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Atos Launches ‘Eviden’ Brand Ahead of Planned Split
About a year ago, French IT giant Atos announced that it was exploring plans to split itself down the middle, separating its computing infrastructure business – which remains under the “Atos� …
Tasty CHIPS – New MEC Program to Expand US Prototyping Capabilities Gains Steam
Sometime later this year, perhaps around July, the Department of Defense is expected to announce the sites and focus of up to nine hubs associated with the Microelectronics Commons (MEC) program. …
Inside NCSA’s Nightingale Cluster, Designed for Sensitive Data
March 10, 2023
The emergence of Covid in 2020 saw an explosion in HPC-powered health research. As the pandemic raged on, though, one limiting factor became increasingly clear: Read more…
Top HPC Players: It’s Time to Get Serious About Security
March 9, 2023
Time’s up: nearly everyone agrees it’s about time to become serious about bringing security safeguards to high-performance computing systems, which has been Read more…
Fujitsu Study Says Quantum Decryption Threat Still Distant
January 23, 2023
Global computer and chip manufacturer Fujitsu today reported that a new study performed on its 39-qubit quantum simulator suggests it will remain difficult for Read more…
Top HPC Players Creating New Security Architecture Amid Neglect
January 20, 2023
Security of high-performance computers is being neglected in the pursuit of horsepower, and there are concerns that the ignorance may be costly if safeguards ar Read more…
Intel’s Rental Service on Chips Could Face Buyer Backlash
January 11, 2023
Intel is bringing subscription and rental services to semiconductors as it explores new business models, but it remains to be seen if buyers warm up to the idea of paying extra to unlock features on a chip. Intel is bringing an "on-demand" feature to its new Xeon CPUs codenamed Sapphire Rapids, which the company launched on Tuesday after long delays. The on-demand feature involves paying a fee to activate... Read more…
The Quantum Kaleidoscope – 2022 Highlights and What to Watch in 2023
January 9, 2023
Making sense of today’s quantum computing landscape is challenging. IBM’s research chief Dario Gil says quantum computing writ large is now undeniably an industry. Jim Clarke, Intel’s quantum hardware chief, says hold your horses – we’re still 10-15 years from a fault-tolerant machine. Markus Pflitsch, founder and CEO of Swiss start-up Terra Quantum/QMware, says why wait, his company is delivering quantum... Read more…
IBM Quantum Summit: Osprey Flies; Error Handling Progress; Quantum-centric Supercomputing
December 1, 2022
Part scorecard, part grand vision, IBM’s annual Quantum Summit held last month is a fascinating snapshot of IBM’s progress, evolving technology roadmap, and Read more…
Blueprints to Shake up Next-generation Server Designs Emerge at OCP Summit
October 24, 2022
A new generation of designs ready to shake up conventional server architecture emerged at the recent Open Compute Project Summit, where Google, Facebook and Mic Read more…
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The increasing complexity of electric vehicles result in large and complex computational models for simulations that demand enormous compute resources. On-premises high-performance computing (HPC) clusters and computer-aided engineering (CAE) tools are commonly used but some limitations occur when the models are too big or when multiple iterations need to be done in a very short term, leading to a lack of available compute resources. In this hybrid approach, cloud computing offers a flexible and cost-effective alternative, allowing engineers to utilize the latest hardware and software on-demand. Ansys Gateway powered by AWS, a cloud-based simulation software platform, drives efficiencies in automotive engineering simulations. Complete Ansys simulation and CAE/CAD developments can be managed in the cloud with access to AWS’s latest hardware instances, providing significant runtime acceleration.
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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.
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