NREL’s Kestrel Supercomputer Begins to Land

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…

2022 Road Trip Tour Stop #4: NREL

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…

With a Carbon Footprint Like HPC’s, It Matters When and Where You Step

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…

ExaWind Prepares for New Architectures, Bigger Simulations

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…

Aquarius Cold Plate Cooling System Tested by NREL, Sandia

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…

NREL ‘Eagle’ Supercomputer to Advance Energy Tech R&D

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…

Unlocking Wind’s Potential: Supercomputing’s Grand Challenge

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…

Supercomputing Drives Wind Power Efficiency

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…

  • arrow
  • Click Here for More Headlines
  • arrow

Whitepaper

Powering Up Automotive Simulation: Why Migrating to the Cloud is a Game Changer

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.

Download Now

Sponsored by ANSYS

Whitepaper

How to Save 80% with TotalCAE Managed On-prem Clusters and Cloud

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.

Download Now

Sponsored by TotalCAE

Advanced Scale Career Development & Workforce Enhancement Center

Featured Advanced Scale Jobs:

SUBSCRIBE for monthly job listings and articles on HPC careers.

HPCwire Resource Library

HPCwire Product Showcase

Subscribe to the Monthly
Technology Product Showcase:

HPCwire