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AI continues to play a key role in scientific research – not only in the management of new discoveries, but also in the way we understand the tools for these discoveries. High -performance computer technology has been the core of the main scientific breakthroughs for years. However, as this system increases size and complexity, it is becoming increasingly difficult to understand.
The restrictions are clear. Scientists can see their simulations, but often cannot explain why the work slowed or failed without warning. Machines generate system data, but most of them are hidden behind IT teams, not researchers. There is no easy way to explore what happened. Although the data is available, work with them requires coding, engineering skills and knowledge of machine learning that many scientists do not have. The tools are slow, static and dynamically adaptable.
Scientists from Sandia National Laboratories are trying to change it. They created a system called EPIC (explained infrastructure and calculation platform), which serves as a platform controlled and intended to increase operational data analysis. It uses new emerging capabilities of basic Genai models in the context of HPC surgical analysis.
Scientists can use Epic to find out what is happening inside the supercomputer using a simple language. Intoread digging using logs or writing complex commands can ask users to ask simple questions and get clear answers to how jobs were or what slowed down simulation.
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“The aim of the EPIC is to increase different data drive tasks, such as descriptive analytics and predictive analysts by automating the process of thinking and interaction with high -dimensional multimodal HPC surgical data and synthesizing results into meaningful insights.”
People focus more on epic effects than just another data tool. They wanted something to help scientists ask questions and understand answers. They tried to create an experience that felt more natural. Something closer to conversation back and kerft before the command line prompt is. Scientists can focus on their questioning without skipping between or digging the logo.
What is experiencing forces is background work. It is based on many sources such as protocol files, telemetry and documentation. It connects them in a way that makes sense. Scientists can adhere to the behavior of the system, identify where there is slowing and spot formulas, all without having to coded or call for support. Epic helps to cause complicated infrastructure to feel more understandable and less stunning.
To make it possible, the EPIC team developed modular architecture that combines general languages with smaller models trained specifically for HPC tasks. This setting allows the system to process different types of data and generate output ranges, from simple charts to graphs, predictions or SQL questions.
By fine -tuning open models that rely on massive commercial systems, they were able to maintain high performance while lowering costs. The aim was to give scientists an instrument that adapts to the way they think and work, not the one that forces them to read another interface.
During testing, the system worked well across a number of tasks. Its removal engine could be straight questions ACCAIRY direct questions about the right models and achieve a score of F1 0.77. Smaller models such as LLAM 3 8B variants have been processing complex tasks such as SQL generation and system predictions, more efficient than larger proprietary models.
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EPIC prognosis also reliable. He created accurate estimates for temperature, energy and energy that uses different support workload. Most importantly, the platform brought these results with a fraction of the cost, and the calculated overhead costs were usually expected from this setting. For scientists working on complex support systems, this type of efficacy can significantly change.
“There is an unmistakable gap between data and insight, which is mainly in the complexity of handling a great love for data from different sources and at the same time fulfills multilateral cases of use focused on many different audiences,” the scientists pointed out.
The closure of this last mile between RAW System and Real Insight remains one of the biggest obstacles in high -performance computer technology. Epic offers a view of what is possible when AI is woven directly into the analytical process, not just a supplement. It can help to transform, as scientists interact with tools that drive their work. As the models improve and the systems are further shrinking, platforms like EPIC could help ensure that understanding maintains step with innovations.
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