many fields and improve the accuracy of machine learning-based potentials in future studies of metal alloys and detonation physics. "Our quantum mechanical calculations to create ANI-1ccx potential were conducted over two years with time split on the Comet supercomputer at the San Diego Supercomputer Center and the Badger supercomputer at LANL," said Olexandr Isayev, paper author and a pharmacy professor at the University of North Carolina at Chapel Hill. "We chose these two supercomputers to train our neural networks as there are few machines that can run these – due to the high memory and core requirements."