close
    • chevron_right

      Is distributed computing dying, or just fading into the backdrop?

      news.movim.eu / ArsTechnica · Tuesday, 11 July, 2023 - 13:44 · 1 minute

    Image of a series of bar graphs in multiple colors.

    Enlarge / This image has a warm, nostalgic feel for many of us. (credit: SETI Institute )

    Distributed computing erupted onto the scene in 1999 with the release of SETI@home, a nifty program and screensaver (back when people still used those) that sifted through radio telescope signals for signs of alien life.

    The concept of distributed computing is simple enough: You take a very large project, slice it up into pieces, and send out individual pieces to PCs for processing. There is no inter-PC connection or communication; it’s all done through a central server. Each piece of the project is independent of the others; a distributed computing project wouldn't work if a process needed the results of a prior process to continue. SETI@home was a prime candidate for distributed computing: Each individual work unit was a unique moment in time and space as seen by a radio telescope.

    Twenty-one years later, SETI@home shut down, having found nothing. An incalculable amount of PC cycles and electricity wasted for nothing. We have no way of knowing all the reasons people quit (feel free to tell us in the comments section), but having nothing to show for it is a pretty good reason.

    Read 15 remaining paragraphs | Comments

    • chevron_right

      DeepMind AI handles protein folding, which humbled previous software

      John Timmer · news.movim.eu / ArsTechnica · Monday, 30 November, 2020 - 22:10 · 1 minute

    Proteins rapidly form complicated structures which had proven difficult to predict.

    Enlarge / Proteins rapidly form complicated structures which had proven difficult to predict. (credit: Argonne National Lab )

    Today, DeepMind announced that it had seemingly solved one of biology's outstanding problems: how the string of amino acids in a protein folds up into a three-dimensional shape that enables their complex functions. It's a computational challenge that has resisted the efforts of many very smart biologists for decades, despite the application of supercomputer-level hardware for these calculations. DeepMind instead trained its system using 128 specialized processors for a couple of weeks; it now returns potential structures within a couple of days.

    The limitations of the system aren't yet clear—DeepMind says it's currently planning on a peer-reviewed paper, and has only made a blog post and some press releases available. But it clearly performs better than anything that's come before it, after having more than doubled the performance of the best system in just four years. Even if it's not useful in every circumstance, the advance likely means that the structure of many proteins can now be predicted from nothing more than the DNA sequence of the gene that encodes them, which would mark a major change for biology.

    Between the folds

    To make proteins, our cells (and those of every other organism) chemically link amino acids to form a chain. This works because every amino acid shares a backbone that can be chemically connected to form a polymer. But each of the 20 amino acids used by life has a distinct set of atoms attached to that backbone. These can be charged or neutral, acidic or basic, etc., and these properties determine how each amino acid interacts with its neighbors and the environment.

    Read 13 remaining paragraphs | Comments

    index?i=xt_2L9as_aI:uGE4QhrVmrc:V_sGLiPBpWUindex?i=xt_2L9as_aI:uGE4QhrVmrc:F7zBnMyn0Loindex?d=qj6IDK7rITsindex?d=yIl2AUoC8zA