January 21, 2023
Fusion energy is having a moment – an increasingly fruitful one – but as radio astronomers and particle physicists know, bigger and better experiments and s Read more…
December 21, 2022
On December 5th, the research team at the National Ignition Facility (NIF) at Lawrence Livermore National Laboratory (LLNL) achieved a historic win in energy sc Read more…
March 30, 2022
From weather sensors and autonomous vehicles to electric grid monitoring and cloud gaming, the world’s edge computing is getting increasingly complex — but the world of HPC hasn’t necessarily caught up to these rapid innovations at the edge. At a panel at Nvidia’s virtual GTC22 (“HPC, AI, and the Edge”), five experts discussed how leading-edge HPC applications... Read more…
March 25, 2022
With climate change accelerating and fossil fuel supplies proving increasingly contentious, ensuring a secure supply of clean energy is top-of-mind for many res Read more…
January 15, 2022
The exascale era has brought with it a bevy of fusion energy simulation projects, aiming to stabilize the notoriously delicate—and so far, unmastered—clean Read more…
August 19, 2021
As the world barrels toward a dark climate future, many people’s hopes increasingly rest with major technological breakthroughs – including, perhaps most fa Read more…
May 26, 2021
Inertial confinement fusion (ICF) experiments is a speculative method of fusion energy generation that would compress a fuel pellet to generate fusion energy ju Read more…
February 25, 2021
Energy researchers have been reaching for the stars for decades in their attempt to artificially recreate a stable fusion energy reactor. If successful, such a 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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