May 28, 2023
We in HPC sometimes roll our eyes at the term “AI supercomputer,” but a new system from Nvidia might live up to the moniker: the DGX GH200 AI supercomputer. Read more…
May 21, 2023
Nvidia is ramping up deployment of its Superchips – amalgamated chips that include either two CPUs (the Grace CPU Superchip) or a CPU and a GPU (the Grace Hop Read more…
March 13, 2023
After getting bruised in servers by AMD, Intel hopes to stop the bleeding in the server market with next year's chip offerings. The difference-making products will be Sierra Forest and Granite Rapids, which are due out in 2024, said Dave Zinsner, chief financial officer at Intel, last week at the Morgan Stanley Technology, Media and Telecom conference. Read more…
December 16, 2022
The European Union will release €270 million in funds as it tries to attain technology independence by building chips based on the open RISC-V instruction set Read more…
November 30, 2022
AWS has announced three new Amazon Elastic Compute Cloud (Amazon EC2) instances powered by AWS-designed chips, as well as several new Intel-powered instances � Read more…
November 18, 2022
One of the original RISC-V designers this week boldly predicted that the open architecture will surpass rival chip architectures in performance. "The prediction is two or three years we'll be surpassing your architectures and available performance with... Read more…
November 16, 2022
Europe’s sovereign approach to exascale computing is complicating plans for U.S. chipmakers to breakthrough in the market, and in the process, empowering local chipmakers. For one, a European chip startup called SiPearl is emerging as an early... Read more…
November 5, 2022
Arm's been riding high on the mobile market for decades now, but has struggled to make its mark on servers. But the company hopes to reverse that with some new initiatives that Arm executives addressed at the recent Arm DevSummit held last week. The top initiative revolves around providing programming and design tools so its chip... 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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