April 10, 2023
About a year and a half ago, HPE announced that it had been selected to build the next supercomputer for the DOE’s National Renewable Energy Laboratory (NREL) Read more…
November 12, 2022
It’s only 25 miles from NCAR to NREL, so I have time to gas up, get a sandwich, and even catch a car wash before my tour stop at NREL. Feeling refreshed, I pu Read more…
December 9, 2021
From European HPC experts pondering “can fast be green?” to new milestones on the Green500 list, sustainability certainly had a moment at the hybrid SC21 conference. And it’s no wonder: the exascale era is here, and power consumption for HPC is skyrocketing even as efficiency is driven to its extremes. At SC21, another session – “HPC’s Growing Sustainability Challenges and Emerging Approaches” – tackled the topic... Read more…
July 10, 2021
The ExaWind project describes itself in terms of terms like wake formation, turbine-turbine interaction and blade-boundary-layer dynamics, but the pitch to the Read more…
March 7, 2019
In 2018, NREL and Sandia announced their intention to study the energy and cost savings of Aquila’s Aquarius cooling system in a real-world environment by installing a test system (“Yacumama”) at NREL. Now, nearly a year later, the partners have revealed the results of the test. Read more…
August 14, 2018
The U.S. Department of Energy (DOE) National Renewable Energy Laboratory (NREL) has contracted with Hewlett Packard Enterprise (HPE) for a new 8-petaflops (peak Read more…
August 30, 2017
A report released last week by the DOE’s National Renewable Energy Laboratory (NREL) asserts that supercomputing-led scientific advances could cut the unsubsidized cost of wind energy in half by the year 2030. Read more…
October 9, 2014
Wind power is is on track to achieve cost parity with fossil fuels thanks in part to the open-source software tool Simulator for Wind Farm Applications (SOWFA) 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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