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At ISC, Sustainable Computing Leaders Discuss HPC’s Energy Crossroads
In the wake of SC22 last year, HPCwire wrote that “the conference’s eyes had shifted to carbon emissions and energy intensity” rather than the historical emphasis on flops-per-watt and powe …
The Grand Challenge of Simulating Nuclear Fusion: An Overview with UKAEA’s Rob Akers
As HPC and AI continue to rapidly advance, the alluring vision of nuclear fusion and its endless zero-carbon, low-radioactivity energy is the sparkle in many a futurist’s eye.
At an ISC focu …
Top500: Frontier Gains 92 Petaflops; Henri Gets a Little Greener
May 22, 2023
It’s not quite homeostasis, but it's close. There was little movement in the latest Top500, released today from the International Supercomputing Conference (I Read more…
Nvidia, HPE Announce Superchip-Powered ‘Isambard 3’ Supercomputer
May 21, 2023
Nvidia is ramping up deployment of its Superchips – amalgamated chips that include either two CPUs (the Grace CPU Superchip) or a CPU and a GPU (the Grace Hop Read more…
NERSC and the HPC Community Bid Farewell to Cori Supercomputer
May 17, 2023
After nearly seven years of service, thousands of user projects, and tens of billions of compute hours, the Cori supercomputer at the National Energy Research S Read more…
HPC Career Notes: May 2023 Edition
May 1, 2023
In this monthly feature, we’ll keep you up-to-date on the latest career developments for individuals in the high-performance computing community. Whether it� Read more…
University of Michigan’s ‘Zeus’ Framework Downsizes AI’s Massive Carbon Footprint
April 20, 2023
Famously, a team of researchers from the University of Massachusetts, Amherst, concluded in 2019 that training a single large AI model could emit five times the Read more…
SandboxAQ Launches End-to-End Cryptography Security Suite
April 19, 2023
Worry about data security and the prospect of RSA decryption by future quantum computers has prompted a surge of efforts to advance cryptography tools in recent 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…
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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Sponsored by ANSYS
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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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Sponsored by TotalCAE
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Whitepaper: Streamlining AI Data Management
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Whitepaper: Reducing Bias and Increasing Accuracy in AI Models
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Whitepaper: A New Standard in CAE Solutions for Manufacturing
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Whitepaper: Porting CUDA Applications to Run on AMD GPUs
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Whitepaper: QCT HPC BeeGFS Storage: A Performance Environment for I/O Intensive Workloads
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Webcasts and webinars: Leveraging Exascale Era Technology for Advanced Computer-Aided Engineering
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