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Google AI Supercomputer Shows the Potential of Optical Interconnects
There are limits on the speed of how fast copper wires can move data between computers, and a transition to light speed will ultimately drive AI and high-performance computing forward. Every majo …
AWS, Partners Report Successful Quantum Key Distribution Trial in Singapore
While quantum computing makes its way haphazardly towards practical realty, quantum key distribution (QKD) is moving quickly towards greater commercial use. Today, AWS reported running a successf …
Ayar Labs to Demo ‘Commercial-Grade’ 4Tbps Optical I/O Solution
March 1, 2023
Ayar Labs, founded in 2015, is pursuing optics as a means of driving higher interconnect speeds and efficiencies in computing. Now, the company is announcing th Read more…
CEO Jack Hidary on SandboxAQ’s Ambitions and Near-term Milestones
October 18, 2022
Spun out from Google last March, SandboxAQ is a fascinating, well-funded start-up targeting the intersection of AI and quantum technology. “As the world enter Read more…
ESnet6 Launches, Heralding a New Era in Scientific Networking
October 11, 2022
The launch of ESnet6 was announced at an event at Berkeley Lab this morning. ESnet – short for “energy sciences network” – is managed by Berkeley Lab, f Read more…
What’s New in HPC Research: ROBE, OpenMP Automated Scheduling, SCALSALE, & More
October 6, 2022
In this regular feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programmin Read more…
Stray Qubits: SandboxAQ buys Cryptosense, Aliro Hires CMO, UWisconsin Advances Error Correction
September 14, 2022
Well-heeled Google spinout SandboxAQ announced its first acquisition today – snapping up cybersecurity software specialist Cryptosense – while quantum netwo Read more…
Does Quantum Entanglement Hold the Key to Unhackable Communications?
July 28, 2022
Scientists have uncovered a new type of quantum cryptography that utilizes one of the same laws of physics used in building quantum computers: quantum entanglement. Quantum entanglement, or what Einstein called “spooky action at a distance,” refers to the phenomenon of two subatomic particles being linked to one another in an exclusive... Read more…
What’s New in HPC Research: EXA2PRO, DQRA, and HiCMA-PaRSE Frameworks & More
June 28, 2022
In this regular feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programmin Read more…
Durham University to Test Rockport Networks on COSMA7 Supercomputer
May 24, 2022
Durham University’s Institute for Computational Cosmology (ICC) is home to the COSMA series of supercomputers (short for “cosmological machine”). COSMA— 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.
Two recent studies show how Ansys Gateway powered by AWS can balance run times and costs, making it a compelling solution for automotive development.
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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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