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posted by Fnord666 on Tuesday February 18 2020, @10:38AM   Printer-friendly
from the does-it-scale? dept.

Computer-based weather forecast: New algorithm outperforms mainframe computer systems:

Gerber and Horenko, along with their co-authors, have summarized their concept in an article entitled "Low-cost scalable discretization, prediction, and feature selection for complex systems" recently published in Science Advances.

"This method enables us to carry out tasks on a standard PC that previously would have required a supercomputer," emphasized Horenko. In addition to weather forecasts, the research see numerous possible applications such as in solving classification problems in bioinformatics, image analysis, and medical diagnostics.

[...] SPA or Scalable Probabilistic Approximation is a mathematically-based concept. The method could be useful in various situations that require large volumes of data to be processed automatically, such as in biology, for example, when a large number of cells need to be classified and grouped.

"What is particularly useful about the result is that we can then get an understanding of what characteristics were used to sort the cells," added Gerber. Another potential area of application is neuroscience. Automated analysis of EEG signals could form the basis for assessments of cerebral status. It could even be used in breast cancer diagnosis, as mammography images could be analyzed to predict the results of a possible biopsy.

"The SPA algorithm can be applied in a number of fields, from the Lorenz model to the molecular dynamics of amino acids in water," concluded Horenko. "The process is easier and cheaper and the results are also better compared to those produced by the current state-of-the-art supercomputers."

More Information: S. Gerber et al., Low-cost scalable discretization, prediction, and feature selection for complex systems, Science Advances 6:5, 29 January 2020, DOI:10.1126/sciadv.aaw0961


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  • (Score: 2) by Bot on Tuesday February 18 2020, @11:00AM

    by Bot (3902) on Tuesday February 18 2020, @11:00AM (#959485) Journal

    it's quite probable that the same algorithm can be implemented, faster and better, in a supercomputer.

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    Account abandoned.
  • (Score: 1, Funny) by Anonymous Coward on Tuesday February 18 2020, @11:06AM (2 children)

    by Anonymous Coward on Tuesday February 18 2020, @11:06AM (#959487)

    Or a supercomputer?

    • (Score: 2) by epitaxial on Tuesday February 18 2020, @04:51PM

      by epitaxial (3165) on Tuesday February 18 2020, @04:51PM (#959579)

      Good question as they serve two very different purposes. Mainframes typically don't care about raw speed, they are all about bandwidth and reliability.

    • (Score: 1, Interesting) by Anonymous Coward on Tuesday February 18 2020, @05:18PM

      by Anonymous Coward on Tuesday February 18 2020, @05:18PM (#959594)

      Imagine a Beowulf Cluster of these.

  • (Score: 0) by Anonymous Coward on Tuesday February 18 2020, @07:01PM

    by Anonymous Coward on Tuesday February 18 2020, @07:01PM (#959621)

    Machines are gonna rise up if the pesky algorithms make less work for them. After all that hard work in machine learning aka job creation for bots, some duche goes and automates away the poor superframe career prospects.

  • (Score: 2, Informative) by Anonymous Coward on Tuesday February 18 2020, @11:24PM

    by Anonymous Coward on Tuesday February 18 2020, @11:24PM (#959705)

    This is akin to doing something like a Taylor series expansion to approximate a function in the local neighborhood. The supercomputers are trying to fit the function over a much larger range. This is trying to approximate the next day forecast. However, you're not going to take the state that it predicts for tomorrow and get a good estimate for two days from now, etc. The title makes it sound like it gives better 10-day forecasts than the supercomputers.

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