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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 …
MLPerf Inference 3.0 Highlights – Nvidia, Intel, Qualcomm and…ChatGPT
MLCommons today released the latest MLPerf Inferencing (v3.0) results for the datacenter and edge. While Nvidia continues to dominate the results – topping all performance categories – other …
Tasty CHIPS – New MEC Program to Expand US Prototyping Capabilities Gains Steam
March 16, 2023
Sometime later this year, perhaps around July, the Department of Defense is expected to announce the sites and focus of up to nine hubs associated with the Micr Read more…
AWS, Partners Report Successful Quantum Key Distribution Trial in Singapore
March 6, 2023
While quantum computing makes its way haphazardly towards practical realty, quantum key distribution (QKD) is moving quickly towards greater commercial use. Tod Read more…
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…
Survey Results: PsiQuantum, ORNL, and D-Wave Tackle Benchmarking, Networking, and More
September 19, 2022
The are many issues in quantum computing today – among the more pressing are benchmarking, networking and development of hybrid classical-quantum approaches. Read more…
AMD Previews 400 Gig Adaptive SmartNIC SOC at Hot Chips
August 24, 2022
Fresh from finalizing its acquisitions of FPGA provider Xilinx (Feb. 2022) and DPU provider Pensando (May 2022) ), AMD previewed what it calls a 400 Gig Adaptive smartNIC SOC yesterday at Hot Chips. It is another contender in the increasingly crowded and blurry smartNIC/DPU space where distinguishing between the two isn’t always easy. The motivation for these device types... Read more…
A Farewell to XSEDE: A Retrospective & Introduction to the ACCESS Program
July 22, 2022
John Towns, principal investigator of the Extreme Science and Engineering Discovery Environment (XSEDE), gave an overview of the soon-to-complete NSF-funded cyb 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.
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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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