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ESnet6 Launches, Heralding a New Era in Scientific Networking
October 11, 2022
The launch of ESnet6 was announced at an event at Berkeley Lab this morning. ESnet – short for “energy sciences network” – is managed by Berkeley Lab, f Read more…
A Farewell to XSEDE: A Retrospective & Introduction to the ACCESS Program
July 22, 2022
John Towns, principal investigator of the Extreme Science and Engineering Discovery Environment (XSEDE), gave an overview of the soon-to-complete NSF-funded cyb Read more…
South African CHPC National Conference: Connecting, Engaging, and Securing Earth’s Quietest Places
July 31, 2020
The header image was captured over a 24-hour period—across all time zones—by a U.S. National Aeronautics and Space Administration (NASA) satellite. It illus Read more…
NSF Awards $10M to Extend Chameleon Cloud Testbed Project
September 19, 2017
The National Science Foundation has awarded a second phase, $10 million grant to the Chameleon cloud computing testbed project led by University of Chicago with Read more…
Need Data Science CyberInfrastructure? Check with RENCI’s xDCI Concierge
September 6, 2017
For about a year the Renaissance Computing Institute (RENCI) has been assembling best practices and open source components around data-driven scientific researc Read more…
NSF Project Sets Up First Machine Learning Cyberinfrastructure – CHASE-CI
July 25, 2017
Earlier this month, the National Science Foundation issued a $1 million grant to Larry Smarr, director of Calit2, and a group of his colleagues to create a comm Read more…
NSF Seeks Input on Cyberinfrastructure Advances Needed
January 12, 2017
In cased you missed it, the National Science Foundation posted a “Dear Colleague Letter” (DCL) late last week seeking input on needs for the next generation Read more…
Jetstream, the NSF-funded “Science” Cloud, Launches Tomorrow
August 31, 2016
Jetstream, the expansive NSF-funded cyberinfrastructure project intended in large measure to serve the long tail of science will officially launch tomorrow (9/1 Read more…
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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.
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