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Meet TempoQuest, a Startup Bringing Weather Code Into the Accelerated Era
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…
Q&A with Microsoft’s Nidhi Chappell, an HPCwire Person to Watch in 2023
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…
Google and Microsoft Set up AI Hardware Battle with Next-Generation Search
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…
US DOD Ends Cloud Drama with $9 Billion Contract to Top Cloud Providers
December 8, 2022
The U.S. Department of Defense wielded its JEDI powers to procure public cloud services with a diplomatic end to a feud between Amazon and Google to win the multi-billion dollar contract. The DoD broke up a $9 billion contract between the top four cloud providers – Google, Amazon, Microsoft and Oracle – for the Joint Warfighting Cloud Capability initiative, which will bring the defense branches – Air Force, Army... Read more…
Blueprints to Shake up Next-generation Server Designs Emerge at OCP Summit
October 24, 2022
A new generation of designs ready to shake up conventional server architecture emerged at the recent Open Compute Project Summit, where Google, Facebook and Mic Read more…
University of Bath Launches Azure-Based ‘Nimbus’ Cloud Supercomputer
October 13, 2022
The University of Bath has launched a new, cloud-based supercomputer, Nimbus, which the university is calling the “central pillar” of its new portfolio of c Read more…
Microsoft Closes Confidential Computing Loop with AMD’s Milan Chip
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…
Microsoft’s ‘Singularity’ to Enable Global Accelerator Network for AI Training
February 24, 2022
In science fiction and future studies, the word “singularity” is invoked in reference to a rapidly snowballing artificial intelligence that, repeatedly iterating on itself, eclipses all human knowledge and ability. It is this word that Microsoft—perhaps ambitiously—has invoked for its new AI project, a “globally distributed scheduling service for highly efficient and reliable execution of deep learning training and inference workloads.” Read more…
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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.
To ensure your AI systems are optimized, follow these five essential steps to eliminate bottlenecks and maximize efficiency.
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