September 26, 2022
Since 2017, plans for the Leadership-Class Computing Facility (LCCF) have been underway. Slated for full operation somewhere around 2026, the LCCF’s scope ext Read more…
May 23, 2022
The Texas Advanced Computing Center (TACC) at The University of Texas at Austin passed to the next phase of the planning process for the Leadership-Class Computing Facility (LCCF), a process that has many approval stages and will take about four more years. If ultimately awarded for construction, the LCCF will serve as the leading facility for advanced computing for the U.S. academic open science community... Read more…
April 14, 2022
During a talk for the Ken Kennedy Institute’s 2022 Energy High Performance Computing Conference, Dan Stanzione, executive director of the Texas Advanced Compu 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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