May 29, 2023
Nvidia launched a new Ethernet-based networking platform – the Nvidia Spectrum-X – that targets generative AI workloads. Based on tight coupling of the Nvi Read more…
May 23, 2023
Jay Lofstead from Sandia National Laboratories and Jakob Luettgau from the University of Tennessee gave a highly audience interactive session on Ethics in AI an Read more…
April 20, 2023
Famously, a team of researchers from the University of Massachusetts, Amherst, concluded in 2019 that training a single large AI model could emit five times the Read more…
April 12, 2023
Generative AI is taking the tech world – and the broader world – by storm, but relatively little word has come out of the major supercomputer centers amid t Read more…
April 5, 2023
MLCommons today released the latest MLPerf Inferencing (v3.0) results for the datacenter and edge. While Nvidia continues to dominate the results – topping al Read more…
March 28, 2023
Researchers looking to create a foundation for a ChatGPT-style application now have an affordable way to do so. Cerebras is releasing open-source learning models for researchers with the ingredients necessary to cook up their own ChatGPT-AI applications. The open-source tools include seven models that form a learning... Read more…
March 23, 2023
I recently wrote about my experience with interviewing ChatGPT here. As promised, in this follow-on and conclusion of my interview, I focus on Fortran and other languages. All in good fun. I hope you enjoy the conclusion of my interview. After my programming language questions, I conclude with a few notes... Read more…
March 21, 2023
Nvidia’s Hopper-generation H100 GPU is continuing its slow march toward “current-generation.” After Nvidia announced that the H100 was in “full producti 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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