May 12, 2023
Travis Humble is the director the Quantum Science Center (QSC) at Oak Ridge National Laboratory. QSC is one of six National QIS Research established by the U.S. National Quantum Initiative Act (NQIA) in 2018 and being overseen by the Department of Energy. Hopes are high that these centers, through their own research and in collaboration... Read more…
April 29, 2023
HPCwire 2023 Person to Watch Trish Damkroger is a long-time HPC enthusiast and seasoned exec hailing from a 17 year tenure at Lawrence Livermore Lab, followed by five years of leading HPC strategy at Intel, before one year ago making the move to HPE (where she is Chief Product Officer and Senior Vice President, HPC, AI & Labs). Read more…
April 15, 2023
HPCwire presents our interview with Nidhi Chappell, General Manager of Azure HPC, AI, SAP, and Confidential Computing at Microsoft. As an HPCwire 2023 Person to Watch, Chappell shares her insights on the evolving HPC cloud market and key trends, including sustainability. She also discusses her role and responsibilities at Azure, and... Read more…
March 31, 2023
SC23 General Chair Dorian C. Arnold is enthusiastic about this year's conference, which will take place Nov. 12-17 in Denver, Colo. Our exclusive interview with Arnold covers his history with the annual event, what's in store for attendees, and his insights into the HPC landscape writ large. In addition to his work with SC, Arnold is also... Read more…
September 9, 2022
HPCwire presents our interview with Eric Monchalin, chair of European Processor Initiative & VP, head of machine intelligence, Atos, and an HPCwire 2022 Person to Watch. Monchalin recaps the EPI program strategy, explains the motivations for tech soveriengty and gives a view to current and future computing... Read more…
August 12, 2022
HPCwire presents our interview with Bronson Messer, distinguished scientist and director of Science at the Oak Ridge Leadership Computing Facility (OLCF), ORNL, and an HPCwire 2022 Person to Watch. Messer recaps ORNL's journey to exascale and sheds light on how all the pieces line up to support the all-important science. Also covered are the role... Read more…
June 17, 2022
HPCwire presents our interview with Jeff McVeigh, vice president and general manager, Super Compute Group, Intel Corporation, and an HPCwire 2022 Person to Watc Read more…
June 9, 2022
HPCwire presents our interview with Jay Gambetta, IBM Fellow and VP, Quantum, and an HPCwire 2022 Person to Watch. Few companies have tackled as many parts of t 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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