May 31, 2023
The end goal is in sight for the multi-institutional Exascale Computing Project (ECP), which launched in 2016 with a mandate from the Department of Energy (DOE) Read more…
May 3, 2021
Treatment for a disease like cancer is much different from treatment for a disease like COVID-19: in the COVID case, a drug or vaccine needs to spread throughou Read more…
April 1, 2021
Aurora, to be hosted by Argonne National Laboratory, is one of three planned exascale-class systems in the U.S. While the Intel-led system has encountered a variety of conceptual transformations (it was originally planned as a pre-exascale system) and setbacks... Read more…
October 15, 2020
In this bimonthly feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programmin Read more…
November 11, 2019
Dynamic partial-wave spectroscopic (PWS) microscopy allows researchers to observe intracellular structures as small as 20 nanometers – smaller than those visi Read more…
October 14, 2019
In this bimonthly feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programm Read more…
August 13, 2019
In the fight against cancer, early prediction, which drastically improves prognoses, is critical. Now, new research by a team from Northwestern University – a Read more…
June 22, 2015
Powerful supercomputers around the country continue to push advances in critical areas, such as cancer research. One of the latest such success stories comes ou 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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