May 2, 2013
It's been a notable week for Intel, with the announcement of two new leadership positions, including the pushing of 30-year veteran engineer, Brian Krzanich into the top slot. With this in mind, we revisit this week's discussion led by Intel's John Hengeveld on where the trends of big data and HPC tend to converge on the interconnect and its role in... Read more…
July 8, 2010
Univa UD recently named former company product manager, Gary Tyreman its new CEO in order to transition the company from its grid roots into the familiar age of the cloud. Tyreman discusses the company's progress, both in the present and down the line with emphasis on Univa's HPC focus. Read more…
November 2, 2009
Former AMD CEO steps down amid insider trading controversy. 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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