March 31, 2023
SC23 General Chair Dorian C. Arnold is enthusiastic about this year's conference, which will take place Nov. 12-17 in Denver, Colo. Our exclusive interview with Arnold covers his history with the annual event, what's in store for attendees, and his insights into the HPC landscape writ large. In addition to his work with SC, Arnold is also... Read more…
March 13, 2023
In celebration of Women’s History Month, SC23 is spending the month of March profiling more than 30 women in the field. Here’s what the project’s lead vol Read more…
February 8, 2023
During the awards ceremony at SC22 in Dallas, one reveal flew a bit under the radar amid the celebration: the introduction of a new Gordon Bell Prize that ACM P 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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