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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 …
Exascale Frontier Supercomputer Has Passed Formal Acceptance: What That Means
Fresh off their third Top500 win for Frontier – now with an 8.4% higher Linpack score – the HPC team at Oak Ridge National Laboratory had some exciting news to share today. Frontier has passe …
NCSA’s Bill Gropp Digs into MPI – Past, Present, and Future
May 18, 2023
If you work in scientific computing, MPI (message passing interface) is likely a part of your life. It may be hidden underneath the applications you run or you Read more…
LANL Report – Looming Fortran Talent Scarcity is Threatening
May 1, 2023
A new report from Los Alamos National Lab sounds alarms over the declining number of Fortran programmers, the shrinking number of efforts to teach Fortran, and 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…
What’s Stirring in Nvidia’s R&D Lab? Chief Scientist Bill Dally Provides a Peek
March 28, 2023
In what’s become a regular GPU Technology Conference feature, Bill Dally, Nvidia chief scientist and SVP of research, provides a glimpse into how Nvidia organ Read more…
Cost-effective Fork of GPT-3 Released to Scientists
March 28, 2023
Researchers looking to create a foundation for a ChatGPT-style application now have an affordable way to do so. Cerebras is releasing open-source learning models for researchers with the ingredients necessary to cook up their own ChatGPT-AI applications. The open-source tools include seven models that form a learning... Read more…
Is Fortran the Best Programming Language? Asking ChatGPT
March 23, 2023
I recently wrote about my experience with interviewing ChatGPT here. As promised, in this follow-on and conclusion of my interview, I focus on Fortran and other languages. All in good fun. I hope you enjoy the conclusion of my interview. After my programming language questions, I conclude with a few notes... Read more…
State of SYCL – ECP BOF Showcases Progress and Performance
February 28, 2023
Enabling interoperability across U.S. exascale supercomputers is one of the chief goals for the U.S. Exascale Computing Project (ECP), which has broadly oversee Read more…
Peeling Back the Covers on Accenture’s Quantum Practice Aspirations
February 15, 2023
Bringing early visions of quantum technology into practical commercial reality will require many participants. How important will the big consulting firms be? A 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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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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