May 22, 2023
Intel has finally provided specific details on wholesale changes it has made to its supercomputing chip roadmap after an abrupt reversal of an ambitious plan to Read more…
May 21, 2023
At ISC this week, Nvidia announced plans for a new hybrid classical-quantum computing lab with partners Jülich Supercomputing Centre and ParTec. The new lab is Read more…
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…
March 5, 2023
Intel CEO Pat Gelsinger is taking a no-holds-barred approach to cutting costs as he whips the company back into financial shape. Intel has already exited seven businesses, and recently made wholesale graphics processors changes by axing products and changing its enterprise GPU roadmap. Intel has scrapped a supercomputer GPU codenamed Rialto Bridge, which was advertised... 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…
October 18, 2022
Oracle is bringing Nvidia's AI Enterprise software suite alongside thousands of its latest GPUs to its cloud infrastructure, which could fuel the chipmaker’s Read more…
July 27, 2022
Graphics processors are taking on a new role beyond gaming and artificial intelligence – they are now serving as surrogate quantum computers until the real hardware arrives. The Jülich Supercomputing Centre is using GPUs and a software toolkit from Nvidia to emulate quantum computers and research... Read more…
June 7, 2022
As quantum computing comes closer to mainstream, it's universally agreed that these systems won't replace classical computing. That raises the question: where e 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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