Today is a good day for trapped ion quantum computer developer Quantinuum. The young company launched its newest system – System Model H2 – with 32 qubits capable of all-to-all connectivity. …
Like many in the quantum computing world, particularly quantum algorithm/software developers, QC Ware is focusing near-term on classical and hybrid classical-quantum offerings. Today, QC Ware int …
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 6, 2023
Financial firm Bloomberg is trying to prove that there are smarter ways to fine-tune artificial intelligence applications without the ethical or security concer 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…
February 20, 2023
Microsoft and Google are driving a major computing shift by bringing AI to people via search engines, and one measure of success may come down to the hardware a Read more…
January 26, 2023
The development of a national flagship supercomputer aimed at exascale computing continues to be a heated competition, especially in the United States, the Euro Read more…
January 11, 2023
Intel is bringing subscription and rental services to semiconductors as it explores new business models, but it remains to be seen if buyers warm up to the idea of paying extra to unlock features on a chip. Intel is bringing an "on-demand" feature to its new Xeon CPUs codenamed Sapphire Rapids, which the company launched on Tuesday after long delays. The on-demand feature involves paying a fee to activate... Read more…
January 10, 2023
After a number of delays, Intel has launched its fourth-generation Intel Xeon Scalable processor, codenamed Sapphire Rapids, the successor to Ice Lake. Manufact Read more…
December 14, 2022
Texas Advanced Computing Center Director Dan Stanzione and HPCwire Managing Editor Tiffany Trader met in Dallas to discuss the biggest trends in HPC and the hot 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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