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Nvidia Launches Spectrum-X Networking Platform for Generative AI
Nvidia launched a new Ethernet-based networking platform – the Nvidia Spectrum-X – that targets generative AI workloads. Based on tight coupling of the Nvidia Spectrum-4 Ethernet switch with …
Nvidia to Offer a ‘1 Exaflops’ AI Supercomputer with 256 Grace Hopper Superchips
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. Announced tonight (mid-day Monday …
The Grand Challenge of Simulating Nuclear Fusion: An Overview with UKAEA’s Rob Akers
May 25, 2023
As HPC and AI continue to rapidly advance, the alluring vision of nuclear fusion and its endless zero-carbon, low-radioactivity energy is the sparkle in many a Read more…
ISC Keynote: To Reinvent HPC After Moore’s Law, Follow the Money
May 23, 2023
This year’s International Supercomputing Conference (ISC) kicked off yesterday in Hamburg, Germany, with a keynote from Dan Reed, presidential professor at th Read more…
Intel Admits GPU Mistakes, Reveals New Supercomputing Chip Roadmap
May 22, 2023
Intel has finally provided specific details on wholesale changes it has made to its supercomputing chip roadmap after an abrupt reversal of an ambitious plan to Read more…
Meta Completes Research SuperCluster, Announces Next-Gen Datacenter
May 18, 2023
2023 finds every major tech player working furiously to build out tools and infrastructure amid the massive surge in large-language models (LLMs). Long-time AI Read more…
Google’s New AI-Focused ‘A3’ Supercomputer Has 26,000 GPUs
May 10, 2023
Cloud providers are building armies of GPUs to provide more AI firepower. Google is joining the gang with a new supercomputer that has almost 2.5 times the number of GPUs than the world’s third-fastest supercomputer called LUMI. Google announced an AI supercomputer with 26,000 GPUs at its developer conference on Wednesday. Read more…
US Takes a Page from Supercomputing Past to Boost AI Research
May 10, 2023
A pattern is emerging on how U.S. government wants to boost its AI research. The approach is similar to how the early supercomputing infrastructures were built: Read more…
Intel’s Habana Labs Takes on Prominent Role as Generative AI Surges
May 9, 2023
Intel acquired AI chipmaker Habana Labs just four years ago; now, the division is serving – per Habana COO Eitan Medina – as “effectively the center of ex Read more…
Esperanto Works to Fill the Software Gap for RISC-V AI Servers
April 22, 2023
Esperanto Technologies is working to accelerate software support for its novel, high-performance AI chips. The company has ported a large-language model (LLM) f Read more…
ISC 2023 Booth Videos
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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.
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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.
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