December 8, 2022
The U.S. Department of Defense wielded its JEDI powers to procure public cloud services with a diplomatic end to a feud between Amazon and Google to win the multi-billion dollar contract. The DoD broke up a $9 billion contract between the top four cloud providers – Google, Amazon, Microsoft and Oracle – for the Joint Warfighting Cloud Capability initiative, which will bring the defense branches – Air Force, Army... Read more…
November 30, 2022
AWS has announced three new Amazon Elastic Compute Cloud (Amazon EC2) instances powered by AWS-designed chips, as well as several new Intel-powered instances � Read more…
August 31, 2022
The Arm chip architecture took the mobile world by storm in 2007 after the release of the first iPhone. Just two years later, an Amazon executive who now leads the company’s semiconductor development, believed Arm would eventually be a big part of server-side computing. “I’ve observed, over the years what happens in mobile ends up happening in servers. Read more…
March 10, 2022
Add Amazon Web Services to the growing list of companies (tech and otherwise) that are curtailing business with Russia in opposition to President Putin’s invasion of Ukraine. As reported in the New York Times and then by Amazon itself, Amazon Web Services is blocking new sign-ups from Russia and Belarus. Existing customers are not impacted. “We’ve suspended shipment of retail... Read more…
October 27, 2021
As machine learning becomes a dominating use case for local and cloud computing, companies are racing to provide solutions specifically optimized and accelerate Read more…
September 10, 2021
Earth’s climate is, to put it mildly, not in a good place. In the wake of a damning report from the Intergovernmental Panel on Climate Change (IPCC), scientis Read more…
December 10, 2020
It’s been a big year for Arm. The new top supercomputer in the world, Fugaku, runs on the company’s chips, and just a few months ago, the chipmaker struck a $40 billion deal to sell itself to Nvidia, creating the potential for a new juggernaut able to go toe-to-toe with Intel and AMD. Now, Arm... Read more…
September 10, 2020
Amazon, Google, Intel and Accenture will contribute toward a $160 million partnership for the next round of eight AI Research Institutes scheduled for creation Read more…
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.
Two recent studies show how Ansys Gateway powered by AWS can balance run times and costs, making it a compelling solution for automotive development.
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.
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