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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…

Oracle Providing a Ground to Fuel Nvidia’s Subscription Revenue

October 18, 2022

Oracle is bringing Nvidia's AI Enterprise software suite alongside thousands of its latest GPUs to its cloud infrastructure, which could fuel the chipmaker’s Read more…

SC21’s Student Cluster Competition Winners Announced

November 19, 2021

SC21 may have been the first major supercomputing conference to return to in-person activities, but not everything returned to the live menu: the Student Cluster Competition – held virtually at ISC 2020, SC20 and ISC 2021 – was again held virtually at SC21. Nevertheless, [email protected] Chair Jay Lofstead took the physical stage at SC21 on Thursday to announce the... Read more…

Using HPC Cloud, Researchers Investigate the COVID-19 Lab Leak Hypothesis

May 27, 2021

At the end of 2019, strange pneumonia cases started cropping up in Wuhan, China. As Wuhan (then China, then the world) scrambled to contain what would, of cours Read more…

OCI Jumps into Arm with Instances and Aggressive Developer Program

May 25, 2021

Oracle Cloud Infrastructure (OCI) today launched a multi-prong Arm initiative including instances (VM and bare metal) based on Ampere’s Altra microprocessor, Read more…

Arm Details Neoverse V1, N2 Platforms with New Mesh Interconnect, Advances Partner Ecosystem

April 27, 2021

Chip designer Arm Holdings is sharing details about its Neoverse V1 and N2 cores, introducing its new CMN-700 interconnect, and showcasing its partners' plans t Read more…

Intel Launches 10nm ‘Ice Lake’ Datacenter CPU with Up to 40 Cores

April 6, 2021

The wait is over. Today Intel officially launched its 10nm datacenter CPU, the third-generation Intel Xeon Scalable processor, codenamed Ice Lake. With up to 40 Read more…

Safety Pharmacology: How HPC Cloud Is Accelerating Drug Cardiotoxicity Screening

March 30, 2021

The extraordinarily rapid drug development necessitated by the COVID-19 pandemic may have pulled AI- and HPC-accelerated pharmacology into the spotlight, but si Read more…

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Whitepaper

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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Whitepaper

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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