May 30, 2023
With ISC23 now in the rearview mirror, let’s get back to the results from the ASC23 Student Cluster Competition. In our last articles, we looked at the compet Read more…
May 19, 2023
The selection of Jack Dongarra as the recipient of the 2021 Turing Award was a well-deserved recognition of his invaluable contributions to the field of high pe Read more…
March 9, 2023
HPL. HPCG. Bookends. One will show you the best possible performance from your cluster while the other will show you the worst. Running and optimizing these t Read more…
December 2, 2022
The Frontier supercomputer – still fresh off its chart-topping 1.1 Linpack exaflops run and maintaining its number-one spot on the Top500 list – was still v Read more…
November 16, 2022
For a few moments, the atmosphere was more Rock Concert than Supercomputing Conference with many members of a packed audience standing, cheering, and waving signs as Jack Dongarra took the stage to deliver the annual ACM Turing Award lecture at SC22. Read more…
July 15, 2022
The direction that exascale supercomputing will need to follow and the continuing value of visual and other non-computational experts in computer visualizations were the focus of the final two plenary sessions at the PEARC22 conference in Boston on July 13. Jack Dongarra, director of research staff and professor at the Oak Ridge National Laboratory and the University of Tennessee, Knoxville... Read more…
November 15, 2021
No exascale for you* -- at least, not within the High-Performance Linpack (HPL) territory of the latest Top500 list, issued today from the 33rd annual Supercomputing Conference (SC21), held in-person in St. Louis, Mo., and virtually, from Nov. 14–19. "We were hoping to have the first exascale system on this list but that didn’t happen," said Top500 co-author... Read more…
April 6, 2021
The wait is over. Today Intel officially launched its 10nm datacenter CPU, the third-generation Intel Xeon Scalable processor, codenamed Ice Lake. With up to 40 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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