May 30, 2023
With ISC23 now in the rearview mirror, let’s get back to the results from the ASC23 Student Cluster Competition. In our last articles, we looked at the compet Read more…
May 18, 2023
The ASC23 cluster competition was held in a basketball stadium on the campus of the University of Science and Technology of China, located in Hefei, China – Read more…
May 17, 2023
The tenth edition of the Asian Supercomputing Challenge took place last week in Hefei, China, and it was tougher than ever. 24 university teams (20 on site, 4 Read more…
April 24, 2023
February 14th to April 15th – it’s been a long run for the 2023 Winter Classic Student Cluster Competition. 63 students from HBCU and HSI schools learned ha Read more…
April 12, 2023
Hands off keyboards, the last computational challenge in the 2023 Winter Classic student cluster competition has been completed, the scores have been compiled, Read more…
April 8, 2023
A wintery mix with a chance for scattered dependencies was the forecast as students tackled the NASA WRF Challenge in the 2023 Winter Classic Invitational Stude Read more…
April 7, 2023
The close of the 2023 Winter Classic Invitational Student Cluster Competition is coming up fast, and I have to get some material out to you, our vast viewing au Read more…
March 14, 2023
In our most recent update, “Triumph and Tragedy with HPL/HPCG”, we detailed how our dozen 2023 Winter Classic Invitational cluster competition teams dealt with their Linpack/HPCG module, mentored by HPE. In this episode of our incredibly popular 2023 Winter Classic Studio Update Show, we... 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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