April 18, 2023
Weather and climate applications are some of the most important for high-performance computing, often serving as raisons d'être and flagship workloads for the Read more…
March 21, 2023
If you are a die-hard Nvidia loyalist, be ready to pay a fortune to use its AI factories in the cloud. Renting the GPU company's DGX Cloud, which is an all-inclusive AI supercomputer in the cloud, starts at $36,999 per instance for a month. The rental includes access to a cloud computer with eight Nvidia H100 or A100 GPUs and 640GB... Read more…
November 10, 2022
AMD’s fourth-generation Epyc processor line has arrived, starting with the “general-purpose” architecture, called “Genoa,” the successor to third-gen Eypc Milan, which debuted in March of last year. At a launch event held today in San Francisco, AMD announced the general availability of the latest Epyc CPUs with up to 96 TSMC 5nm Zen 4 cores... Read more…
September 22, 2022
Microsoft shared details on how it uses an AMD technology to secure artificial intelligence as it builds out a secure AI infrastructure in its Azure cloud service. Microsoft has a strong relationship with Nvidia, but is also working with AMD's Epyc chips (including the new 3D VCache series), MI Instinct accelerators, and also... 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…
March 10, 2022
Add Amazon Web Services to the growing list of companies (tech and otherwise) that are curtailing business with Russia in opposition to President Putin’s invasion of Ukraine. As reported in the New York Times and then by Amazon itself, Amazon Web Services is blocking new sign-ups from Russia and Belarus. Existing customers are not impacted. “We’ve suspended shipment of retail... Read more…
June 29, 2021
Matthias Troyer, who leads Microsoft’s quantum computing research, is on a mission, actually two missions. One is to develop practical applications for quantum computing. The other, also important, is to convince the HPC community that efforts to develop quantum computing – so frequently overhyped and off-putting... 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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