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Google’s New AI-Focused ‘A3’ Supercomputer Has 26,000 GPUs
Cloud providers are building armies of GPUs to provide more AI firepower. Google is joining the gang with a new supercomputer that has almost 2.5 times the number of GPUs than the world’s third …
IBM Opens Access to Latest Osprey Quantum Processor at 413 Qubits
Yesterday, IBM announced its newest quantum processor – Osprey – introduced last December is now accessible as an “as an exploratory technical demonstration” on IBM’s cloud. Jay Gambett …
Intel Puts a Happy Face on Its Worst Quarterly Loss Ever
April 27, 2023
Intel posted its worst quarterly loss in history on Thursday, but the chipmaker took a bold move to put a positive spin on the grim news. “We delivered solid first-quarter results, representing steady progress with our transformation,” said Pat Gelsinger, Intel's CEO, in a press release. Read more…
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…
QC Ware Launches Promethium, a Quantum Chemistry SaaS; Targets Hybrid World Near-term
April 17, 2023
Like many in the quantum computing world, particularly quantum algorithm/software developers, QC Ware is focusing near-term on classical and hybrid classical-qu 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…
2023 Winter Classic AWS GROMACS Results!
April 12, 2023
Hands off keyboards, the last computational challenge in the 2023 Winter Classic student cluster competition has been completed, the scores have been compiled, Read more…
Google AI Supercomputer Shows the Potential of Optical Interconnects
April 10, 2023
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 major chipmaker is in agreement that optical interconnects will be needed to reach zettascale computing in an energy-efficient way. That opinion was... Read more…
Bloomberg Uses 1.3 Million Hours of GPU Time for Homegrown Large-Language Model
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…
MLPerf Inference 3.0 Highlights – Nvidia, Intel, Qualcomm and…ChatGPT
April 5, 2023
MLCommons today released the latest MLPerf Inferencing (v3.0) results for the datacenter and edge. While Nvidia continues to dominate the results – topping al Read more…
ISC 2023 Booth Videos
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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.
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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