May 10, 2023
Cloud providers are building armies of GPUs to provide more AI firepower. Google is joining the gang with a new supercomputer that has almost 2.5 times the number of GPUs than the world’s third-fastest supercomputer called LUMI. Google announced an AI supercomputer with 26,000 GPUs at its developer conference on Wednesday. Read more…
April 10, 2023
There are limits on the speed of how fast copper wires can move data between computers, and a transition to light speed will ultimately drive AI and high-performance computing forward. Every major chipmaker is in agreement that optical interconnects will be needed to reach zettascale computing in an energy-efficient way. That opinion was... Read more…
April 6, 2023
A new scientific paper from Google details the performance of its Cloud TPU v4 supercomputing platform, claiming it provides exascale performance for machine le Read more…
February 27, 2023
Google over the last few years has thrown shade at today's fastest supercomputers with dubious claims of achieving "quantum supremacy." The tech giant may be ba Read more…
February 20, 2023
Microsoft and Google are driving a major computing shift by bringing AI to people via search engines, and one measure of success may come down to the hardware a Read more…
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…
October 18, 2022
Spun out from Google last March, SandboxAQ is a fascinating, well-funded start-up targeting the intersection of AI and quantum technology. “As the world enter Read more…
September 14, 2022
When DeepMind, an Alphabet subsidiary, started off more than a decade ago, solving some most pressing research questions and problems with AI wasn’t at the top of the company’s mind. Instead, the company started off AI research with computer games. Every score and win was a measuring stick of success... 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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