Enabling interoperability across U.S. exascale supercomputers is one of the chief goals for the U.S. Exascale Computing Project (ECP), which has broadly overseen development of the early software …
Currently, Intel doesn’t have a quantum processor that potential users can access. In the fall, it launched a new quantum software development kit and simulator. Sometime in 2023, Intel plans t …
Find out which 12 HPC luminaries are being recognized this year for driving innovation within their particular fields.
November 23, 2022
Last month, QuEra Computing began providing access to its 256-qubit, neutral atom-based quantum system, Aquila, from Amazon Braket. Founded in 2018, and built o Read more…
November 16, 2022
For a few moments, the atmosphere was more Rock Concert than Supercomputing Conference with many members of a packed audience standing, cheering, and waving signs as Jack Dongarra took the stage to deliver the annual ACM Turing Award lecture at SC22. Read more…
November 10, 2022
MLCommons yesterday issued its latest round of MLPerf benchmarks – for Training, HPC, and Tiny. Releasing three sets of benchmarks at the same time makes pars Read more…
November 3, 2022
D-Wave Systems, a pioneer in quantum annealing-based computing, today announced significant upgrades to its constrained quadratic model (CQM) hybrid solver that Read more…
October 27, 2022
What’s the quantum computing fuss all about? Should you jump into the game or run as fast as you can away from it? A fascinating panel with committed quantum players, IBM and Strangeworks, and traditional computing powerhouses, Dell (systems) and AMD (chips), tackled this topic at the HPC + AI on Wall Street conference held earlier this... Read more…
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…
September 8, 2022
The steady maturation of MLCommons/MLPerf as an AI benchmarking tool was apparent in today’s release of MLPerf v2.1 Inference results. Twenty-one organization Read more…
August 30, 2022
It is perhaps not surprising that the big cloud providers – a poor term really – have jumped into quantum computing. Amazon, Microsoft Azure, Google, and th Read more…
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.
Karlsruhe Institute of Technology (KIT) is an elite public research university located in Karlsruhe, Germany and is engaged in a broad range of disciplines in natural sciences, engineering, economics, humanities, and social sciences. For institutions like KIT, HPC has become indispensable to cutting-edge research in these areas.
KIT’s HoreKa supercomputer supports hundreds of research initiatives including a project aimed at predicting when the Earth’s ozone layer will be fully healed. With HoreKa, projects like these can process larger amounts of data enabling researchers to deepen their understanding of highly complex natural processes.
Read this case study to learn how KIT implemented their supercomputer powered by Lenovo ThinkSystem servers, featuring Lenovo Neptune™ liquid cooling technology, to attain higher performance while reducing power consumption.
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