DARPA Looks to Propel Parallelism

September 4, 2019

As Moore’s law runs out of steam, new programming approaches are being pursued with the goal of greater hardware performance with less coding. The Defense Advanced Projects Research Agency is launching a new programming effort aimed at leveraging the benefits of massive distributed parallelism with less sweat. Read more…

‘Next Generation’ Universe Simulation Is Most Advanced Yet

February 5, 2018

The research group that gave us the most detailed time-lapse simulation of the universe’s evolution in 2014, spanning 13.8 billion years of cosmic evolution, is back in the spotlight with an even more advanced cosmological model that is providing new insights into how black holes influence the distribution of dark matter, how heavy elements are produced and distributed, and where magnetic fields originate. Read more…

Researchers Recreate ‘El Reno’ Tornado on Blue Waters Supercomputer

March 16, 2017

The United States experiences more tornadoes than any other country. About 1,200 tornadoes touch down each each year in the U.S. with most occurring during torn Read more…

Simulating Combustion at Exascale: a Q&A with ISC Keynoter Jacqueline Chen

March 14, 2016

At the 2016 ISC High Performance conference this June, distinguished Sandia computational combustion scientist Jacqueline H. Chen will deliver a keynote highlighting the latest advances in combustion modeling and simulation. In this interesting and informative Q&A, Chen describes the challenges and opportunities involved in preparing combustion codes for exascale machines. Read more…

U of Michigan Project Combines Modeling and Machine Learning

September 10, 2015

Although we've yet to settle on a term for it, the convergence of HPC and a new generation of big data technologies is set to transform science. The compute-pl Read more…

Argonne Team Tackles Uncertainties in Engine Simulation

August 27, 2015

As we head deeper into the digital age, computers appropriate an ever greater share of the work of designing and testing physical systems, spanning the gamu Read more…

Digital Prototyping a Mercedes

July 14, 2015

ISC 2015’s emphasis on HPC use in industry was reflected in the choice of Monday’s opening keynote speaker, Jürgen Kohler, senior manager, NVH (noise, vibr Read more…

Computer Model Addresses Fate of Missing Malaysian Airlines Flight

June 10, 2015

Forensic reconstruction from an interdisciplinary research team offers new insight into the tragic disappearance of Malaysia Airlines Flight MH370 on March 8, 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

See More in the Resource Library

HPCwire Product Showcase

Subscribe to the Monthly
Technology Product Showcase:

© 2023 HPCwire. All Rights Reserved. A Tabor Communications Publication

HPCwire is a registered trademark of Tabor Communications, Inc. Use of this site is governed by our Terms of Use and Privacy Policy.

Reproduction in whole or in part in any form or medium without express written permission of Tabor Communications, Inc. is prohibited.

HPCwire