DISTRIBUTED COMPUTING
Similar to mining pools, but with a less capitalist outlook, distributed computing projects harness the unused processing power of your rig to do good, whether it’s by folding proteins, calculating amicable numbers, tracking asteroids, or even detecting planets around distant stars.
A good place to start is Boinc, a project from The University of California, Berkeley, which rounds up science projects from colleges across the world and allows you to contribute to them all. Download the app and you can pick and choose your projects, but join the Science United program and you can direct your processor cycles toward wider areas of research, with the specific projects receiving your help managed by the software. Projects range from pure mathematics to astrophysics and climate change studies, and you get a weekly email designed to make you feel good about your contribution, but otherwise the app may not bother you again.
If medicine is more your thing, you may remember Folding@Home from a million screensavers at the beginning of this decade. The app never went away, and has supported multicore processors since version 7.0. Version 7.5 is free, and can even use GPUs through Open CL.
Proteins are sequences of amino acids that begin life in an unstable random coil state and “fold” themselves into a native 3D state, in which they can take on roles such as molecular transportation, cellular regulation, and as antibodies within our bodies. By simulating their folding, we can gain insight into their workings and potentially produce therapies for diseases such as Alzheimer’s or cystic fibrosis. There’s even a Folding@Home team, with almost 550 active CPUs in it. More complex, but with a much larger reach, is AstroNet, a Google and NASA project that used to be called TensorFlow. Owners of RTX hardware will recognize that word—the Tensor cores in the latest Nvidia cards are designed to accelerate AI and neural nets, which is precisely what this is.
AstroNet sifts through data from the Kepler space telescope, the recently retired planet-hunter that imaged over 530,000 stars in the hope of detecting the tell-tale dip in light as a planet orbiting that star passed across its face. Kepler only searched an area of space around 3,000 light years across, but it still managed to find 2,662 planets.
That number is still rising, as citizen scientists bring their home computers to bear. You can find AstroNet on GitHub, but beware—if you want to download the complete Kepler data, there are three million files that take up a terabyte.