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…
April 5, 2023
MLCommons today released the latest MLPerf Inferencing (v3.0) results for the datacenter and edge. While Nvidia continues to dominate the results – topping al Read more…
October 13, 2022
The University of Bath has launched a new, cloud-based supercomputer, Nimbus, which the university is calling the “central pillar” of its new portfolio of c Read more…
September 16, 2022
Full-stack quantum computing startup Rigetti announced a number of new partnerships and strategic updates at its inaugural investor day meeting, held in-person Read more…
April 5, 2022
There was a time when “the cloud” ran on pretty vanilla x86 architecture, save for boutique firms like Nimbix (acquired by Atos last year) that pioneered the use of then-exotic hardware like GPUs and FPGAs and other Intel alternatives. If further evidence was needed of the... Read more…
January 27, 2022
Nearly two years ago, the UK’s Meteorological Office (Met Office) announced a stunning £1.2 billion plan to deliver the world’s most powerful supercomputer Read more…
November 19, 2021
SC21 may have been the first major supercomputing conference to return to in-person activities, but not everything returned to the live menu: the Student Cluster Competition – held virtually at ISC 2020, SC20 and ISC 2021 – was again held virtually at SC21. Nevertheless, [email protected] Chair Jay Lofstead took the physical stage at SC21 on Thursday to announce the... Read more…
October 6, 2021
The University of Bath is upgrading its HPC infrastructure, which it says “supports a growing and wide range of research activities across the University.” 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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